MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks

## Abstract

Abstract We present MEGA-Bench , an evaluation suite that scales multimodal evaluation to over 500 real-world tasks, to address the highly heterogeneous daily use cases of end users.
Our objective is to optimize for a set of high-quality data samples that cover a highly diverse and rich set of multimodal tasks, while enabling cost-effective and accurate model evaluation.
In particular, we collected 505 realistic tasks encompassing over 8,000 samples from 16 expert annotators to extensively cover the multimodal task space. Instead of unifying these problems into standard multi-choice questions (like MMMU, MMBench, and MMT-Bench), we embrace a wide range of output formats like numbers, phrases, code, L a T e X , coordinates, JSON, free-form, etc. To accommodate these formats, we developed over 40 metrics to evaluate these tasks.
Unlike existing benchmarks, MEGA-Bench offers a fine-grained capability report across multiple dimensions (e.g., application, input type, output format, skill), allowing users to interact with and visualize model capabilities in depth. We evaluate a wide variety of frontier vision-language models on MEGA-Bench to understand their capabilities across these dimensions.

## 1 Introduction

Large foundation models (OpenAI, ; ; Anthropic, ; Google, ; Meta, ; Alibaba, ) have dramatically transformed the landscape of artificial intelligence by showcasing exceptional capabilities across various tasks and domains. Originating in the realm of natural language processing, these models have progressively expanded to perceive and interpret multimodal information, including single images, multiple images, and videos. Previously, multimodal models were mainly used for standardized tasks like image captioning (Lin et al., ), video captioning (Wang et al., ), and visual question answering (Antol et al., ; Goyal et al., ; Xiao et al., ). With the recent progress on multimodal alignment, these models have shown great potential to solve any desired task with a well-designed prompt. As a result, people have applied them to assist with many realistic tasks like “web navigation” (Koh et al., ), “game playing” (Valevski et al., ), “travel planning” (Xie et al., ), “visual navigation” (Wang et al., ), “sports analysis” (Xia et al., ), “visual entity recognition” (Hu et al., ), “visual quality assessment” (Ku et al., ), and more. These efforts have significantly increased the utility of multimodal models.

An important challenge is identifying how to accurately gauge the abilities of these vision-language models (VLMs) across a wide range of tasks. Most existing benchmarks are designed to cover only one or a few similar tasks, making them inadequate for evaluating the models’ overall capabilities. The status quo is to evaluate the model on many existing benchmarks to showcase their all-round abilities. For example, Qwen2-VL(^1^11https://github.com/QwenLM/Qwen2-VL) was evaluated on 27 image and video benchmarks in total. Although this massive evaluation effort provides valuable insights into how well these models handle specialized tasks, it also introduces a significant overhead and several challenges:
- Limited Output Diversity: The existing multi-task benchmarks like MMMU (Yue et al., ), MMT-Bench (Ying et al., ) rely heavily on multiple-choice questions to lower the burden of evaluation. This fails to evaluate the generative abilities of these multimodal models.
- Lack of Task Coverage: The existing benchmarks are often sporadic and lack a systematic design to cover the multimodal task space. Certain abilities are not well covered in the current ecosystem. Consequently, even exhaustively testing all the available benchmarks would not be sufficient.
- Expensive Inference Cost: The full evaluation process is expensive in the computation cost, the time elapsed, and API price in $. Since many examples or tasks are highly similar in the capabilities that they assess, overly repetitive evaluation at a large scale causes a waste of resources.
- Unmanageable Setups: Each benchmark has its own complexities when setting up the evaluation. For example, VQA (Goyal et al., ) has four splits including val, dev-test, std-test, and test. It is hard to track the exact setup of different baseline models to ensure a fair comparison.

To address these challenges, we advocate for a unified protocol that scales up multi-modal evaluation to maximize the task coverage and the diversity in model outputs while optimizing the inference cost. As an initial attempt, we propose MEGA-Bench, which is designed to provide a comprehensive and systematic assessment of multimodal foundation models.

To build MEGA-Bench, we first construct a task taxonomy tree that organizes different multimodal tasks based on the application type (Figure 1), with significant effort spent adjusting and refining the taxonomy tree to ensure sufficient coverage and diversity. The task taxonomy tree then serves as the guiding principle to ensure all relevant tasks and skills are covered and appropriately balanced.
To help the annotators create their tasks, we build an annotation GUI to simplify the process of creating the task JSON files and a web tool to visualize the results of the VLM’s responses alongside the ground truth. We also review each task contribution when it is first submitted, after evaluating the models on the new tasks, and periodically throughout the annotation process to ensure that all of the tasks are novel and high-quality.
This collaborative effort resulted in the compilation of 505 realistic tasks, effectively covering (almost) the entire multimodal capability space at a manageable inference cost. To facilitate nuanced and precise evaluation, we also developed 45 highly-customized metrics tailored to these tasks during the annotation process.

Unlike existing benchmarks that often provide a single score, MEGA-Bench offers a fine-grained capability report based on multiple dimensions such as the input type, input format, output format, and required skills. This interactive and visualizable report enables users to identify the models’ performance across several orthogonal dimensions, uncovering strengths and weaknesses that might be obscured in aggregate scores. Such detailed analysis is invaluable for researchers and developers aiming to enhance foundation models and optimize them for specific downstream applications.

Based on the MEGA-Bench, comprehensive studies on existing flagship and efficiency multimodal foundation models (covering most open-source software and proprietary model APIs) have discovered the following findings:
1. Among flagship models, Claude 3.5 Sonnet (1022) and GPT-4o (0513) currently lead in performance across a wide range of multimodal tasks, with less than a 0.1% difference in their overall scores. Detailed breakdown analyses show Claude 3.5 Sonnet excels in planning and math, while GPT-4o leads in information extraction and knowledge-intensive tasks.
2. Among open-sourced models, Qwen2-VL performs the best, with its performance near the top close-sourced flagship models, and outperforms the second best open-source model by $\approx$10%.
3. Among efficiency models, Gemini 1.5 Flash is the strongest model overall, except for the tasks related to handling User Interfaces and Documents.
4. Proprietary models can effectively leverage Chain-of-Thought (CoT) prompting to improve their performance, while open-source models hardly produce helpful reasoning processes. In our evaluation results, 11 out of 16 open-source models get worse results with CoT prompting.

## 2 Related Work

Multimodal benchmarks.
Benchmarking in vision-language models has been a long-standing research problem. Prior to large multimodal models, most benchmarks were focused on specific tasks or skills. Some benchmarks like VQA (Antol et al., ), GQA (Hudson & Manning, ), and ViswizVQA (Gurari et al., ) focus on photograph or natural images. ChartQA (Masry et al., ), InfoVQA (Mathew et al., ), DocVQA (Mathew et al., ), and OCR-VQA (Mishra et al., ) focus more on documents, infographics, and other similar media. Later on, there was a trend to build more well-rounded benchmarks to cover wider range of skills including MMMU (Yue et al., ), MMMU-Pro (Yue et al., ), MMT-Bench (Ying et al., ), MMBench (Liu et al., ), ScienceQA (Lu et al., ), and more. However, due to the diversity of these different tasks, most benchmarks simply use multiple-choice questions for all of the problems. Therefore, these benchmarks are not able to reflect the generational abilities of multimodal models. Complementary to this, LMsys arena (Chiang et al., ) and WildVision arena (Lu et al., ) have proposed to use user voting and Elo-ranking to benchmark multimodal models. Our benchmark is the first to scale up the tasks by a signficant magnitude. Also, our benchmark provides a breakdown report to show the models’ abilities across multiple dimensions.

Sensitivity of large model leaderboards to input format.
Creating reliable leaderboards poses a substantial challenge for evaluating large models.
Previous studies have noted that LLMs exhibit sensitivity to minor input modifications, including prompts and in-context examples in few-shot settings (Sclar et al., ; Chang & Jia, ).
To mitigate input sensitivities, researchers have developed specialized prompt design and prompting-based training approaches (Liu et al., ; Jain et al., ).
Nonetheless, for benchmarks that only allow a multiple-choice format (Wang et al., ), studies by Zheng et al. () and Robinson et al. () find the option sequencing can significantly alter model rankings on the leaderboard.
Recently, Alzahrani et al. () explores the advantage of a hybrid scoring method to stabilize models’ leaderboard rankings over input format.
Though MEGA-Bench does not include hybrid scoring for each individual task, the overall use of diverse and hybrid scoring methods and output formats across more than 500 tasks demonstrates the robustness of the benchmark.

## 3 MEGA-Bench

Figure: Figure 2: MEGA-Bench’s four keyword dimensions and the task-level statistics. The diversity along various dimensions enables fine-grained capability analysis.
Refer to caption: x1.png

MEGA-Bench is a comprehensive multimodal benchmark that spans 7 input formats, 6 output formats, 10 different types of skills, and a varying number of visual inputs, whether single-image, multi-image, or from video, as shown in Figure 2. Our benchmark covers 8 distinct subject areas in a hierarchical taxonomy to evaluate the ability of VLMs to tackle a variety of different tasks.

### 3.1 Benchmark Construction Process

Preparation.
Figure 3 illustrates our annotation process. In the conceptualization stage, we propose a “draft” task taxonomy tree with the top two levels of Figure 1. The first level consists of the task scopes like “Perception”, “Planning”, “Reasoning”, etc. The second level consists of more concrete categories like “Document Understanding”, “App Understanding”, “Logic Reasoning”, and so forth. We add a few exemplars under each second-level node and write detailed descriptions of the tasks we expect from the annotators.

Figure: Figure 3: The annotation process of MEGA-Bench. We first propose a “draft” taxonomy tree, and then distribute the meta-nodes to different annotators. We allow the annotators to gradually refine the tree structure as they add new tasks. Each task consists of many examples and has a shared task-level instruction, several per-example questions, and several per-question ground truth answers.
Refer to caption: x2.png

We then distribute the second-level nodes in “draft” taxonomy tree to the annotators based on their preferences. This top-down annotation framework can help minimize the overlaps of tasks contributed by different annotators. To ensure consistent commitment throughout the project, we call up over 16 designated expert annotators. Our annotators are all graduate students or above from different majors like Computer Science, Electronics, Bio-statistics, and Finance. The annotators can 1) refine the “draft” taxonomy by adding/deleting nodes, 2) add “task group” nodes and then add a series of tasks under that, and 3) directly add tasks under the second-level meta-task node. To simplify the task contribution, we develop tools to facilitate the annotation process, including 1) an interactive annotation tool that defines the annotation format and automatically unifies all annotations as JSON files (Please see §B.1 for our annotation format), 2) a GitHub repository to coordinate the task submission, reviewing, and discussion process, which was inspired by BIG-bench (Srivastava et al., ), and 3) a visualization tool that allows annotators to browse the existing tasks and the evaluation results of representative vision language models (VLMs). We coordinate all the annotators to ensure they understand our expectations and continuously improve our tools.

Task annotation.
The annotation process contains two rounds. The annotators submit tasks to the benchmark by creating pull requests (PR) to the main branch of our GitHub repository.

In the first round of the annotation process, we ask the annotators to contribute 20 tasks following the principles below to ensure the quality of the task:

$\bullet$ Data source and answer format: Creative tasks with diverse data sources and answer formats are encouraged. If the data was collected from existing datasets, we ask annotators to adapt the original annotation into more specific questions and design more diverse answer formats.

$\bullet$ Number of examples: Each task should have at least 15 examples. Exceptions are allowed for some complicated tasks where the data are scarce.

$\bullet$ Documentation: Each task should be accompanied by documentation that indicates the source of the data, the capabilities the task tries to evaluate, and the evaluation metric to be used.

Our core contributors review each PRs carefully to provide feedback, and the accepted PRs are merged into the main branch. We periodically run the evaluation with commercial VLMs (e.g., GPT-4o) and update the results of existing tasks on our visualization page, which allows the annotators to better understand the difficulty of their tasks and catch potential glitches in the annotation. We found that this helps significantly improve the annotation quality.

Before the second round of annotations, the core contributors gathered with the annotators to review all the contributed tasks in the taxonomy tree and discover the biases in the task distribution. We then host another brainstorming session to propose new meta-tasks to balance the distribution and maximize the coverage. We then distribute the new meta-tasks to our annotators in the second round and follow the same guidelines as before to finish the rest of the annotations. After this round, each annotator contributes at least another 10 tasks to meet the requirements (total $\geq$ 30).

Quality control and refinement.
We leverage state-of-the-art VLMs to help the annotators better examine the quality of the tasks.
Concretely, we gather the results of GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro and compute an average score on each task. Tasks with almost 1.0 scores often have trivial questions (based on manual inspection) and can hardly distinguish the ability of different models. We ask the corresponding annotators to investigate and augment those tasks to increase the difficulty. For tasks with almost zero scores, the task reviewers audit them carefully and remove them if the zero score comes from an incorrect annotation or insufficient question context. Finally, the benchmark contains a total 505 tasks with roughly 8,200 examples, which is large enough to minimize the sample variance within each high-level taxonomy node. Please refer to §4.3 for an in-depth analysis of the number of examples per task.

### 3.2 Metrics for answers in diverse output formats

Figure: Figure 4: Representative examples for MEGA-Bench’s diverse output formats and the corresponding customized metrics. The outputs are the real responses from GPT-4o (OpenAI, ). We implement robust parsing to extract the final answer from raw responses.
Refer to caption: x3.png

To properly evaluate the tasks with different output formats, we develop a set of highly-customized evaluation metrics in parallel with the benchmark construction process (§3.1). Figure 4 shows several examples of the model outputs along with the task’s associated metrics. When new tasks are submitted to our GitHub repository, we implement any new metrics specified by the task authors. We use two types of metrics: rule-based metrics and LLM-assisted metrics.

Rule-based metrics. When there is a unique answer under the question context or the correctness of the answer can be verified by rules (e.g., if the generated story/poetry meets the desired formats, or if the generated code can pass the given test cases), we implement rule-based metrics for evaluation. To satisfy the needs of all tasks submitted by our annotators, we end up with a suite of over 40 rule-based metrics. Robust string parsing is also implemented to extract the answer from the model’s response. We conduct a sanity check to ensure the correct implementation of all rule-based metrics. Specifically, we create an “oracle” model that always returns the ground truth annotation. We then compute its score over all tasks evaluated with rule-based metrics. The sanity check is passed when the “oracle” model gets a 1.0 score. See §D.4 for details.

LLM-assisted metrics. For open-ended tasks that do not have a unique answer, we instead employ an LLM-assisted metric (Zheng et al., ; Li et al., ). We design a per-task evaluation prompt template and fill in the tailored evaluation criteria for each task. The LLM is instructed to compare the model response with the reference answer and assign a score from 1 to 10. The score is then normalized into [0, 1] to be consistent with the other metrics. See §D.3 for details.

We divide the tasks into two subsets based on the different evaluation processes. The Core Set is evaluated with rule-based metrics to make the evaluation fast and cost-free. The Open-Ended Set is evaluated with metrics that use an LLM-as-a-judge, where the evaluation pipeline calls a proprietary LLM over an API. Specifically, we use GPT-4o-0806 (OpenAI, ) as the judge LLM. The Core and Open-Ended sets contain 440 and 65 tasks, respectively.

### 3.3 Multi-dimensional Keywords for fine-grained analysis

Existing multi-task multimodal benchmarks analyze models according to dimensions like the image type and academic discipline (Yue et al., ), ability (Liu et al., ), or meta-task (Ying et al., ).
MEGA-Bench offers a broad and diverse range of coverage across all these dimensions, and extends even further beyond them.

As explained in §3.1, the taxonomy tree divides the tasks into general application scenarios, the most manageable dimension for distributing the annotation efforts to different annotators. After we collected all tasks and finished the quality control process, we grouped all tasks based on four extra dimensions: input visual type, input visual number, output format, and required skills (Figure 2). Each dimension has 6 to 10 keywords, enabling fine-grained analysis and comparison. Interactive visualization tools can then be developed based on our evaluation results, which allows model developers to delve deep into different aspects of a model, and compare different models comprehensively like how a scout compares athletes.

**Table 1: A comparison between MEGA-Bench and existing works. MEGA-Bench has a greater diversity in data sources, input/output format, the number of metrics, and the number of tasks.**
| Dataset | Annotation | Source | Input | Output | #Metrics | #Tasks |
| --- | --- | --- | --- | --- | --- | --- |
| VQA-v2 (Antol et al., ) | New | Photo | 1 Image | Phrase/Bool/Num | 1 | 1 |
| GQA (Hudson & Manning, ) | New | Photo | 1 Image | Phrase/Bool/Num | 1 | 1 |
| VizwizVQA (Gurari et al., ) | New | Photo | 1 Image | Phrase/Bool/Num | 1 | 1 |
| ChartQA (Masry et al., ) | New | Chart | 1 Image | Bool/Num | 1 | 1 |
| AI2D (Kembhavi et al., ) | New | Diagram | 1 Image | Multi-choice (MC) | 1 | 1 |
| GeoQA (Chen et al., ) | New | Geometry | 1 Image | Multi-choice (MC) | 1 | 1 |
| NLVR^2 (Suhr & Artzi, ) | New | Photo | 2 Images | Bool | 1 | 1 |
| InfoVQA (Mathew et al., ) | New | Infographics | 1 Image | Phrase/Bool/Num | 1 | 1 |
| DocVQA (Mathew et al., ) | New | Document | 1 Image | Phrase/Bool/Num | 1 | 1 |
| OCR-VQA (Mishra et al., ) | New | Book covers | 1 Image | Phrase | 1 | 1 |
| MathVista (Lu et al., ) | Repurposed | Existing | 1 Image | Free-form (FF) | 1 | 31 |
| MMBench (Liu et al., ) | Repurposed | Existing | 1 Image | Multi-choice (MC) | 1 | 20 |
| MME (Yin et al., ) | Repurposed | Existing | 1 Image | Multi-choice (MC) | 1 | 14 |
| MMStar (Chen et al., ) | Repurposed | Existing | 1 Image | Multi-choice (MC) | 1 | 18 |
| MMVet (Yu et al., ) | Repurposed | Existing | 1 Image | Multi-choice (MC) | 1 | 16 |
| ScienceQA (Lu et al., ) | New | K12 Books | $\leq$ 1 Image | Multi-choice (MC) | 1 | 26 |
| MMMU (Yue et al., ) | New | Diverse | $\geq$ 1 Image | Multi-choice (MC) | 1 | 30 |
| MUIRBench (Wang et al., ) | Hybrid | Existing | $>$ 1 Image | Multi-choice (MC) | 1 | 12 |
| MileBench (Song et al., ) | Repurposed | Existing | $>$ 1 Image | MC or FF | 2 | 28 |
| VideoMME (Fu et al., ) | New | Youtube | Video | Multi-choice (MC) | 1 | 30 |
| MVBench (Li et al., ) | Repurposed | Existing | Video | Multi-choice (MC) | 1 | 20 |
| MMTBench (Ying et al., ) | Repurposed | Existing | Image/Videos | Multi-choice (MC) | 1 | 162 |
| MEGA-Bench | New | Unrestricted | Image/Videos | Unrestricted | 45 | 505 |

### 3.4 Dataset statistics and comparison with other benchmarks

MEGA-Bench contains 505 real-world tasks with 8,186 manually annotated or repurposed samples. Even for repurposed data, considerable effort is needed to convert the original annotations into specific task descriptions, diverse output formats, and additional instructions to include auxiliary information and about the formatting. Figure 2 shows the task distribution of all five dimensions, and the detailed breakdown statistics are in §C.2.

Table 1 compares MEGA-Bench to existing multimodal benchmarks. The key feature of our benchmark is the diversity across all aspects in the table. MMMU (Yue et al., ; ) focuses on college-level exam questions with various discipline and image formats, testing visual and textual fusion with advanced knowledge. All questions are single-image and answered in multiple-choice format.
MMT-Bench (Ying et al., ) covers 162 concrete sub-tasks, enabling in-depth analysis based on their “taskonomy” and diverse input forms. However, all of the tasks MMT-Bench are from existing datasets and these tasks are mostly to under the “Perception” sub-tree in our taxonomy, and all outputs are in multiple-choice form like MMMU.
To maximize task coverage and the diversity in model outputs with cost-effective inference, MEGA-Bench includes a much broader range of task types and output formats, while having fewer total samples compared to existing benchmarks.

## 4 Experiments

We evaluate 22 VLMs with multi-image support on MEGA-Bench. §4.1 describes the models being assessed and the evaluation pipeline. §4.2 presents the quantitative evaluation results with a fine-grained analytical breakdown. §4.3 discusses the impact of the number of examples per task on the evaluation results. §4.4 provides error analysis based on GPT-4o. Detailed inspections of model behaviors with concrete error examples can be found in §F of the Appendix.

### 4.1 Evaluation Settings

Evaluated models.
We evaluate a diverse range of large multimodal models. The proprietary models assessed include GPT-4o (0513) and GPT-4o mini (OpenAI, ), Claude-3.5-Sonnet (0620 and 1022) (Anthropic, ; ), Gemini-1.5-Pro (002) and Gemini-1.5-Flash (002) (Google, ). For open-source models, we mainly focus on large flagship ($>$70B parameters) and small-to-medium efficiency models. The large models include Qwen2-VL-72B (Alibaba, ), InternVL2-Llama3-76B (Chen et al., ), LLaVA-OneVision-72B (Li et al., ), and NVLM (Dai et al., ). The medium-scale models comprise Qwen2-VL-(7B/2B) (Alibaba, ), Pixtral 12B (Mistral, ), Aria (Li et al., ), InternVL2-(8B/2B) (Chen et al., ), Phi-3.5-Vision (Abdin et al., ), MiniCPM-V2.6 (Yao et al., ), LLaVA-OneVision-7B (Li et al., ), Llama-3.2-11B Meta (), Idefics3-8B-Llama3 (Laurençon et al., ), and Aquila-VL-2B-llava-qwen (Gu et al., ).

Evaluation pipeline.
MEGA-Bench has diverse and flexible formats. To ensure the models have clear instructions on the output format, we provide all evaluated VLMs with a one-shot in-context example. For each query, we fill in a pre-defined prompt template with the task instructions written by the task annotators, the 1-shot example, and the concrete query question.
Since this one-shot example’s primary purpose is to illustrate the output format, we allocate only a tiny portion of the total image budget for it.
For each model, we conduct experiments with and without Chain-of-Thought (CoT) prompting (Wei et al., ) for the Core tasks (the one-shot example of Open-ended tasks already contains CoT demonstrations).
The prompt templates and other evaluation details (e.g., the frame sampling strategy for video inputs) are in §D.

Single-image setting.
Our evaluation pipeline focuses on models with multi-image support. To properly evaluate models trained mainly for single-image use cases, we create a single-image setting using the single-image tasks of MEGA-Bench. See §A for the detailed results and analyses.

**Table 2: The main results of different models on the Core and Open-ended subset of MEGA-Bench, with 440 and 65 tasks, respectively. We report the macro mean scores across all tasks in each set. The overall score is the weighted average of the Core and Open-ended scores. When computing the overall score, we use the higher Core score from ‘w/o CoT’ and ‘w/ CoT’. Some open-source models perform more reasonably when the input contains only a single image. Please see §A for results and analyses of our single-image setting.**
| Model | Eval Tier | Open Source | Core (rule eval) | Open-ended<br>(GPT eval) | Overall |  |
| --- | --- | --- | --- | --- | --- | --- |
| w/o CoT | w/ CoT |  |  |  |  |  |
| Claude-3.5-Sonnet (1022) (Anthropic, ) | Flagship | No | 49.20 | 52.59 | 65.63 | 54.27 |
| GPT-4o (0513) (OpenAI, ) | Flagship | No | 52.03 | 52.65 | 64.78 | 54.21 |
| Claude-3.5-Sonnet (0620) (Anthropic, ) | Flagship | No | 48.80 | 50.41 | 63.74 | 52.13 |
| Gemini-1.5-Pro-002 (Google, ) | Flagship | No | 46.99 | 48.22 | 58.58 | 49.55 |
| Gemini-1.5-Flash-002 (Google, ) | Efficiency | No | 41.90 | 41.89 | 56.91 | 43.82 |
| GPT-4o mini (OpenAI, ) | Efficiency | No | 39.85 | 40.77 | 58.65 | 43.07 |
| Qwen2-VL-72B (Alibaba, ) | Flagship | Yes | 46.41 | 45.42 | 56.40 | 47.70 |
| InternVL2-Llama3-76B (Chen et al., ) | Flagship | Yes | 35.02 | 35.63 | 51.93 | 37.73 |
| LLaVA-OneVision-72B (Li et al., ) | Flagship | Yes | 31.99 | 29.74 | 45.99 | 33.79 |
| NVLM-72B (Dai et al., ) | Flagship | Yes | 24.21 | 21.59 | 34.78 | 25.57 |
| Qwen2-VL-7B (Alibaba, ) | Efficiency | Yes | 34.80 | 32.93 | 43.96 | 35.98 |
| Pixtral-12B (Mistral, ) | Efficiency | Yes | 31.91 | 31.36 | 45.66 | 33.68 |
| Aria-MoE-25B (Li et al., ) | Efficiency | Yes | 30.49 | 28.90 | 51.03 | 33.13 |
| InternVL2-8B (Chen et al., ) | Efficiency | Yes | 25.96 | 24.09 | 39.79 | 27.74 |
| Phi-3.5-Vision-4B (Abdin et al., ) | Efficiency | Yes | 23.27 | 23.00 | 39.48 | 25.36 |
| MiniCPM-V2.6-8B (Yao et al., ) | Efficiency | Yes | 22.88 | 22.96 | 41.73 | 25.38 |
| LLaVA-OneVision-7B (Li et al., ) | Efficiency | Yes | 22.41 | 21.36 | 33.98 | 23.90 |
| Qwen2-VL-2B (Alibaba, ) | Efficiency | Yes | 16.45 | 20.88 | 31.54 | 22.25 |
| Llama-3.2-11B (Meta, ) | Efficiency | Yes | 10.04 | 16.00 | 31.73 | 18.02 |
| Aquila-VL-2B-llava-qwen (Gu et al., ) | Efficiency | Yes | 16.32 | 16.00 | 24.57 | 17.38 |
| InternVL2-2B (Chen et al., ) | Efficiency | Yes | 9.09 | 13.14 | 23.86 | 14.52 |
| Idefics3-8B-Llama3 (Laurençon et al., ) | Efficiency | Yes | 11.12 | 8.96 | 32.11 | 13.82 |

### 4.2 Main Results with breakdown analysis

Table 2 presents the main evaluation results, with Figure 5 and Figure 6 being the accompanying fine-grained breakdowns enabled by MEGA-Bench’s multi-dimensional diversity. We discuss some important findings below and leave a full breakdown of the results in §E of the Appendix. For the sake of careful comparison, we organize the results into two tiers: (1) The Flagship Model Tier compares the strongest performing models from each model’s organization, (believed) with #params$\,\geq\,$70B. (2) The Efficiency Model Tier compares efficiency models from each model’s organization, (believed) with #params$\,\leq\,$20B.

Figure: Figure 5: Fine-grained breakdown analysis of flagship models on four dimensions. From top-left to bottom-right: input format, output format, skills, and application.
Refer to caption: x11.png

Flagship models. Unlike the results on recent benchmarks like MMMU-Pro (Yue et al., ) where GPT-4o (0513) and Claude-3.5-Sonnet (0620) get close scores, GPT-4o (0513) outperforms Claude-3.5-Sonnet (0620) with a clear margin on MEGA-Bench ($>$ 2%). Investigating the breakdown results, we observe that GPT-4o (0513) wins in most applications/skills except for coding, math, and planning-related tasks, where the answers are typically in a “structured” output format, as shown in Figure 5. The recent update of Claude-3.5-Sonnet (1022) makes consistent improvements across almost all dimensions, especially in planning-related tasks and those with infographics/UI/photographs inputs, and slightly surpasses GPT-4o (0513) in the overall score ($<$ 0.1%). The “Planning” application keyword contains tasks like symbolic planning (Zhu et al., ), navigation (Ku et al., ), chess games (Fu et al., ), puzzle games (e.g., maze, Sudoku, etc.), and even the best models get low scores.

One typical observation of Claude-3.5-Sonnet models is that they tend to be meticulous and refuse to answer routine knowledge or commonsense questions, such as the name and nationality of famous actors. The bottom radar maps show that they fall behind in knowledge, information extraction, and commonsense reasoning compared to GPT-4o, partially because of this refusal behavior.

The evaluation results suggest that Qwen2-VL performs particularly well amongst open-source models of similar parameter sizes. In Figure 5, Qwen2-VL-72B gets a similar score to closed-source models in the general perception category and outperforms Gemini-1.5-Pro-002 on information extraction tasks.
Llava-OneVision-72B scores very low when the visual inputs are in “UI related” and “Document” formats while performing well on video inputs. This suggests a lack of OCR and language parsing abilities, which can be confirmed with its skills radar plot.

Figure: Figure 6: Fine-grained analysis of efficiency models on input format (left) and application (right).
Refer to caption: x12.png

Efficiency models. Figure 6 analyzes the results on efficiency models. In general, Gemini-1.5-flash-002 has the best performance with exceptional scores in Science and Metrics applications. The Metrics keyword contains tasks such as rating the quality of GenAI results (He et al., ; Jiang et al., ) and requires deep multimodal reasoning and commonsense. However, its performance on UI-related inputs and information extraction tasks falls behind GPT-4o mini.

Chain-of-Thought. An interesting finding is that the CoT prompt (See §D) effectively guides all proprietary models to generate a detailed reasoning process, and flagship-tier proprietary models all obtain better performance on the Core set. However, it has almost no effect on most open-source models. For example, the Qwen2-VL, InternVL2, and LLaVA-OneVision models rarely produce reasoning when given a CoT instruction, and sometimes get confused about the required format after generating the reasoning process, leading to a lower score on the Core set.

Some open-source models get comparatively low scores for their parameter count. Llama-3.2-11B has difficulty leveraging the one-shot example to understand the correct output format and tends to generate a long descriptive sentence instead. This issue is alleviated under the CoT setting because the prompt provides extra instructions on the output format beyond the one-shot example, requesting the model to strictly separate the reasoning process from the final answer. Idefics3 frequently repeats the example answer from the one-shot demonstration. We suspect the reason for this problem is the poor support for multi-image (as our query contains at least two images, including the one-shot example) since it can generate reasonable responses with a single-image input. §A in the Appendix presents a single-image setting of MEGA-Bench and conducts further analyses.

### 4.3 Analysis on the number of samples per task

As discussed in §1, one of MEGA-Bench’s goals is to optimize the inference cost while still producing detailed multi-dimensional breakdown analysis. Therefore, we prioritize expanding the number of tasks over adding many examples per task in the benchmark construction process.
To understand the robustness of the benchmark score with around 15 examples per task, we obtained bootstrap distributions (Efron & Tibshirani, ; Hesterberg, ) of the model scores for our Core set with the CoT prompting. We did this by taking a random subset of the model’s responses of size $n$ ($n=3,5,\dots,13,15$) with replacement for each task and calculating the task-level macro-mean scores. To ensure the bootstrap distribution was numerically stable, we ran 10,000 Monte Carlo simulations.
Figure 7 (left) shows that the variance in model scores rapidly narrows as the number of examples per task increases. As the number of examples per task increases beyond 7, the marginal return in variance reduction diminishes.

Figure: Figure 7: (Left) The bootstrap distribution of benchmark scores of a subset of models as we gradually increase the bootstrap sample size of the number of examples per task. We use the results with the CoT prompting. Claude 3.5 Sonnet (1022) and Gemini 1.5 Pro (002) are used in this figure. (Right) The task-wise error distribution of GPT-4o (0513) over a subset of 255 Core tasks.
Refer to caption: x13.png

### 4.4 Error Analysis

To understand the limitations of state-of-the-art VLMs, we analyze the GPT-4o (0513) results by manually identifying the error types over a subset of 255 tasks from the Core set. We use the CoT setting since the reasoning process helps determine the error type. Figure 7 (right) presents the error distribution. For GPT-4o, the lack of various reasoning capabilities (e.g., symbolic reasoning for planning/coding tasks, spatial or temporal reasoning for complex perception tasks, etc.) is the dominating failure mode on MEGA-Bench. Please refer to §F for the full definition of error types and detailed example-wise inspection results with different models.

## 5 Conclusion

This paper presents MEGA-Bench, a comprehensive benchmark that scales multimodal evaluation to over 500 real-world tasks but at a manageable inference cost. By systematically organizing tasks across dimensions like skill, output format, and input type, we enable fine-grained analysis of multimodal models. Our evaluation of state-of-the-art VLMs revealed significant performance variations between models that previously seemed similar. MEGA-Bench provides a new standard for multimodal evaluation, offering a robust analysis tool for model development.

## Appendix A Single-image Setting: Results and Analyses

Table 2 in the main paper focuses on models with multi-image support. However, some open-source models are only trained with single images. To provide a feasible evaluation setting for these models, we create a single-image (SI) setting using the single-image tasks in MEGA-Bench, containing 273 and 42 tasks from the Core and Open-ended sets, respectively.

Evaluation setup.
The Chain-of-Thought (CoT) prompting is used for Core SI tasks. To make the entire query contain only one image as needed by some single-image models, we drop the image input of the 1-shot demonstration example (“✗ demo im” column in the table). In this case, the 1-shot example only demonstrates the output format, which is necessary for inferring the correct answer. For those models already evaluated in Table 2, we calculate the task-level average scores on single-image tasks to obtain the “✓ demo im” results. Compared to Table 2, 3 single-image models are evaluated and added: Molmo-72B-0924 (Deitke et al., ), Molmo-7B-D-0924 (Deitke et al., ), and POINTS-Qwen2.5-7B (Liu et al., ).

Evaluation results.
Table 3 presents the evaluation results of the SI setting. The Core and Open-ended scores of the standard setting (with CoT prompting) are also in the table for reference. Some observations from the table are listed below:

$\bullet$ Single-image tasks are easier than multi-image tasks in general, and all models get higher scores in the SI setting than in the standard setting.

$\bullet$ GPT-4o has the best overall SI score, slightly higher than Claude 3.5 Sonnet (1022). Interestingly, GPT-4o mini overtakes Gemini-1.5-Flash-002 under the SI setting, suggesting that Gemini-1.5-Flash has pretty stable performance across different numbers of image inputs.

$\bullet$ NVLM-72B (Dai et al., ) has much better scores in the SI setting than in the standard setting, suggesting its training data might only contain single or a few images.

$\bullet$ Comparing the “✓ demo im” and “✗ demo im” results of open-source models, the image input in the 1-shot demonstration example is not well utilized by the models to better understand the task logic. Including the image input in the demonstration example makes the results much worse for models like Llama-3.2-11B.

More detailed breakdown results are available on our project page and the leaderboard (hosted with Hugging Face Spaces)

**Table 3: The single-image (SI) setting results of MEGA-Bench. The Core set evaluation uses Chain-of-Thought (CoT) prompting. The “demo img” means the image input of the 1-shot demonstration example. The “✓ demo im” directly takes the single-image subset average from the full results in Table 2. The “✗ demo im” means the 1-shot demonstration example only demonstrates the output format, and the entire query has a single image. We report “✓ demo im” alone for the proprietary models because they have good multi-image support. For open-source models, we do additional evaluations with the “✗ demo im” setting and use it to compute the overall score.**
| Model | Core | Core SI | Open | Open SI | Overall<br>SI |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | ✓ demo im | ✗ demo im |  | ✓ demo im | ✗ demo im |  |  |
| GPT-4o (0513) (OpenAI, ) | 52.65 | 55.30 | - | 64.78 | 66.00 | - | 56.73 |
| Claude-3.5-Sonnet (1022) (Anthropic, ) | 52.59 | 54.63 | - | 65.63 | 67.64 | - | 56.36 |
| Claude-3.5-Sonnet (0620) (Anthropic, ) | 50.41 | 52.03 | - | 63.74 | 64.80 | - | 53.73 |
| Gemini-1.5-Pro-002 (Google, ) | 48.22 | 49.14 | - | 58.58 | 58.15 | - | 50.34 |
| GPT-4o mini (OpenAI, ) | 40.77 | 44.31 | - | 58.65 | 59.56 | - | 46.32 |
| Gemini-1.5-Flash-002 (Google, ) | 41.89 | 43.48 | - | 56.91 | 57.87 | - | 45.40 |
| Qwen2-VL-72B (Alibaba, ) | 45.42 | 47.31 | 47.31 | 56.40 | 58.50 | 55.10 | 48.34 |
| InternVL2-Llama3-76B (Chen et al., ) | 35.63 | 39.32 | 39.99 | 51.93 | 55.33 | 55.47 | 42.05 |
| Molmo-72B-0924 (Deitke et al., ) | - | - | 36.48 | - | - | 44.66 | 37.58 |
| NVLM-72B (Dai et al., ) | 21.59 | 31.19 | 32.99 | 34.78 | 48.67 | 44.69 | 34.55 |
| LLaVA-OneVision-72B (Li et al., ) | 29.74 | 31.77 | 31.26 | 45.99 | 46.12 | 44.26 | 32.99 |
| Qwen2-VL-7B (Alibaba, ) | 32.93 | 35.04 | 35.39 | 43.96 | 45.87 | 45.17 | 36.69 |
| Pixtral-12B (Mistral, ) | 31.36 | 34.87 | 34.37 | 45.66 | 44.03 | 44.17 | 35.68 |
| Aria-MoE-25B (Li et al., ) | 28.90 | 31.67 | 31.79 | 51.03 | 50.92 | 51.37 | 34.40 |
| InternVL2-8B (Chen et al., ) | 24.09 | 27.19 | 27.65 | 39.79 | 40.94 | 39.39 | 29.21 |
| Phi-3.5-Vision-4B (Abdin et al., ) | 23.00 | 25.72 | 25.61 | 39.48 | 44.61 | 42.72 | 27.89 |
| POINTS-Qwen2.5-7B (Liu et al., ) | - | - | 25.51 | - | - | 30.32 | 26.15 |
| MiniCPM-V2.6-8B (Yao et al., ) | 22.96 | 23.82 | 23.23 | 41.73 | 42.54 | 43.61 | 25.95 |
| LLaVA-OneVision-7B (Li et al., ) | 21.36 | 22.70 | 23.68 | 33.98 | 36.44 | 38.71 | 25.69 |
| Qwen2-VL-2B (Alibaba, ) | 20.88 | 24.16 | 22.78 | 31.54 | 30.59 | 35.09 | 24.43 |
| Molmo-7B-D (Deitke et al., ) | - | - | 20.98 | - | - | 35.70 | 22.95 |
| Llama-3.2-11B (Meta, ) | 16.00 | 17.34 | 20.79 | 31.73 | 34.29 | 38.61 | 23.17 |
| Aquila-VL-2B-llava-qwen (Gu et al., ) | 16.00 | 16.98 | 20.77 | 24.57 | 24.58 | 31.47 | 22.20 |
| InternVL2-2B (Chen et al., ) | 13.14 | 13.83 | 12.07 | 23.86 | 24.28 | 28.52 | 14.26 |
| Idefics3-8B-Llama3 (Laurençon et al., ) | 8.96 | 9.13 | 8.94 | 32.11 | 33.25 | 32.31 | 12.06 |

## Appendix B Details of Annotation Protocols

This section presents additional details of our task annotation pipeline and protocols, providing complete details for §3.1 of the main paper.

### B.1 The unified annotation format

Figure 8 presents the annotation format designed and used in our annotation process. All annotated tasks share this unified structure, including task instruction, optional global media to provide context to all the questions (typically used in retrieval-related tasks). Additionally, each specific example contains distinct media path(s), a concrete question, and an answer with a single or multiple answer fields. Multi-field answers are organized as JSON structures.

Figure: Figure 8: The structure of our task annotation format, which helps coordinate all task annotators and standardize the annotation format.
Refer to caption: x15.png

Our evaluation pipeline follows this format to convert the task information into concrete queries and feed them to the evaluated model. Based on this format, we establish an interactive annotation tool to ensure the tasks submitted by all annotators have the correct and unified format. Figure 9 demonstrates the GUI of the annotation tool.

Figure: Figure 9: A screenshot of our GUI annotation tool.
Refer to caption: extracted/5993817/assets/annotation_tool.png

### B.2 General task collection and creation guidelines

This subsection provides more detailed annotation guidelines for our annotators, complementing the descriptions in §3.1.

Data source of each task.
There is no restriction on the data source as long as the annotator follows the copyright and license requirements of the original data. Below are three typical task types and their data sources:

(1). The task is designed entirely by the annotator, and the annotator looks for the image or video resources from the Internet or even using code/simulator;

(2). The task is inspired by existing benchmarks or datasets. The annotator collects the raw image/video data from existing datasets but does not use the original annotation. The annotator redesigns/repurposes the data by writing concrete task descriptions and creating new questions and answers, or using scripts to re-process the data for the designed task.

(3). The task is directly converted from existing benchmarks or datasets. The annotator randomly samples a subset from the existing benchmark, directly using its image/video and the annotation without redesign.

In our annotation process, the first two task types are encouraged. The task reviewers strictly control the number of the third type and reject the task if an annotator submits many tasks of the third type. Appendix G shows the detailed data source of all tasks in MEGA-Bench.

Output format and answer uniqueness. We aim to cover diverse output formats in MEGA-Bench. Therefore, we always require the task annotators to consider adapting the original dataset’s answer format, especially avoiding unnecessary multiple-choice questions (many MCQs are unnatural and mainly for evaluation convenience). Notably, the annotator must provide sufficient context in the task description and per-example question so that the range of the correct answer is manageable and the task can be evaluated with a clearly defined metric.

Metric specification.
When creating a task, the annotator must specify the corresponding evaluation metric. Since the metric implementation is in parallel to the task construction process, as described in §3.2, our GUI annotation tool (Figure 9) allows annotators to choose from existing metrics for each answer field of the task and assigns different weights to each field. When the desired metric is unavailable, the annotator chooses an “unsupported” metric type and writes down detailed metric specifications in the pull request. Our core contributors periodically check the needs of new metrics and implement them.

Documentation.
When submitting the pull request, the annotator must write README documentation for each task. If the desired metric has not been implemented, the documentation should contain the specification described in the last point. Furthermore, the doc should record the data source (e.g., the Web, an existing dataset, etc.) and brief descriptions of the task. These descriptions are instrumental in helping the core contributors assign various keywords to the task and creating Appendix G to show the details of all tasks.

### B.3 Tools for coordinating annotation and quality control

As described in §3.1, we have two additional tools for coordinating the annotation process and maintaining the data. We present the details in this subsection.

The GitHub repository for task organization.
We created a private GitHub repository for constructing MEGA-Bench. The repository’s main branch is protected, and all task submissions must go through pull requests (PRs). The core contributors serve as the task reviewers and discuss with task annotators in the pull request forum to ensure the task conforms to our data collection guidelines (§B.2). The code of our evaluation pipeline, including the model query and score computation, is maintained in the same repository. The core contributors submit pull requests to support different VLMs and add new evaluation metrics, and these PRs are cross-reviewed by other core contributors.

We also actively use the repository’s Issues forum to report bugs in annotation or metric implementation so the corresponding contributors can get notified and work on the fix.
At the end of the annotation process, our repository has 685 pull requests and 40 issues. 277 out of the 685 PRs are for task submission, indicating that many annotators submit task groups with more than one task in each PR. Other PRs are mainly for the evaluation pipeline and bug fixing.

Figure: Figure 10: Illustrations of our task visualization page.
Refer to caption: x16.png

Task visualization web page.
We developed a simple visualization web page and periodically synchronized the evaluation results of existing tasks on the page. The page provides several benefits: 1) it allows the core contributors to keep track of the overall annotation process, 2) it helps the annotators understand the capability of state-of-the-art VLMs, so that they can adjust the task difficulty accordingly, and 3) it facilitates the checking of the potential annotation glitches or metric bugs, significantly improving the overall quality of MEGA-Bench. Figure 10 shows screenshots of the visualization page taken during the benchmark construction process. Note that the task names in the figure might not align with the final names in the paper. In our project page, we will provide a similar visualization page for users to interactively inspect the behaviors of different VLMs.

## Appendix C Taxonomy Tree and Multi-dimensional Keywords

This section presents the full details of our application-based taxonomy tree and the multi-dimensional keywords.

### C.1 Details of the Taxonomy Structure

subsection C.1 shows the detailed structure of our application-driven task taxonomy. The first level defines the broad scope of use cases. At the second level, tasks are categorized into more specific domains. These first two levels guide the annotation process of our benchmark and are gradually updated/refined in the annotation process.
The third level lists the concrete names of tasks or task groups. If the third-level node is a task group, the number of concrete tasks under this group is shown in the parenthesis.

¿X—¿X—¿X

Details of taxonomy of MEGA-Bench.
Level-2 Tasks   Leaf Tasks (at Level-3 or deeper)   # Tasks
\endfirsthead

Table 3 – continued from previous page
Level-2 Tasks   Leaf Tasks (at Level-3 or deeper)   # Tasks
\endhead

Coding
Code Debugging Stackoverflow Debug Qa, Code Error Line Identification 2
Code Generation Document Conversion (8 tasks), Programming Problems (4 tasks), Visualization With Code 13
Code Translation Code Translation Easy, Code Translation Python, Code Translation Hard, Code Translation Advanced 4
Code Understanding Symbolic Graphics Programming (2 tasks), Webpage Code Understanding, Code Add Tag, Code Match (5 tasks), Code Output (3 tasks) 12
Information Extraction
App Function Understanding App Layout Understanding Leetcode, App Layout Understanding Youtube, App Layout Understanding Amazon, App Layout Understanding Word, App Layout Understanding Notes, App Layout Understanding Ppt, App Layout Understanding Alipay, App Layout Understanding Instagram, App Layout Understanding Zoom, App Layout Understanding Excel, App Layout Understanding Iphone Settings, App Layout Understanding Tiktok, App Layout Understanding Twitter 13
Compound Search and Calculate Cheapest Flight Identification, Weather Info Retrieval, Stock Info Retrieval, Game Platform Support Identification, Top Rated Hotel Identification, Movie Info Retrieval, Top Video Creator Identification, Highest Discount Game Price Identification, Newspaper Page Parse And Count, Remaining Playback Time Calculation 10
Detailed Manual Understanding Multi Lingual Manual Explanation Scooter Spanish, Multi Lingual Manual Explanation Scooter Arabic, Multi Lingual Manual Explanation Scooter French, Multi Lingual Manual Explanation Scooter Chinese, Multi Lingual Manual Explanation Scooter Russian 5
Multimodal QA Multilingual News Qa, Product Ocr Qa, Large Image (3 tasks), Gui Chat (2 tasks), Realworld Qa En2cn, Star Object Interaction Video, Video Qa (7 tasks) 16
Search by Attribute wo Calculate Coco Ood Global Image Retrieval By Query Property, Places365 Similar Scene Retrieval, Booking Web Recommendation, Game Info Retrieval, Media Homepage Profile, Movie Retrieval By Actor, Music Info Retrieval, Tv Show Retrieval By Character 8
Structured Parsing Multilingual Movie Info Parsing, Movie Info Parsing, Stock Info Parsing, Music Info Parsing, Multilingual Game Info Parsing, Ocr Article Authors, Youtube Video Info Parsing, Tv Show Info Parsing, Ocr Resume School Plain, Image Translation En2cn, Booking Web Rating, Weather Info Parsing, Game Info Parsing, Weather Map Climate Type Temperature Parsing, Hotel Booking Confirmation Parsing, Entertainment Web Game Style 16
Summarization Video Summary, Video Short Title, Video2notes, Video Content Reasoning 4
Knowledge
Arts Poetry Generation (7 tasks), Ascii Art 30 8
Fact Checking Background Change, Out Of Context, Text Entity Replace, Text Style, Face Attribute Edit, Face Swap, Interpret Force Perspective Illusion, Clip Stable Diffusion Generate, Unusual Images, Forensic Detection Of Different Images, Veracity, Distinguish Ai Generated Image 12
Human and Culture Cultural Vqa, Human Relationship Reasoning, Sign Language, Ishihara Test, Safety And Norm (13 tasks), Video Content Follow Up, Emotion And Intent Understanding (9 tasks), Theory Of Minds (2 tasks), Hashtag Recommendation 30
World Knowledge Dish Ingredient Match, Music (6 tasks), Insect Order Classification, Signage Navigation, Song Title Identification From Lyrics, Logo And Sign (3 tasks), Chinese Idiom Recognition, Ruozhiba (6 tasks), Font Recognition, Traffic Accident Analysis, Multiple State Identification (4 tasks), Worldle, Location Vqa, Daily (2 tasks), Ancient Map Understanding, Rocks Samples Compare, Painting (2 tasks), Memorization (4 tasks), Soccer Offside, Deciphering Oracle Bone, Actor Character And Famous People (3 tasks), Landmark And Buliding (3 tasks), Defeasible Reasoning 47
Mathematics
Algebra Algebra 1
Calculus Scibench Calculus Wo Solution 1
Functions Math Parity, Math Breakpoint, Math Convexity Value Estimation 3
General Math Exams V, Theoremqa, Math 3
Geometry Geometry Reasoning Count Line Intersections, Geometry Length, Geometry Reasoning Nested Squares, Geometry Transformation, Geometry Reasoning Overlapped Circle, Geometry Area, Geometry Reasoning Grid, Polygon Interior Angles, Geometry Solid, Geometry Analytic, Geometry Descriptive 11
Graph Theory Graph Shortest Path Kamada Kawai, Graph Shortest Path Planar, Graph Connectivity, Graph Theory, Graph Isomorphism, Graph Hamiltonian Cycle, Graph Hamiltonian Path, Graph Chordless Cycle, Topological Sort, Graph Maxflow 10
Number Theory Counterfactual Arithmetic 1
Numeric Reasoning Clevr Arithmetic, Iconqa Count And Reasoning, Number Comparison 3
Metrics
Generated Image Eval Autorater Artifact, Autorater Control, Autorater Artifact Reason, Autorater Aesthetics, Autorater Unmask, Autorater Subject, Autorater 3d Model Texturing, Autorater Semantics, Autorater Motion Guided Editing, Autorater Mask 10
Generated Video Eval Video Eval Visual Pref, Generated Video Artifacts, Video Eval Factual Pref, Video Eval Dynamic Pref 4
Paper Review Paper Review Writing, Paper Review Rating, Paper Review Acceptance 3
Quality Assessment Vizwiz Quality Accessment For Blind 1
Reward Models Reward Models T2i Reward, Reward Models I2t Reward 2
Perception
3D understanding Adapted Cvbench Depth, Relative Depth Of Different Points, Visual Prediction Rater Depth Estimation, Visual Prediction Rater Novel View Synthesis, Pokemon 3d Recognition, Av View Identification, Multiview Reasoning Camera Moving, 3d Indoor Scene Text Bbox Prediction, Google Streetview Circle Reasoning, Google Streetview Direction Understanding, Video Motion Matching Real 3d, Video Motion Matching 3d Real, Visual Prediction Rater 3d Assembled Quality Understanding, Visual Prediction Rater Surface Normal Estimation, Visual Prediction Rater Plane Segmentation, 3d Indoor Scene Text Bbox Selection, Google Streetview Circle Sorting 17
Counting Ad Count Detection, Adapted Cvbench Count, Av Vehicle Multiview Counting, Counting Multi Image, Av Human Multiview Counting, Shape Composition Shapes, Counting Single Image, Clevrer Video Moving Object Count, Shape Composition Colours 9
Diagram and Document Understanding Diagram (23 tasks), Document (9 tasks), Table Qa (6 tasks) 38
Image Segmentation Visual Prediction Rater Openable Part Segmentation, Visual Prediction Rater Panoptic Segmentation, Visual Prediction Rater Semantic Segmentation 3
Multimodal Captioning Video Detail Description, Guess Image Generation Prompt, Docci Image Description Long, Tweets Captioning, Image Captioning With Additional Requirements 5
Multimodal Constrained Captioning Contain Contain Images, Contain Repeat Length, Multi Contain Repeat Position Only Length, Contain Length, Contain Position Images, Contain Position Length, Xor Images, Multi Contain Repeat, Contain Contain Length, Multi Contain Position Only 10
Object and Scene Understanding Autonomous Driving Scene Analysis, Super Clevr Scene Understanding, Functionality Matching In Different Objects, Visual Dialog Image Guessing, Nlvr2 Two Image Compare Qa, Egocentric Analysis Single Image, Clevrer Object Existence Video, Snli Ve Visual Entailment, Ocr Open Ended Qa, Semantic Matching Of Two Images 10
Physical Understanding Physical Reasoning (8 tasks), Lighting And Shading (2 tasks) 10
Spatial Understanding Adapted Cvbench Relation, Visual Correspondance In Two Images, 2d Image Jigsaw Puzzle Easy, Geometry Plot Position Relationship, Adapted Cvbench Distance, Video Grounding Spatial, Egocentric Spatial Reasoning 7
Temporal Understanding Video To Camera Trajectory Retrieval, Sceneqa Scene Transition Video, Video Segments Reordering, Video Action Recognition, Action Sequence Understanding, Google Streetview Line Sorting, Next Action Prediction, Perception Test Video Action Count, Google Streetview Line Reasoning, Video Camera Motion Description, Video Grounding Temporal, Web Action Prediction, Cam Traj To Video Selection, Sta Action Localization Video 14
Visual Recognition Face Identity Matching, Rocks Samples Identify, Animal Pose Estimation, License Plate Recognition, Image Style Recognition, Long String Letter Recognition, Coco Object Detection By Query Property, Widerface Face Count And Event Classification, Handwritten Math Expression Extraction, Geometry Reasoning Circled Letter, Av Multicamera Tracking Predict Bbox, Ascii Art Understanding, Face Keypoint Detection, Extract Webpage Headline, Waldo, Geographic Remote Sensing Land Cover, Signboard Identification, Long String Number Recognition, Waybill Number Sequence Extraction, Single Person Pose Estimation, Coco Person Detection, Places365 Scene Type Classification 22
Planning
Agents and Planning Wikihow Complex Task Completion, Navigation (6 tasks), Gui Operation (18 tasks), Calendar Schedule Suggestion, Symbolic Planning (13 tasks) 39
Puzzles and Games Logical Reasoning Find Odd One Out, Logical Reasoning Fit Pattern, Perception Test Object Shuffle Video, Board Games (12 tasks), Bongard Problem, Number Puzzle Kakuro 5x5, Mensa Iq Test, Arc Agi, Mnist Pattern, Number Puzzle Sudoku, Move Pos To Pos Hanoi 4 Pole, Pictionary (5 tasks), Annoying Word Search, Logical Reasoning 2d Views Of 3d Shapes, Maze 2d 8x8, Crossword Mini 5x5, Rebus, Icon Arithmetic Puzzle, Iq Test Open Ended, Ball Cup Swap 3, Logical Reasoning 2d Folding 36
Reordering Perception Test Video Character Order, Comic Page Ordering, Recipe Image Ordering 3
Science
Chemistry Chemistry Exams V, Science Molecule Chemistry 2
Life Sciences Biology Exams V, Medical (15 tasks) 16
Physics Circuit Diagram Understanding, Mmmu Physics Chemistry Selected, Science Basic Physics, Physics Exams V 4
STEM Mmmu Pro Exam Screenshot, Scibench W Solution Open Ended, Arxiv Vqa, Tqa Textbook Qa, Question Solution Solving, Quizlet Question Solving, Scibench Fundamental Wo Solution 7

### C.2 Statistics of each keyword dimension

Figure 2 of the main paper presented the overall keyword distribution. As a complement, Table 4 provides more detailed statistics. Each of the five dimensions contains multiple keywords, and for each keyword, we explicitly show the number of related tasks and the total number of samples.

**Table 4: Number of tasks and samples across the five dimensions, with detailed breakdown into each keyword.**
| Dimension | Keywords (number of tasks, num of samples) |
| --- | --- |
| Skills | Object Recognition (303, 4755), OCR (137, 2239), Language Parsing & Gen. (154, 2509), Scene & Event Understanding (154, 2467), Math & Logical Reasoning (109, 1910), Commonsense & Social Reasoning (51, 855), Ethical & Safety Reasoning (15, 245), Domain-Specific Knowledge/Skills (77, 1387), Spatial & Temporal Reasoning (152, 2437), Planning & Decision Making (37, 577) |
| Input Format | User Interface (93, 1517), Text-rich Image & Doc (82, 1294), Diagrams & Visualizations (101, 1718), Videos (43, 698), Artistic & Creative (32, 542), Photographs (143, 2248), 3D Related (11, 169) |
| Output Format | Contextual Formatted (98, 1514), Structured (110, 1714), Exact (83, 1279), Numerical (49, 862), Open-ended (80, 1454), Multiple Choice (85, 1363) |
| Input Number | 6-8 images (21, 314), 9-image+ (41, 623), 1-image (315, 5228), Video (43, 698), 4-5 images (34, 520), 2-3 images (51, 802) |
| Application | Information_Extraction (72, 1124), Planning (78, 1239), Coding (31, 474), Perception (145, 2313), Metrics (20, 309), Science (29, 574), Knowledge (97, 1605), Mathematics (33, 547) |

## Appendix D Evaluation Details

This section details our evaluation settings, including the prompt template design, model query details, and evaluation metrics.

Figure: Figure 11: The prompt template structure without Chain-of-Thought (CoT).
Refer to caption: extracted/5993817/tex/figures/annotation_format/Non-CoT.png

### D.1 Prompt template

We provide the concrete prompt template in Figure 11 and Figure 12. All the information organized by the prompt template is serialized by our evaluation pipeline before sending queries to the evaluated model.

The non-CoT prompt instructs the VLM to strictly follow the one-shot example, directly producing the answer without additional text.
In contrast, the CoT prompt instructs the VLM to output step-by-step reasoning before providing the final answer, and the model must strictly separate the reasoning process from the final answer.

Note that our prompt sets different formats for single-field and multi-field outputs. Single-field answers must be explicitly indicated by the “Answer: …” format so that our output parser can robustly locate and extract the model’s answer. Multi-field answers are in JSON format, and our JSON parser can robustly extract the JSON-style answer from the entire response without the “Answer: …” format.

Figure: Figure 12: The prompt template structure for the Chain-of-Thought (CoT) setting
Refer to caption: extracted/5993817/tex/figures/annotation_format/CoT.png

### D.2 Model query details

**Table 5: The maximum number of images and the budget for the in-context example per model.**
| Model | Max # of images | In-context example budget |
| --- | --- | --- |
| GPT-4o (0513) (OpenAI, ) | 64 | 8 |
| Claude-3.5-Sonnet (1022) (Anthropic, ) | 64 | 8 |
| Claude-3.5-Sonnet (0620) (Anthropic, ) | 64 | 8 |
| Gemini-1.5-Pro-002 (Google, ) | 128 | 16 |
| Gemini-1.5-Flash-002 (Google, ) | 128 | 16 |
| GPT-4o Mini (OpenAI, ) | 64 | 8 |
| Qwen2-VL-72B (Alibaba, ) | 24 | 2 |
| InternVL2-Llama3-76B (Chen et al., ) | 24 | 4 |
| NVLM-72B Dai et al. () | 32 | 4 |
| Molmo-72B-0924 (Deitke et al., ) | 1 | 0 |
| LLaVA-OneVision-72B (Li et al., ) | 28 | 4 |
| Qwen2-VL-7B (Alibaba, ) | 18 | 2 |
| Pixtral-12B (Mistral, ) | 48 | 6 |
| Aria-MoE-25B (Li et al., ) | 32 | 4 |
| POINTS-Qwen2.5-7B (Liu et al., ) | 1 | 0 |
| InternVL2-8B (Chen et al., ) | 18 | 2 |
| Phi-3.5-Vision (Abdin et al., ) | 16 | 2 |
| MiniCPM-V2.6 (Yao et al., ) | 64 | 8 |
| Molmo-7B-D (Deitke et al., ) | 1 | 0 |
| LLaVA-OneVision-7B (Li et al., ) | 20 | 4 |
| Llama-3.2-11B (Meta, ) | 32 | 4 |
| Idefics3-8B-Llama3 (Laurençon et al., ) | 20 | 2 |
| Qwen2-VL-2B (Alibaba, ) | 16 | 2 |
| InternVL2-2B (Chen et al., ) | 18 | 2 |
| Aquila-VL-2B-llava-qwen (Gu et al., ) | 8 | 1 |

Since the evaluated VLMs have different context windows, we must tailor the number of query images or video frames for each model.
We implement an image/video pre-processing pipeline that follows the settings listed in
Table 5 to sub-sample the input images and videos.
We allocate different budgets for in-context examples and the query. Since the in-context examples (we use a one-shot example) mainly help models understand the task logic and the output format, we reserve most of the image budget for the query. Images or video frames surpassing the budget are discarded. To make sure the open-source models can run smoothly, we implement a fallback strategy, which reduces the image budget to decrease the number of input tokens if the model’s maximum context length is exceeded.

For images or video frames with a longer side larger than 1000 pixels, we resize the longer side to 1000 without changing the aspect ratio before sending them to the evaluated model. Each

### D.3 LLM-assisted metrics

The LLM-assisted metric instructs a multimodal LLM to evaluate VLM’s response by providing a detailed evaluation prompt. When submitting a task with open-ended answers that cannot be evaluated by rule-based metrics, the annotator is asked to write down a detailed evaluation prompt for the LLM judge following the prompt format in Figure 13.

Figure: Figure 13: The prompt template structure for LLM-Assisted Metrics
Refer to caption: x17.png

Concretely, the task annotator decides if the LLM judge should consider the question’s visual input when evaluating the model’s response. If yes, then the query media (images or videos) will be passed to the LLM as well (we use GPT-4o-0806 as a multimodal judge model).
For most tasks, the LLM judge can do a proper evaluation by comparing the model’s response with the reference answer, and the visual media is not needed. The task annotator also writes a thorough evaluation criteria, explaining to the judge model the meaning of each score range, which is important to get reliable evaluation results.

At the end of the prompt, a pre-defined scoring format instruction is attached, ensuring the judge model outputs a score between 1 and 10 and an explanation for the score.

### D.4 Rule-based metrics

We have over 40 highly customized rule-based metrics to evaluate the Core set of MEGA-Bench. Basic metrics like “extract string match” and “simple string match” (which ignores punctuation and special characters) are first added to the supported metric set. New metrics are implemented when our task annotators submit new tasks requiring uncovered metrics. In the end, we get 45 customized tasks, as shown in Table 6. The usage distribution is long-tail because many metric implementations are triggered by a single novel task.

**Table 6: All metrics used in MEGA-Bench.**
| Metric Name | Usage Count (# tasks) |
| --- | --- |
| Exact String Match | 198 |
| GPT-4o as Judge | 64 |
| Simple String Match | 61 |
| Multi Reference Phrase Evaluation | 25 |
| Constrained Generation | 18 |
| Set Equality | 15 |
| Sequence Equality | 15 |
| General Single Numerical Match | 14 |
| Exact String Match Case Insensitive | 14 |
| Sequence Accuracy Case Insensitive | 13 |
| Symbolic Planning Test | 13 |
| String Set Equality Comma | 9 |
| Normalized RMSE | 8 |
| Program Judge | 8 |
| Set Precision | 5 |
| Dictionary Equality | 4 |
| String Set Equality Line Break | 4 |
| Sequence Coordinates Similarity | 3 |
| LaTeX Expression Equality | 3 |
| Jaccard Index Case Insensitive | 3 |
| Jaccard Index | 3 |
| Normalized Bounding Box IOU Tuple | 2 |
| Number Relative Difference Ratio | 2 |
| XML Bounding Box IOU | 2 |
| Dictionary Exact String Match Aggregate Recall | 2 |
| Boxed Single Numerical Match | 2 |
| Positive Integer Match | 2 |
| Chess Move List Jaccard Index | 2 |
| Code Result Exact String Match | 1 |
| Normalized Bounding Box IOU Single | 1 |
| Normalized Bounding Box IOU Sequence | 1 |
| Normalized Similarity Damerau-Levenshtein | 1 |
| Near String Match | 1 |
| XML Normalized Point Distance | 1 |
| Dictionary Precision | 1 |
| Text with LaTeX Expression Equality | 1 |
| Angle Sequence Float RMSE | 1 |
| XML Normalized Point in Bounding Box | 1 |
| Longest Common List Prefix Ratio | 1 |
| Sequence Equality Case Insensitive | 1 |
| Set Equality Case Insensitive | 1 |
| GLEU (Chinese) | 1 |
| ASCII Art GPT-4O Judge | 1 |
| Dictionary Jaccard Aggregate Jaccard | 1 |
| Dictionary Normalized Bounding Box IOU Tuple Aggregate Jaccard | 1 |

### D.5 Answer extraction from model response

For Core tasks, our rule-based evaluation metrics compare the model’s answer with a ground-truth answer or some ground-truth constraints. Therefore, an answer extraction step is necessary to separate the final answer from the reasoning process and other irrelevant texts. We implement robust extraction logic for different types of outputs based on the format specified in the prompt template:

Single-field answer. We first reduce the answer by the “Answer: …” pattern. If this pattern does not exist, we take the entire response. Since many VLMs do not strictly follow the format instructions,
we have specific and extra processing for different output formats to improve robustness. Some typical examples are: 1) For multiple-choice outputs, we locate the exact letter or index choice using sophisticated regular expressions, which excludes any potential parenthesis or accompanying texts; 2) For code outputs, we extract the code from the potential code blocks; 3) For structured outputs, we parse the structural data into the proper Python data structures (list, set, dictionary, etc.), with tolerance on minor syntax errors (e.g., we automatically fix wrong quotes).

Multi-field answer. Since the prompt requires the model to output the final answer in JSON format, we implement a robust JSON parser to locate the JSON structure in the raw response and convert the JSON structure into the corresponding Python data structure.

If our comprehensive answer extraction fails to obtain any meaningful final answer from the model response, we consider the model as “fail to follow instructions”.

## Appendix E Complete Multi-dimensional Breakdown Results

This section provides the full breakdown results over the five dimensions of MEGA-Bench, complementing section 4 of the main paper.

### E.1 Breakdown results on the skill dimension

**Table 7: Average scores for each model on the skill dimension. The best-performing model in each category is in-bold, and the second best is underlined.**
| Model | CASR | DKAS | EASR | LUAG | MALR | ORAC | PADM | SAEU | SATR | TR |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Claude-3.5-Sonnet (1022) (Anthropic, ) | 59.1 | 54.9 | 65.7 | 60.8 | 48.9 | 56.9 | 29.1 | 55.1 | 43.2 | 62.2 |
| GPT-4o (0513) (OpenAI, ) | 63.5 | 55.1 | 68.0 | 61.6 | 44.2 | 56.3 | 22.9 | 58.2 | 39.4 | 62.2 |
| Claude-3.5-Sonnet (0620) (Anthropic, ) | 57.6 | 52.8 | 69.7 | 57.5 | 47.7 | 54.1 | 23.8 | 54.5 | 40.8 | 60.8 |
| Gemini-1.5-Pro-002 (Google, ) | 57.5 | 51.4 | 69.8 | 55.3 | 42.6 | 52.0 | 23.9 | 54.7 | 38.5 | 50.2 |
| Gemini-1.5-Flash-002 (Google, ) | 55.9 | 44.8 | 63.8 | 49.9 | 34.4 | 46.3 | 19.0 | 51.0 | 34.5 | 43.4 |
| GPT-4o mini (OpenAI, ) | 55.7 | 41.9 | 69.0 | 51.7 | 34.1 | 44.9 | 19.4 | 46.7 | 29.4 | 49.0 |
| Qwen2-VL-72B (Alibaba, ) | 56.8 | 46.3 | 60.5 | 53.9 | 37.8 | 49.8 | 22.0 | 50.9 | 35.1 | 54.4 |
| InternVL2-Llama3-76B (Chen et al., ) | 52.6 | 33.3 | 57.8 | 43.7 | 29.8 | 38.2 | 17.0 | 42.7 | 29.5 | 41.3 |
| LLaVA-OneVision-72B (Li et al., ) | 47.8 | 31.7 | 60.1 | 36.7 | 29.5 | 36.2 | 13.9 | 42.1 | 29.6 | 28.3 |
| NVLM-72B (Dai et al., ) | 40.9 | 25.8 | 45.6 | 29.4 | 26.4 | 24.0 | 6.7 | 22.8 | 15.7 | 32.2 |
| Qwen2-VL-7B (Alibaba, ) | 49.4 | 33.3 | 52.2 | 40.3 | 28.2 | 37.1 | 14.7 | 41.1 | 27.6 | 40.2 |
| Pixtral 12B (Mistral, ) | 41.9 | 32.8 | 56.9 | 38.3 | 28.3 | 34.6 | 10.6 | 37.8 | 26.8 | 37.8 |
| Aria-MoE-25B (Li et al., ) | 49.4 | 32.8 | 58.1 | 40.0 | 27.6 | 32.6 | 11.9 | 37.8 | 24.8 | 35.7 |
| InternVL2-8B (Chen et al., ) | 39.7 | 27.1 | 47.0 | 32.0 | 24.1 | 28.2 | 8.3 | 32.6 | 23.2 | 28.1 |
| Phi-3.5-Vision (Abdin et al., ) | 36.8 | 24.1 | 46.7 | 28.7 | 21.7 | 25.5 | 8.9 | 30.5 | 21.5 | 24.8 |
| MiniCPM-V2.6 (Yao et al., ) | 40.7 | 23.7 | 48.8 | 30.0 | 18.3 | 26.0 | 8.7 | 31.8 | 19.7 | 25.0 |
| LLaVA-OneVision-7B (Li et al., ) | 36.8 | 24.5 | 45.0 | 25.6 | 19.0 | 25.2 | 6.7 | 30.0 | 21.8 | 19.1 |
| Qwen2-VL-2B (Alibaba, ) | 31.3 | 20.8 | 41.4 | 25.7 | 17.6 | 22.2 | 6.2 | 26.5 | 17.3 | 23.7 |
| Llama-3.2-11B (Meta, ) | 32.3 | 17.7 | 42.6 | 19.6 | 13.3 | 19.1 | 6.6 | 22.4 | 15.4 | 14.3 |
| Aquila-VL-2B-llava-qwen (Gu et al., ) | 26.6 | 18.6 | 35.2 | 17.9 | 16.8 | 18.4 | 4.5 | 22.0 | 16.2 | 12.4 |
| InternVL2-2B (Chen et al., ) | 24.0 | 14.8 | 34.2 | 16.9 | 13.9 | 14.5 | 1.7 | 18.5 | 13.0 | 12.1 |
| Idefics3-8B-Llama3 (Laurençon et al., ) | 19.2 | 17.9 | 28.6 | 17.3 | 13.3 | 14.5 | 4.2 | 14.7 | 10.2 | 11.6 |

The abbreviations used in the table above are explained in the following table:

**Table 8: Abbreviation list of the keywords in the skill dimension.**
| Abbreviation | Skill |
| --- | --- |
| CASR | Commonsense and Social Reasoning |
| DKAS | Domain-Specific Knowledge and Skills |
| EASR | Ethical and Safety Reasoning |
| LUAG | Language Understanding and Generation |
| MALR | Mathematical and Logical Reasoning |
| ORAC | Object Recognition and Classification |
| PADM | Planning and Decision Making |
| SAEU | Scene and Event Understanding |
| SATR | Spatial and Temporal Reasoning |
| TR | Text Recognition (OCR) |

### E.2 Breakdown results on the input format dimension

**Table 9: Average scores for each model on the input format dimension. The best-performing model in each category is in-bold, and the second best is underlined.**
| Model | 3MAAI | AACC | DADV | P | TIAD | UIS | V |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Claude-3.5-Sonnet (1022) (Anthropic, ) | 44.2 | 55.6 | 55.6 | 54.3 | 48.9 | 60.5 | 49.5 |
| GPT-4o (0513) (OpenAI, ) | 47.8 | 56.4 | 50.0 | 56.1 | 49.1 | 60.8 | 53.2 |
| Claude-3.5-Sonnet (0620) (Anthropic, ) | 44.3 | 57.0 | 52.6 | 51.0 | 48.0 | 56.9 | 50.9 |
| Gemini-1.5-Pro-002 (Google, ) | 42.9 | 55.8 | 48.7 | 55.0 | 42.9 | 46.3 | 50.3 |
| Gemini-1.5-Flash-002 (Google, ) | 38.5 | 50.5 | 40.1 | 51.7 | 36.0 | 38.7 | 49.0 |
| GPT-4o mini (OpenAI, ) | 29.4 | 47.6 | 38.9 | 46.5 | 36.2 | 47.2 | 45.5 |
| Qwen2-VL-72B (Alibaba, ) | 36.2 | 50.8 | 42.1 | 49.8 | 42.9 | 54.0 | 49.9 |
| InternVL2-Llama3-76B (Chen et al., ) | 28.7 | 45.0 | 34.7 | 42.9 | 31.4 | 36.3 | 39.6 |
| LLaVA-OneVision-72B (Li et al., ) | 23.9 | 44.0 | 34.6 | 42.5 | 21.3 | 23.4 | 44.5 |
| NVLM-72B (Dai et al., ) | 5.7 | 34.7 | 30.3 | 32.6 | 21.7 | 23.9 | 0.0 |
| Qwen2-VL-7B (Alibaba, ) | 26.2 | 34.8 | 32.2 | 40.7 | 29.0 | 38.2 | 41.1 |
| Pixtral 12B (Mistral, ) | 24.0 | 37.5 | 32.2 | 37.1 | 28.8 | 30.7 | 41.0 |
| Aria-MoE-25B (Li et al., ) | 19.6 | 36.1 | 32.4 | 37.3 | 27.8 | 28.3 | 42.9 |
| InternVL2-8B (Chen et al., ) | 10.9 | 29.4 | 28.0 | 33.9 | 20.1 | 22.8 | 34.8 |
| Phi-3.5-Vision (Abdin et al., ) | 15.4 | 27.9 | 26.1 | 34.1 | 17.5 | 18.7 | 24.7 |
| MiniCPM-V2.6 (Yao et al., ) | 7.6 | 31.0 | 21.6 | 31.8 | 18.6 | 21.2 | 35.3 |
| LLaVA-OneVision-7B (Li et al., ) | 13.0 | 32.0 | 24.2 | 32.6 | 13.3 | 14.7 | 31.0 |
| Qwen2-VL-2B (Alibaba, ) | 13.4 | 24.9 | 19.6 | 28.8 | 16.3 | 19.1 | 25.2 |
| Llama-3.2-11B (Meta, ) | 6.4 | 25.2 | 16.9 | 24.9 | 11.5 | 11.9 | 21.2 |
| Aquila-VL-2B-llava-qwen (Gu et al., ) | 10.1 | 19.7 | 19.4 | 24.6 | 11.4 | 7.5 | 21.4 |
| InternVL2-2B (Chen et al., ) | 11.9 | 14.9 | 16.3 | 20.1 | 10.5 | 5.7 | 19.0 |
| Idefics3-8B-Llama3 (Laurençon et al., ) | 4.0 | 18.4 | 16.2 | 14.9 | 11.4 | 10.1 | 16.2 |

The abbreviations used in the table above are explained in the following table:

**Table 10: Abbreviation list of the keywords in the input formats dimension.**
| Abbreviation | Input Format |
| --- | --- |
| 3MAAI | 3D Models and Aerial Imagery |
| AACC | Artistic and Creative Content |
| DADV | Diagrams and Data Visualizations |
| P | Photographs |
| TIAD | Text-Based Images and Documents |
| UIS | User Interface Screenshots |
| V | Videos |

### E.3 Breakdown results on the output format dimension

**Table 11: Average scores for each model on the output format dimension. The best-performing model in each category is in-bold, and the second best is underlined.**
| Model | C | E | M | N | O | S |
| --- | --- | --- | --- | --- | --- | --- |
| Claude-3.5-Sonnet (1022) (Anthropic, ) | 51.9 | 53.9 | 57.8 | 48.2 | 62.4 | 50.7 |
| GPT-4o (0513) (OpenAI, ) | 53.9 | 59.9 | 54.5 | 44.6 | 62.7 | 48.0 |
| Claude-3.5-Sonnet (0620) (Anthropic, ) | 50.7 | 52.8 | 54.6 | 44.9 | 58.4 | 49.7 |
| Gemini-1.5-Pro-002 (Google, ) | 44.9 | 51.5 | 55.4 | 46.9 | 55.8 | 44.4 |
| Gemini-1.5-Flash-002 (Google, ) | 38.7 | 44.8 | 47.8 | 37.0 | 54.5 | 39.9 |
| GPT-4o mini (OpenAI, ) | 41.2 | 44.2 | 39.9 | 36.3 | 57.1 | 39.1 |
| Qwen2-VL-72B (Alibaba, ) | 44.7 | 51.0 | 52.0 | 40.3 | 51.6 | 45.0 |
| InternVL2-Llama3-76B (Chen et al., ) | 36.3 | 39.4 | 38.8 | 29.2 | 45.8 | 34.8 |
| LLaVA-OneVision-72B (Li et al., ) | 28.7 | 37.1 | 39.9 | 30.7 | 42.9 | 25.9 |
| NVLM-72B (Dai et al., ) | 22.9 | 27.9 | 18.5 | 23.3 | 32.2 | 27.9 |
| Qwen2-VL-7B (Alibaba, ) | 34.3 | 35.2 | 39.9 | 32.7 | 39.1 | 34.3 |
| Pixtral 12B (Mistral, ) | 30.8 | 36.4 | 30.1 | 32.1 | 41.7 | 31.9 |
| Aria-MoE-25B (Li et al., ) | 30.9 | 29.3 | 32.8 | 30.9 | 45.2 | 30.4 |
| InternVL2-8B (Chen et al., ) | 25.1 | 27.4 | 30.3 | 22.4 | 35.4 | 25.2 |
| Phi-3.5-Vision (Abdin et al., ) | 21.8 | 25.7 | 26.0 | 21.4 | 36.5 | 21.4 |
| MiniCPM-V2.6 (Yao et al., ) | 23.5 | 25.5 | 29.3 | 20.8 | 36.5 | 17.8 |
| LLaVA-OneVision-7B (Li et al., ) | 20.3 | 25.4 | 28.0 | 22.0 | 31.3 | 18.3 |
| Qwen2-VL-2B (Alibaba, ) | 16.2 | 20.0 | 25.7 | 22.0 | 30.2 | 21.0 |
| Llama-3.2-11B (Meta, ) | 12.4 | 15.8 | 19.3 | 15.0 | 30.0 | 16.4 |
| Aquila-VL-2B-llava-qwen (Gu et al., ) | 11.9 | 18.5 | 22.1 | 19.9 | 23.3 | 12.3 |
| InternVL2-2B (Chen et al., ) | 11.3 | 15.5 | 21.3 | 16.0 | 21.4 | 5.7 |
| Idefics3-8B-Llama3 (Laurençon et al., ) | 14.0 | 7.1 | 11.6 | 9.8 | 29.9 | 10.6 |

The abbreviations used in the table above are explained in the following table:

**Table 12: Abbreviation list of keywords in the output formats dimension.**
| Abbreviation | Output Format |
| --- | --- |
| C | Contextual Formatted Text |
| E | Exact Text |
| M | Multiple Choice |
| N | Numerical Data |
| O | Open-ended Output |
| S | Structured Output |

### E.4 Breakdown results on the application dimension

**Table 13: Average scores for each model on the application dimension. The best-performing model in each category is in-bold, and the second best is underlined.**
| Model | C | I | K | M | M2 | P | P2 | S |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Claude-3.5-Sonnet (1022) (Anthropic, ) | 51.7 | 65.9 | 56.6 | 47.6 | 61.2 | 55.6 | 39.9 | 55.1 |
| GPT-4o (0513) (OpenAI, ) | 50.3 | 70.6 | 61.4 | 44.0 | 61.0 | 55.1 | 33.2 | 52.8 |
| Claude-3.5-Sonnet (0620) (Anthropic, ) | 51.9 | 66.6 | 55.1 | 47.5 | 58.1 | 53.2 | 33.8 | 51.3 |
| Gemini-1.5-Pro-002 (Google, ) | 43.5 | 54.2 | 57.2 | 41.2 | 58.2 | 52.5 | 33.4 | 51.2 |
| Gemini-1.5-Flash-002 (Google, ) | 40.4 | 46.6 | 51.2 | 33.7 | 60.1 | 48.0 | 25.2 | 45.7 |
| GPT-4o mini (OpenAI, ) | 34.6 | 56.7 | 54.0 | 32.9 | 51.8 | 43.6 | 24.2 | 35.5 |
| Qwen2-VL-72B (Alibaba, ) | 43.7 | 58.1 | 51.7 | 31.2 | 49.7 | 53.6 | 31.2 | 44.9 |
| InternVL2-Llama3-76B (Chen et al., ) | 29.5 | 43.1 | 46.3 | 28.7 | 47.4 | 42.2 | 21.3 | 30.0 |
| LLaVA-OneVision-72B (Li et al., ) | 23.2 | 30.8 | 43.6 | 31.6 | 48.1 | 38.4 | 18.2 | 31.7 |
| NVLM-72B (Dai et al., ) | 23.9 | 22.8 | 37.2 | 24.5 | 18.9 | 30.2 | 8.0 | 24.9 |
| Qwen2-VL-7B (Alibaba, ) | 32.7 | 42.7 | 42.8 | 25.6 | 42.5 | 40.0 | 20.0 | 29.9 |
| Pixtral 12B (Mistral, ) | 25.7 | 43.0 | 38.1 | 24.2 | 50.2 | 38.9 | 13.6 | 31.3 |
| Aria-MoE-25B (Li et al., ) | 28.5 | 38.3 | 41.0 | 26.2 | 39.7 | 37.8 | 14.3 | 29.7 |
| InternVL2-8B (Chen et al., ) | 24.7 | 29.1 | 33.9 | 22.1 | 40.0 | 32.1 | 12.2 | 24.6 |
| Phi-3.5-Vision (Abdin et al., ) | 21.9 | 22.4 | 33.3 | 17.6 | 39.5 | 31.6 | 8.9 | 21.9 |
| MiniCPM-V2.6 (Yao et al., ) | 15.3 | 26.7 | 33.2 | 16.5 | 37.8 | 29.2 | 11.7 | 25.7 |
| LLaVA-OneVision-7B (Li et al., ) | 15.2 | 19.3 | 32.7 | 22.1 | 36.0 | 28.5 | 9.8 | 23.7 |
| Qwen2-VL-2B (Alibaba, ) | 17.0 | 25.2 | 26.6 | 16.4 | 31.0 | 27.6 | 7.0 | 21.1 |
| Llama-3.2-11B (Meta, ) | 5.8 | 17.3 | 28.1 | 13.9 | 25.4 | 19.9 | 8.1 | 16.3 |
| Aquila-VL-2B-llava-qwen (Gu et al., ) | 13.3 | 9.5 | 24.1 | 20.7 | 29.3 | 20.7 | 5.9 | 21.1 |
| InternVL2-2B (Chen et al., ) | 11.3 | 8.7 | 21.2 | 11.0 | 33.3 | 17.0 | 4.1 | 16.9 |
| Idefics3-8B-Llama3 (Laurençon et al., ) | 9.1 | 14.7 | 17.6 | 13.2 | 14.6 | 14.6 | 5.4 | 22.7 |

The abbreviations used in the table above are explained in the following table:

**Table 14: Abbreviation list of keywords in the applications dimension .**
| Abbreviation | Application |
| --- | --- |
| C | Coding |
| I | Information-Extraction |
| K | Knowledge |
| M | Mathematics |
| M2 | Metrics |
| P | Perception |
| P2 | Planning |
| S | Science |

### E.5 Breakdown results on the visual input number dimension

**Table 15: Average scores for each model on the visual input number dimension. The best-performing model in each category is in-bold, and the second best is underlined.**
| Model | 1 | 2I | 4I | 6I | 9OM | V |
| --- | --- | --- | --- | --- | --- | --- |
| Claude-3.5-Sonnet (1022) (Anthropic, ) | 56.4 | 48.8 | 48.3 | 46.3 | 59.1 | 49.5 |
| GPT-4o (0513) (OpenAI, ) | 56.7 | 49.1 | 45.0 | 47.5 | 53.4 | 53.2 |
| Claude-3.5-Sonnet (0620) (Anthropic, ) | 53.7 | 49.3 | 44.2 | 46.3 | 54.1 | 50.9 |
| Gemini-1.5-Pro-002 (Google, ) | 50.3 | 45.5 | 48.9 | 39.1 | 53.7 | 50.3 |
| Gemini-1.5-Flash-002 (Google, ) | 44.3 | 42.0 | 42.3 | 33.7 | 43.7 | 49.0 |
| GPT-4o mini (OpenAI, ) | 46.3 | 37.0 | 24.7 | 33.6 | 43.1 | 45.5 |
| Qwen2-VL-72B (Alibaba, ) | 49.2 | 45.2 | 36.7 | 31.0 | 54.7 | 49.9 |
| InternVL2-Llama3-76B (Chen et al., ) | 41.5 | 31.5 | 24.4 | 20.3 | 34.8 | 39.6 |
| LLaVA-OneVision-72B (Li et al., ) | 34.8 | 34.2 | 25.0 | 20.7 | 28.1 | 44.5 |
| NVLM-72B (Dai et al., ) | 36.8 | 23.3 | 3.8 | 0.0 | 0.0 | 0.0 |
| Qwen2-VL-7B (Alibaba, ) | 37.7 | 33.0 | 26.4 | 19.4 | 37.5 | 41.1 |
| Pixtral 12B (Mistral, ) | 37.1 | 31.0 | 25.8 | 19.7 | 16.6 | 41.0 |
| Aria-MoE-25B (Li et al., ) | 35.8 | 27.3 | 19.8 | 21.1 | 27.1 | 42.9 |
| InternVL2-8B (Chen et al., ) | 30.1 | 25.3 | 17.7 | 15.4 | 19.9 | 34.8 |
| Phi-3.5-Vision (Abdin et al., ) | 27.8 | 28.5 | 20.2 | 12.5 | 14.3 | 24.7 |
| MiniCPM-V2.6 (Yao et al., ) | 26.3 | 22.3 | 17.9 | 14.0 | 23.6 | 35.3 |
| LLaVA-OneVision-7B (Li et al., ) | 25.5 | 24.1 | 17.8 | 14.8 | 13.8 | 31.0 |
| Qwen2-VL-2B (Alibaba, ) | 25.0 | 21.3 | 17.4 | 7.7 | 10.5 | 25.2 |
| Llama-3.2-11B (Meta, ) | 19.6 | 18.6 | 13.5 | 14.6 | 7.3 | 21.2 |
| Aquila-VL-2B-llava-qwen (Gu et al., ) | 18.2 | 23.3 | 19.0 | 11.1 | 1.2 | 21.4 |
| InternVL2-2B (Chen et al., ) | 15.2 | 15.8 | 17.7 | 3.7 | 5.8 | 19.0 |
| Idefics3-8B-Llama3 (Laurençon et al., ) | 14.8 | 12.3 | 12.2 | 10.1 | 9.3 | 16.2 |

The abbreviations used in the table above are explained in the following table:

**Table 16: Abbreviation list of keywords in the visual input number dimension.**
| Abbreviation | Input Number |
| --- | --- |
| 1 | 1-image |
| 2I | 2-3 images |
| 4I | 4-5 images |
| 6I | 6-8 images |
| 9OM | 9-image or more |
| V | video |

## Appendix F Detailed Inspection of Model Behaviours on MEGA-Bench

To complement §4.4 of the main paper, this section presents a case study analysis of the error types of different models on different tasks in MEGA-Bench.
We use similar error categories as in MMMU (Yue et al., ) and MMT-Bench (Ying et al., ):

$\bullet$ Perception Error: VLMs fail to recognize or perceive the content of interest in the query image(s). Perception errors indicate the

$\bullet$ Lack of Knowledge: VLMs lack the domain-specific knowledge to answer specialized questions, such as identifying the taxonomic order of an insect.

$\bullet$ Lack of (Reasoning) Capability: VLMs lack the necessary capabilities to solve the task, mainly related to various reasoning abilities, such as logical reasoning, counting, spatial or temporal reasoning, symbolic reasoning for code or various programs, and so on. This is a broad type that covers many errors.
One typical case for this error type is that the models can accurately follow instructions and perceive the visual inputs but struggle with the required reasoning process, leading to incorrect answers.

$\bullet$ Refuse to Answer: VLMs refuse to answer questions that they believe to involve sensitive content.

$\bullet$ Fail to Follow Instructions: VLMs fail to correctly understand instructions and provide wrong answers. The tasks in MEGA-Bench usually have more instructions on the answer format compared to previous benchmarks. A typical error pattern is not comprehending the required format, thus providing answers with incorrect formats or generating irrelevant responses. This error type is much more common in open-source models.

Figure 14 to Figure 33 shows the case study for samples from different tasks. We use distinct colors to highlight the tags in each task sample. We borrow the error case analysis template from MMMU (Yue et al., ) while adding the keywords information of MEGA-Bench. We mainly focus on the flagship proprietary models. The Claude-3.5 in these figures refers to the Claude-3.5-Sonnet (0620) model.

Figure: Figure 14: A sample error case of Coding (subfield: Code Debugging). Source:Web Back to List of Figures
Refer to caption: x18.png

Figure: Figure 15: A sample error case of Coding (subfield: Code Understanding). Source:Web Back to List of Figures
Refer to caption: x19.png

Figure: Figure 16: A sample error case of Coding (subfield: Code Understanding). Source:Web Back to List of Figures
Refer to caption: x20.png

Figure: Figure 17: A sample error case of Information Extraction (subfield: Multimodel QA). Source: MVBench (Li et al., ) and STAR (Wu et al., ) Back to List of Figures
Refer to caption: x21.png

Figure: Figure 18: A sample error case of Information Extraction (subfield: Detailed Manual Understanding).Source:Web Back to List of Figures
Refer to caption: x22.png

Figure: Figure 19: A sample error case of Information Extraction (subfield: Search by Attribute without Calculate).Source:Web Back to List of Figures
Refer to caption: x23.png

Figure: Figure 20: A sample error case of Knowledge (subfield: World Knowledge). Source: BIOSCAN-1M (Gharaee et al., ) Back to List of Figures
Refer to caption: x24.png

Figure: Figure 21: A sample error case of Knowledge (subfield: World Knowledge). Source: Web Back to List of Figures
Refer to caption: x25.png

Figure: Figure 22: A sample error case of Knowledge (subfield: Art). Source: Web Back to List of Figures
Refer to caption: x26.png

Figure: Figure 23: A sample error case of Mathematics (subfield: Graph Theory). Source:Web Back to List of Figures
Refer to caption: x27.png

Figure: Figure 24: A sample error case of Metrics (subfield: Generated Image Eval). Source:Motion Guidance (Geng & Owens, ) Back to List of Figures
Refer to caption: x28.png

Figure: Figure 25: A sample error case of Metrics (subfield: Generated Image Eval). Source: EASI-Tex (Perla et al., ) Back to List of Figures
Refer to caption: x29.png

Figure: Figure 26: A sample error case of Planning: (subfield: Puzzles and Games). Source: Web Back to List of Figures
Refer to caption: x30.png

Figure: Figure 27: A sample error case of Planning (subfield: Puzzles and Games). Source:Web Back to List of Figures
Refer to caption: x31.png

Figure: Figure 28: A sample error case of Planning (subfield: Reordering). Source:Perception Test (Patraucean et al., ) Back to List of Figures
Refer to caption: x32.png

Figure: Figure 29: A sample error case of Perception (subfield: Multimodal Constrained Captioning). Source: Web Back to List of Figures
Refer to caption: x33.png

Figure: Figure 30: A sample error case of Perception (subfield: Visual Recognition). Source:Web Back to List of Figures
Refer to caption: x34.png

Figure: Figure 31: A sample error case of Perception (subfield: Visual Recognition). Source: COCO (Lin et al., ) Back to List of Figures
Refer to caption: x35.png

Figure: Figure 32: A sample error case of Perception (subfield: Visual Recognition). Source: CelebA (Liu et al., ) Back to List of Figures
Refer to caption: x36.png

Figure: Figure 33: A sample error case of Science (subfield: STEM). Source: SciBench (Wang et al., ) Back to List of Figures
Refer to caption: x37.png

## Appendix G Detailed Task Information

In Appendix G, we list data source details for every task in our benchmark. We also list the output format and metrics to help better understand each task’s form.

¿X—¿X—¿X—¿X
Detailed task information description of MEGA-Bench.
Task Name   Source Description   Output Format   Metrics
\endfirsthead Table 16 – continued from previous page
Task Name Source Description   Output Format   Metrics
\endhead\endfoot\endlastfoot Information Extraction
Cheapest Flight Identification Screenshots were taken by the human annotator on [Google Flights](https://www.google.com/travel/flights). Questions and answers were created by the annotator. Contextual Simple String Match
Weather Info Retrieval Screenshots were taken by the human annotator on [Microsoft Weather](https://www.msn.com/en-ca/weather/forecast). Questions and answers were created by the annotator. Contextual String Set Equality Comma
Stock Info Retrieval Screenshots were taken by the human annotator on [Yahoo Finance](https://finance.yahoo.com/). Questions and answers were created by the annotator. Contextual Set Equality
Game Platform Support Identification Screenshots were taken by the human annotator on the [Steam store](https://store.steampowered.com/). Questions and answers were created by the annotator. Structured Exact String Match, Set Equality
Top Rated Hotel Identification Screenshots were taken by the human annotator on [Booking.com](https://www.booking.com/). Questions and answers were created by the annotator. Contextual String Set Equality Comma
Movie Info Retrieval Screenshots were taken by the human annotator on the [Amazon Prime Video webpage](https://www.primevideo.com/). Questions and answers were created by the annotator. Contextual String Set Equality Comma
Top Video Creator Identification Screenshots were taken by the human annotator on [YouTube](https://www.youtube.com). Questions and answers were created by the annotator. Exact Exact String Match
Highest Discount Game Price Identification Screenshots were taken by the human annotator on the [Steam store](https://store.steampowered.com/). Questions and answers were created by the annotator. Numerical Exact String Match
Newspaper Page Parse And Count Data collected from the Newspaper Navigation Dataset (Lee et al., ). Questions and answers were created by the annotator. Exact Exact String Match
Remaining Playback Time Calculation Screenshots were taken by the human annotator on [YouTube](https://www.youtube.com). Questions and answers were created by the annotator. Exact Exact String Match
Multi Lingual Manual Explanation Scooter Spanish Screenshots taken from user manual located at [https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf](https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf). Questions and answers created by human annnotator. Open GPT-4o as Judge
Multi Lingual Manual Explanation Scooter Arabic Screenshots taken from user manual located at [https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf](https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf). Questions and answers created by human annnotator. Open GPT-4o as Judge
Multi Lingual Manual Explanation Scooter French Screenshots taken from user manual located at [https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf](https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf). Questions and answers created by human annnotator. Open GPT-4o as Judge
Multi Lingual Manual Explanation Scooter Chinese Screenshots taken from user manual located at [https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf](https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf). Questions and answers created by human annnotator. Open GPT-4o as Judge
Multi Lingual Manual Explanation Scooter Russian Screenshots taken from user manual located at [https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf](https://fcc.report/FCC-ID/2A33E5LCHG11U/6288539.pdf). Questions and answers created by human annnotator. Open GPT-4o as Judge
Video Summary Videos taken from [WikiHow](https://www.wikihow.com/Main-Page) or [YouTube](https://www.youtube.com/). Questions and answers created by human annnotator. Open GPT-4o as Judge
Video Short Title Videos taken from [YouTube](https://www.youtube.com/). Questions and answers created by human annnotator. Open GPT-4o as Judge
Video2notes [WikiHow](https://www.wikihow.com/Main-Page) or [YouTube](https://www.youtube.com/). Questions and answers created by human annnotator. Open GPT-4o as Judge
Video Content Reasoning Videos and annotations were taken from the HME100k (Yuan et al., ) dataset. Questions and answers were adapted by a human annotator. Contextual Simple String Match
COCO OOD Global Image Retrieval By Query Property Images were from COCO-O (Mao et al., ). Questions and answers were re-designed by the annotator manually Structured Jaccard Index
Places365 Similar Scene Retrieval Images and labels were taken from the Places365 dataset (Zhou et al., ) and adapted into questions and answers by a human annotator. MC Exact String Match
Booking Web Recommendation Images and labels come from the SEED-Bench (Li et al., ) dataset. Some images are from [Yelp](https://www.yelp.com/). Questions and annotations were adapted by a human annotator. Contextual Jaccard Index Case Insensitive
Game Info Retrieval Screenshots were taken by the human annotator on the [Epic Games Store](https://store.epicgames.com/). Questions and answers were created by the annotator. Contextual String Set Equality Comma
Media Homepage Profile Most images and labels come from the SEED-Bench (Li et al., ) dataset, while one came from a screenshot taken by a human annotator. Questions and annotations were adapted by a human annotator. Structured Jaccard Index Case Insensitive
Movie Retrieval By Actor Screenshots were taken by the human annotator on the [Amazon Prime Video webpage](https://www.primevideo.com/). Questions and answers were created by the annotator. Contextual String Set Equality Comma
Music Info Retrieval Screenshots were taken by the human annotator on the [Spotify Web Player](https://open.spotify.com/). Questions and answers were created by the annotator. Contextual String Set Equality Comma
Tv Show Retrieval By Character Screenshots were taken by the human annotator on the [Amazon Prime Video webpage](https://www.primevideo.com/). Questions and answers were created by the annotator. Contextual String Set Equality Comma
App Layout Understanding Leetcode Screenshots were taken by the human annotator on [Leetcode](https://leetcode.com/). Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Youtube Screenshots were taken by the human annotator on [YouTube](https://www.youtube.com/). Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Amazon Screenshots were taken by the human annotator on [Amazon](https://www.primevideo.com/). Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Word Screenshots were taken by the human annotator on Microsoft Word. Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Notes Screenshots were taken by the human annotator on the Google Notes app. Questions and answers were created by the annotator. Exact Exact Str Match Case Insensitive
App Layout Understanding Ppt Screenshots were taken by the human annotator on Microsoft PowerPoint. Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Alipay Screenshots were taken by the human annotator on the Alipay app. Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Instagram Screenshots were taken by the human annotator on the Instagram app. Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Zoom Screenshots were taken by the human annotator on [Zoom](https://zoom.us). Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Excel Screenshots were taken by the human annotator on Microsoft Excel. Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Iphone Settings Screenshots were taken by the human annotator on the iPhone. Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Tiktok Screenshots were taken by the human annotator on the TikTok app. Questions and answers were created by the annotator. Exact Exact String Match
App Layout Understanding Twitter Screenshots were taken by the human annotator on the X (formerly Twitter) app. Questions and answers were created by the annotator. Exact Exact String Match
Multilingual News Qa Screenshots were taken by the human annotator on [X](https://x.com/) (formerly Twitter). Questions and answers were created by the annotator. Contextual Multi Ref Phrase
Product Ocr Qa Images were taken from various websites. Questions and answers were created by the annotator. Exact Exact String Match
Research Website Parsing Blogpost Screenshots were taken of various ML research websites. Questions and answers were created by the annotator. Contextual Multi Ref Phrase
Research Website Parsing Homepage Screenshots were taken of various ML research websites. Questions and answers were created by the annotator. Contextual Multi Ref Phrase
Research Website Parsing Publication Screenshots were taken of various ML research websites. Questions and answers were created by the annotator. Contextual Multi Ref Phrase
Gui Chat Easy Images and annotations were adapted from the GUI Chat dataset (Chen et al., ) by the human annotator into an open-ended question. Open GPT-4o as Judge
Gui Chat Hard Images and annotations were adapted from the GUI Chat dataset (Chen et al., ) by the human annotator into an open-ended question. Open GPT-4o as Judge
Realworld Qa En2cn Images and annotations were adapted from the RealWorldQA benchmark (xAI, ) by the human annotator into an open-ended question. The translation requirement was added by the human annotator. Contextual Multi Ref Phrase
Star Object Interaction Video Videos and annotations were adapted from the STAR benchmark (Wu et al., ) by the human annotator into questions and answers. Contextual Multi Ref Phrase
Funqa Unexpected Action Magic Video Videos and annotations were adapted from the FunQA benchmark (Xie et al., ) by the human annotator into being an open-ended question. Open GPT-4o as Judge
Activitynetqa Images and annotations were adapted from the ActivityNetQA benchmark (Yu et al., ) by the human annotator into being an open-ended question. Open GPT-4o as Judge
Funqa Unexpected Action Creative Video Videos and annotations were adapted from the FunQA benchmark (Xie et al., ) by the human annotator into being an open-ended question. Open GPT-4o as Judge
Nextqa Mc Images and annotations were adapted from the NExTQA benchmark (Xiao et al., ) by the human annotator into questions and answers. MC Exact String Match
Video Qa Videos taken from [YouTube](https://www.youtube.com/). Questions and answers created by human annnotator. Open GPT-4o as Judge
Nextqa Oe Images and annotations were adapted from the NExTQA benchmark (Xiao et al., ) by the human annotator into being an open-ended question. Open GPT-4o as Judge
Funqa Unexpected Action Humor Video Videos and annotations were adapted from the FunQA benchmark (Xie et al., ) by the human annotator into being an open-ended question. Open GPT-4o as Judge
Multilingual Movie Info Parsing Screenshots were taken by the human annotator on the [Amazon Prime Video webpage](https://www.primevideo.com/). Questions and answers were created by the annotator. Structured Exact String Match, Simple String Match
Movie Info Parsing Screenshots were taken by the human annotator on the [Amazon Prime Video webpage](https://www.primevideo.com/). Questions and answers were created by the annotator. Structured Exact String Match
Stock Info Parsing Screenshots were taken by the human annotator on [Yahoo Finance](https://finance.yahoo.com/). Questions and answers were created by the annotator. Structured Exact String Match
Music Info Parsing Screenshots were taken by the human annotator on the [Spotify Web Player](https://open.spotify.com/). Questions and answers were created by the annotator. Structured Exact String Match
Multilingual Game Info Parsing Screenshots were taken by the human annotator on the [Epic Games Store](https://store.epicgames.com/). Questions and answers were created by the annotator. Structured Exact String Match
Ocr Article Authors Screenshots taken of various academic papers. Questions and answers created by human annotator. Structured Simple String Match
Youtube Video Info Parsing Videos taken from [YouTube](https://www.youtube.com/). Questions and answers created by human annnotator. Structured Exact String Match
Tv Show Info Parsing Screenshots were taken by the human annotator on the [Amazon Prime Video webpage](https://www.primevideo.com/). Questions and answers were created by the annotator. Structured Simple String Match
Ocr Resume School Plain Resumes taken from various personal websites. Questions and answers were created by the annotator. Contextual String Set Equality Line Break
Image Translation En2cn Images were collected from various sources, including academic papers, news articles, shopping receipts, etc. The annotations are obtained by GPT-4o translation followed by a human check. Contextual Gleu Cn
Booking Web Rating Images and labels come from the SEED-Bench (Li et al., ) dataset. Some images are from [Yelp](https://www.yelp.com/). Questions and annotations were adapted by a human annotator. Structured Exact String Match
Weather Info Parsing Images were collected from the Microsoft Weather by taking screenshots. Questions and answers were designed by the annotator. Structured Exact String Match
Game Info Parsing Screenshots were taken by the human annotator on the [Epic Games Store](https://store.epicgames.com/). Questions and answers were created by the annotator. Structured Exact String Match
Weather Map Climate Type Temperature Parsing One of the examples comes from the SEED-Bench 2 Plus benchmark  (Li et al., ). The rest of the images were collected from various online websites. Questions and annotations were adapted by a human annotator. Structured Exact String Match
Hotel Booking Confirmation Parsing Screenshots were taken by the human annotator on [Booking.com](https://www.booking.com/). Questions and answers were created by the annotator. Structured Exact String Match
Entertainment Web Game Style Some of the examples come from the SEED-Bench 2 Plus benchmark  (Li et al., ). The rest of the screenshots were taken on the [Steam store](https://store.steampowered.com/). Questions and annotations were adapted by a human annotator. Structured Exact Str Match Case Insensitive, Exact String Match
Planning
Wikihow Complex Task Completion Data collected from website, and the questions and answers are designed by human annotator Open GPT-4o as Judge
Vln Identify Robot Data collected from RxR dataset (Ku et al., ), the question and answer are adapted to select the robot that should execute the instruction Exact Exact String Match
Vln English Next Step Data collected from RxR dataset (Ku et al., ), the question and answer are adapted by human annotator Contextual Simple String Match
Vlnqa Egocentric Navigation Video Data collected from VLN-CE (Krantz et al., ) and the task is adapted from MVBench (Li et al., ), the question and answer are adapted by human annotator Contextual Simple String Match
Vln Identify Location Data collected from RxR dataset (Ku et al., ), the question and answer are adapted by human annotator Structured Exact String Match
Vln Tegulu Next Step Data collected from RxR dataset (Ku et al., ), the question and answer are adapted by human annotator Structured Simple String Match
Vln Hindi Next Step Data collected from RxR dataset (Ku et al., ), the question and answer are adapted by human annotator Contextual Simple String Match
App Interactive Operations Instagram Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
App Interactive Operations Leetcode Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
Gui Act Web Multi Data collected from webpage screenshots by human annotator, and the questions and answers bounding boxes are annotated by human annotator Structured Exact String Match, Xml Nbbox Iou Single
App Interactive Operations Ppt Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
Gui Act Mobile Swipe Data collected from application screenshots by human annotator, and the questions and answers bounding boxes are annotated by human annotator Structured Xml Norm Point Distance
App Interactive Operations Excel Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
Gui Act Mobile Tap Data collected from application screenshots by human annotator, and the questions and answers bounding boxes are annotated by human annotator Numerical Xml Norm Point In Bbox
App Interactive Operations Alipay Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
Gui Act Web Single Data collected from webpage screenshots by human annotator, and the questions and answers bounding boxes are annotated by human annotator Numerical Xml Nbbox Iou Single
App Interactive Operations Twitter Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
App Interactive Operations Word Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
App Interactive Operations Iphone Settings Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
App Interactive Operations Tiktok Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
App Interactive Operations Notes Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
App Interactive Operations Zoom Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
App Interactive Operations Amazon Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
Web Action Grounding Data collected from VisualWebBench (Liu et al., ), and the questions and answers are adapted by human annotator MC Exact String Match
App Interactive Operations Youtube Data collected from application screenshots by human annotator, and the questions and answers are designed by human annotator MC Exact String Match
Calendar Schedule Suggestion Data collected from Google Calendar by human annotator, and the questions and answers are designed by human annotator to identify all possible starting times for a meeting within a specified time range and duration Contextual Set Equality
Planning Visual Barman Data collected from [Planning Domain Definition Language (PDDL) of Barman](http://picat-lang.org/ipc14/barman.pddl), and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Visual Floortile Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Visual Storage Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Screenshot Grippers Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Visual Blocksworld Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Screenshot Barman Data collected from [Planning Domain Definition Language (PDDL) of Barman](http://picat-lang.org/ipc14/barman.pddl), and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Screenshot Termes Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Screenshot Floortile Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Screenshot Blocksworld Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Screenshot Storage Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Visual Termes Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Screenshot Tyreworld Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Planning Visual Grippers Data collected from website, and the questions and answers are adapted to match the transitions from init state to goal state Structured Symbolic Planning Test
Logical Reasoning Find Odd One Out Data collected from [website](https://brightside.me/articles/test-try-to-find-the-odd-one-out-in-less-than-10-seconds-801866/), and the questions and answers are adapted to match strings Structured Dict Equality, Exact String Match
Logical Reasoning Fit Pattern Data collected from LogicVista (Xiao et al., ), and the questions and answers are adapted by human annotator MC Exact String Match
Perception-Test Object Shuffle Video Data collected from VLN-CE (Krantz et al., ) and the task is adapted from MVBench (Li et al., ), the question and answer are adapted into single choice by human annotator MC Simple String Match
Chess Puzzles Checkmate Data collected from [Lichess](Lichess.org), and the questions and answers are adapted to match strings Structured Set Equality
Chess Puzzles Equality Data collected from [Lichess](Lichess.org), and the questions and answers are adapted to match strings Structured Set Equality
Bridge Strategies Expert Data and answer are collected from Bridge Master 2000 Open GPT-4o as Judge
Chess Puzzles Crushing Data collected from [Lichess](Lichess.org), and the questions and answers are adapted to match strings Exact Exact String Match
Chess Puzzle Single Step Data collected from [Lichess](Lichess.org), and the questions and answers are adapted to match strings Exact Exact String Match
Chess Find Legal Moves Data collected from game positions of games in the 2024 FIDE Candidates tournament, and the questions and answers are adapted to match strings Exact Chess Move List Jaccard Index, Exact String Match
Bridge Strategies Advanced Data and answer are collected from Bridge Master 2000 Open GPT-4o as Judge
Chess Winner Identification Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator Exact Exact String Match
Bridge Strategies Worldclass Data and answer are collected from Bridge Master 2000 Open GPT-4o as Judge
Mahjong Data collected from website and screenshot of MajSoul, and the answer are annotated by human annotator Exact Exact String Match
Chess Sygyzy Endgames Endgames created by human annotator and data collected from [https://syzygy-tables.info](https://syzygy-tables.info/), and the questions and answers are adapted to match Jaccard index Exact Chess Move List Jaccard Index, Exact String Match
Go Capture Stone Data collected from [https://online-go.com/learn-to-play-go/capture](https://online-go.com/learn-to-play-go/capture) and [https://forums.online-go.com/t/capture-go-problems/31531/9](https://forums.online-go.com/t/capture-go-problems/31531/9), and the questions and answers are adapted to match strings Exact Exact String Match
Bongard Problem Data collected from [https://www.oebp.org/welcome.php](https://www.oebp.org/welcome.php) and [https://www.foundalis.com/res/bps/bpidx.htm](https://www.foundalis.com/res/bps/bpidx.htm) Contextual String Set Equality Comma
Number Puzzle Kakuro 5x5 Data collected from [https://krazydad.com/kakuro/](https://krazydad.com/kakuro/), and the questions and answers are adapted to match strings Exact Exact String Match
Mensa Iq Test Data collected from website, and the questions and answers are adapted to match dict equality Structured Dict Equality
Arc Agi Data collected from [https://arcprize.org/play](https://arcprize.org/play) and the task is adapted from Intelligence (Chollet, ), the question and answer are adapted into a grid of digits by human annotator Exact Exact String Match
Mnist Pattern Data collected from MNIST (Deng, ), and the questions and answers are adapted to match strings Numerical Exact String Match
Number Puzzle Sudoku Data collected from [puzzles.ca](https://www.puzzles.ca/sudoku/), and the questions and answers are adapted to match strings Contextual Simple String Match
Move Pos To Pos Hanoi 4 Pole Shortest paths derived from a diagram found on website and the questions and answers are created to match strings and the longest common move prefix Structured Exact String Match, Longest Common List Prefix Ratio
Pictionary Cartoon Drawing Guess Data collected from An early evaluation of gpt-4v (ision) (Wu et al., ), the question and answer are adapted to match strings by human annotator Exact Exact Str Match Case Insensitive
Pictionary Chinese Food Img2en Data collected from website, and the questions and answers are adapted to match strings Exact Exact Str Match Case Insensitive
Pictionary Doodle Guess Data collected from [website](https://quickdraw.withgoogle.com/data), and the questions and answers are adapted to match strings Exact Exact String Match
Pictionary Skribbl Io Data collected from screenshots collected by human annotator on [skribbl.io](https://skribbl.io/) and the questions and answers are adapted to match strings Exact Exact Str Match Case Insensitive
Pictionary Genai Output Chinese Data collected from screenshot of website, and the questions and answers are adapted to match strings Exact Exact String Match
Annoying Word Search Data collected from various websites, and the answers are annotated by human annotator Contextual Dict Jaccard Agg Jaccard
Logical Reasoning 2d Views Of 3d Shapes Data collected from website, and the questions and answers are adapted to match strings Structured Dict Equality
Maze 2d 8x8 Data generated from [https://www.mazegenerator.net/](https://www.mazegenerator.net/), and the questions and answers are adapted to match strings Exact Exact Str Match Case Insensitive
Crossword Mini 5x5 Data collected from website, and the questions and answers are adapted to match strings Structured Dict Exact Str Match Agg Recall
Rebus Data collected from website, and the questions and answers are adapted to match strings Contextual Simple String Match
Icon Arithmetic Puzzle Data collected from An early evaluation of gpt-4v (ision) (Wu et al., ), the question and answer are adapted to match strings by human annotator Structured Exact String Match, Sequence Equality
Iq Test Open Ended Data and answers are collected from website Open GPT-4o as Judge
Ball Cup Swap 3 Screenshots taken from video and edited together using images found online, and the questions and answers are adapted to match strings MC Exact String Match
Logical Reasoning 2d Folding Data collected from website, and the questions and answers are adapted to match strings MC Exact String Match
Perception Test Video Character Order Data collected from Perception Test (Patraucean et al., ) and the task is adapted from MVBench (Li et al., ), the question and answer are adapted into single answer string by human annotator Contextual Simple String Match
Comic Page Ordering Data collected from website Contextual Sequence Equality
Recipe Image Ordering Data collected from website MC Sequence Equality
Coding
Code Translation Easy Data and test cases are collected from [Pintia](https://pintia.cn) Structured Program Judge
Code Translation Python Data collected from xCodeEval split (Khan et al., ), and test cases are annotated by human Structured Program Judge
Code Translation Hard Data and test cases are collected from [Pintia](https://pintia.cn) Structured Program Judge
Code Translation Advanced Data and test cases are collected from [Pintia](https://pintia.cn) Structured Program Judge
Symbolic Graphics Programs Computer Aided Design Data and answer are collected from SGP-Bench (Qiu et al., ) Contextual Multi Ref Phrase
Symbolic Graphics Programs Scalable Vector Graphics Data and answer are collected from SGP-Bench (Qiu et al., ) Contextual Multi Ref Phrase
Webpage Code Understanding Data are collected from website, and the question and answer are adapted for string match MC Exact String Match
Code Add Tag Data collected from xCodeEval  (Khan et al., ), the question and answer are adapted to match code tag Contextual Set Equality
Media Recommend Solutions Stackoverflow Data are collected from Stack Overflow Website, and the question and answer are adapted to match string MC Exact String Match
Flowchart Code Generation Data are collected from website, and the question and answer are designed by human annotator MC Exact String Match
Code Solution Compare Data collected from SGP-Bench (Qiu et al., ), and the question and answer are adapted for string match Exact Exact String Match
Code Match Problem Data collected from SGP-Bench (Qiu et al., ), and the question and answer are adapted to match code Exact Exact String Match
Code Visualization Output Understanding Data are collected from website, and the question and answer are designed by human annotator MC String Set Equality Comma
Code Output Result Data are collected from [SanFoundry MCQs](www.sanfoundry.com), and the question and answer are designed by human annotator Exact Code Result Exact Str Match
Code Execution Data collected from execution-v2 (Jain et al., ), the question and answer are adapted to match string Contextual Simple String Match
Code Retrieval Data collected from SGP-Bench (Qiu et al., ), and the question and answer are adapted to match string Exact Exact String Match
Table2latex Complex Data collected from SGP-Bench (Qiu et al., ), and the question and answer are adapted for LLM Judge Structured GPT-4o as Judge
Ocr Table To Html Data are collected from website, and the question and answer are designed by human annotator Structured Simple String Match
Ocr Table To Markdown Data are collected from website, and the question and answer are designed by human annotator Structured Simple String Match
Ocr Math Text Latex Data are collected from website, and the question and answer are designed by human annotator to match text with LaTeX Contextual Text With Latex Expr Equality
Ocr Math Equation Data are collected from website, and the question and answer are designed by human annotator to match LaTeX Contextual Latex Expr Equality
Latex Complex Formula Convertion Data are collected from [latex-formulas](https://huggingface.co/datasets/OleehyO/latex-formulas) and [TexTeller](https://github.com/OleehyO/TexTeller?tab=readme-ov-file), and the question and answer are designed by human annotator Structured Latex Expr Equality
Ocr Table To Latex Data are collected from website, and the question and answer are designed by human annotator Structured Simple String Match
Ocr Table To Csv Data are collected from website, and the question and answer are designed by human annotator Structured Simple String Match
Code Programming Test Easy Data and test cases are collected from [Pintia](https://pintia.cn) Structured Program Judge
Code Programming Test Hard Data and test cases are collected from [Pintia](https://pintia.cn) Structured Program Judge
Code Programming Test Advanced Data and test cases are collected from [Pintia](https://pintia.cn) Structured Program Judge
Code Programming Extremely Hard Data and test cases are collected from [Pintia](https://pintia.cn) Structured Program Judge
Visualization With Code Data are collected from website, and the question and answer are designed by human annotator Structured GPT-4o as Judge
Stackoverflow Debug Qa Data are collected from Stack Overflow Website, and the question and answer are adapted to match string Structured Exact Str Match Case Insensitive, Exact String Match
Code Error Line Identification Data collected from [Pintia](https://pintia.cn), and the question and answer are adapted to match string MC Exact String Match
Perception
Visual Correspondence In Two Images Images are from BLINK (Fu et al., ). Annotator manually added one more reference point per sample and designed structured answers Structured Dict Equality
2D Image Jigsaw Puzzle Easy Images created by playing the online Jigsaw simulator and taking screenshots Structured Dict Exact Str Match Agg Recall
Adapted Cvbench Distance Data collected from CV-Bench’s distance split (Tong et al., ), and adapted into exact text answer Exact Exact String Match
Geometry Plot Position Relationship Data collected from Internet. Question and answers were designed by the annotator Exact Exact String Match
Video Grounding Spatial Videos collected from VidOR (Shang et al., ). Re-designed questions and answers for this specific task Contextual Simple String Match
Adapted Cvbench Relation Data collected from CV-Bench’s relation split (Tong et al., ), and adapted into exact text answer Exact Exact String Match
Egocentric Spatial Reasoning Data are collected from Epic-Kitchen (Damen et al., ) and the Internet. Questions and answers are adapted for contextual formatted output Contextual Multi Ref Phrase
Trance Physics Reasoning Basic Data are collected from Trance (Hong et al., ) by specifically picking up samples with the easiest settings. Questions and answers are re-designed for this specific task Exact Exact String Match
CLEVER Moving Direction Video Video data are collected from MVBench (Li et al., ). Questions and answers are adapted for the contextual formatted output format Contextual Multi Ref Phrase
Trance Physics Reasoning Event Data are collected from Trance (Hong et al., ) by selecting settings where objects are moved. Questions and answers are re-designed for indicating changed objects MC Set Equality
3D Fragments Understanding We write rendering scripts to produce the data from the assets of the Break Bad dataset (Sellán et al., ) Numerical Simple String Match
Physical Property Reasoning Images are collected from the Internet, questions and answers are designed by annotator Contextual Simple String Match
ClEVRER Physics Images are collected from CLEVRER (Yi et al., ), questions and answers are re-designed for testing the understanding of physical status Numerical Exact String Match
ClEVRER Video Moving Object Property Recognition The videos are collected from MVBench (Li et al., ), the questions and answers are adapted to test the understanding of physical property and dynamics Contextual Multi Ref Phrase
Trance Physics Reasoning View Data are collected from Trance (Hong et al., ) by selecting the most challenging settings (objects are moved, and two states are captured by different cameras). Questions and answers are re-designed for indicating changed objects MC Set Equality
Photoshop Operation Images are collected from the Web, questions and answers designed by annotator Structured Jaccard Index
Relative Reflectance Of Different Regions Images come from BLINK (Fu et al., ), the annotator added one more point per image and converted the task into a reflectance sorting task Structured Sequence Equality
Autonomous Driving Scene Analysis Images are collected from the Internet, questions and answers are designed by annotator Exact Exact Str Match Case Insensitive
Functionality Matching In Different Objects The images come from BLINK (Fu et al., ). The annotator manually added one ref point per image to augment the task Structured Dict Equality
NLVR2 Two Image Compare QA Images are collected from NLVR2 (Suhr & Artzi, ). Questions and answers re-designed by the annotator MC Multi Ref Phrase
Egocentric Analysis Single Image The images are collected from Epic-Kitchens (Damen et al., ). Questions and answers are re-designed by the annotator Exact Exact String Match Case Insensitive
ClEVR Object Existence Video Videos are collected from MVBench (Li et al., ). Questions and answers are slightly adapted MC Simple String Match
SNLI-VE Visual Entailment Data are collected and converted from SNLI-VE dataset (Xie et al., ) Exact Exact String Match
OCR Open-ended QA Images collected from the Internet. Questions and answers made up by the annotator for the open-ended output format Open GPT-4o as Judge
Super ClEVR Scene Understanding Images are collected from SuperCLEVR (Li et al., ). Questions and answers are re-designed by the annotator Contextual Multi Ref Phrase
Visual Dialog Image Guessing Images are collected from Visual Dialog dataset (Das et al., ). Questions and answers are designed by the annotator MC Exact String Match
Semantic Matching Of Two Images Images come from BLINK dataset (Fu et al., ). The annotator augmented the data by adding one more ref point and re-designed the answer Structured Dict Equality
Recover Masked Word In Figure The annotator took screenshots from a few public papers on arXiv and designed the question-answer pairs Contextual Simple String Match
Graph Interpretation The images of line/dot graphs are collected from the Internet, and the annotator created the question and open-ended reference answer Open GPT-4o as Judge
Science Figure Explanation The images of science figures are collected from the Internet, and the annotator created the question and open-ended reference answer Open GPT-4o as Judge
Bar Chart Interpretation The images of bar graphs are collected from the Internet, and the annotator created the question and open-ended reference answer Open GPT-4o as Judge
Electricity Load Estimate Plot The temporal data were collected from Informer (Zhou et al., ) and AutoFormer (Wu et al., ). The annotator re-processed the data to design a more specific task Numerical Normalized RMSE
Average Humidity Estimate Plot The temporal data were collected from AutoFormer (Wu et al., ). The annotator re-processed the data to design a more specific task Numerical Normalized RMSE
Exchange Rate Estimate Plot The temporal data were collected from Lai et al. () and AutoFormer (Wu et al., ). The annotator re-processed the data to design a more specific task Numerical Normalized Rmse
Road Map Find Highway Between Two Place The road map images were collected from Seed-Bencn (Li et al., ) and the Internet. Questions and answers are designed by the annotator Exact Exact String Match
Transit Map Intersection Points The transit map images were collected from Seed-Bencn (Li et al., ) and the Internet. Questions and answers are designed by the annotator Structured Exact String Match, Sequence Equality Case Insensitive
Panel Images Single Question Panel images were collected from  (Fan et al., ). Questions and answers were designed by the annotator MC Exact String Match
Knowledge Graph Understanding The large knowledge graph image was collected from the Internet. Questions and answers were designed by the annotator Contextual Set Equality
Panel Images Multi Question Panel images were collected from  (Fan et al., ). Questions and answers were designed by the annotator Structured Exact String Match
Mindmap Elements Parsing Mindmap images were collected from Seed-Bencn (Li et al., ) and the Internet. Questions and answers are designed by the annotator Structured Set Equality Case Insensitive
Dvqa Images were collected from Dvqa dataset (Kafle et al., ). Questions and answers were re-designed by the annotator Numerical Multi Ref Phrase
Figureqa Images were collected from FigureQA dataset (Kahou et al., ). Questions and answers were re-designed by the annotator MC Multi Ref Phrase
Map Diagram Qa Images were collected from MapQA dataset (Chang et al., ). Questions and answers were re-designed by the annotator Contextual Simple String Match
Chart Vqa Data were collected from MathVista (Lu et al., ) (statistics subset) and converted into a more specific task Numerical General Single Numerical Match
Photo Sharing Image Retrieval Images were from the PhotoChat (Zang et al., ) dataset. Questions and answers are designed by the annotator MC Exact String Match
Multi Load Type Prediction From Plot The temporal data were collected from Informer (Zhou et al., ) and AutoFormer (Wu et al., ). The annotator re-processed the data to design a more specific task MC Sequence Accuracy Case Insensitive
Stock Price Future Prediction The annotator downloaded data from Yahoo! Finance’s API, and processed data to design this task Contextual Normalized Rmse
Traffic Future Prediction From Line Plot The temporal data were collected from AutoFormer (Wu et al., ). The annotator re-processed the data to design a more specific task Numerical Normalized Rmse
Electricity Plot Future Prediction The temporal data were collected from AutoFormer (Wu et al., ). The annotator re-processed the data to design a more specific task Numerical Normalized Rmse
Ili Ratio Future Prediction The temporal data were collected from AutoFormer (Wu et al., ). The annotator re-processed the data to design a more specific task Numerical Normalized Rmse
Paper Vqa The annotator took high-resolution screenshots of a few papers on arXiv, and designed the questions and answers Contextual Simple String Match
Doc Vqa Data and open-ended QA pairs were converted from DocMatix (HuggingFaceM4, ) Open GPT-4o as Judge
FunSD Document Qa Images were collected from FunSD (Jaume et al., ). Questions and answers were designed by annotator Contextual Simple String Match
OCR Article Journal The article screenshots were taken from various websites. Questions and answers were created by the annotator Contextual Simple String Match
IAM Line Ocr And Locate Images were collected from the IAM handwritten database (Marti & Bunke, ). Questions and answers were re-designed by the annotator Structured Exact String Match, Normalized Similarity Damerau Levenshtein
OCR Resume Experience Plain The resume screenshots were taken from various websites. Questions and answers were created by the annotator Contextual String Set Equality Line Break
Newspaper Ocr In Query Box Images were collected from The Newspaper Navigator Dataset (Lee et al., ). Questions and answers were adapted by the annotator into simple string answer format. Contextual Simple String Match
OCR Resume Skill Plain The article screenshots were taken from various websites. Questions and answers were created by the annotator Contextual String Set Equality Line Break
OCR Resume Employer Plain The article screenshots were taken from various websites. Questions and answers were created by the annotator Contextual String Set Equality Line Break
Finance Table Understanding Images were collected from MMMU (Yue et al., ). Questions and answers were adapted by the annotator into direct numerical output format Numerical Exact String Match
Monthly Weather Days Count Images were collected from the Microsoft Weather by taking screenshots. Questions and answers were designed by the annotator. Structured Exact String Match
Table Understanding Complex Question Answering Tables were collected from WikiTableQuestions (Pasupat & Liang, ) and TabFact (Chen et al., ). Questions and answers were designed by the annotator Contextual Simple String Match
Table Understanding Fetaqa Data were collected and converted from FetaQA (Nan et al., ) Open GPT-4o as Judge
Table Understanding Fact Verification Tables were collected from WikiTableQuestions (Pasupat & Liang, ) and TabFact (Chen et al., ). Questions and answers were designed by the annotator Contextual Dict Precision
Electricity Future Prediction From Table The temporal data were collected from AutoFormer (Wu et al., ). The annotator re-processed the data to design a more specific task Numerical Normalized Rmse
Video Detail Description Video and description data were collected from VideoDetailCaption (Maaz et al., ) and converted into a specific task Open GPT-4o as Judge
Guess Image Generation Prompt Examples were collected from various online text-to-image generation demos Open GPT-4o as Judge
Docci Image Description Long Data were collected from DOCCI (Onoe et al., ) Open GPT-4o as Judge
Tweets Captioning The annotator collected the data from X by taking screenshots and and the texts Open GPT-4o as Judge
Image Captioning With Additional Requirements Images were collected from various sources on the Web. The annotator used Claude 3.5 Sonnet to generate reference answers and manually polished them Open GPT-4o as Judge
Ad Count Detection Image were collected from various websites by taking screenshots. Questions and answers created by the annotator Numerical Exact String Match
Adapted Cvbench Count Data were collected from CV-Bench’s counting split (Tong et al., ) and adapted into a specific task by rewriting the question-answer pairs Numerical Exact String Match
Av Vehicle Multiview Counting Images were collected from the nuScenes (Caesar et al., ) dataset. The annotator designed the questions and implemented a script to generate the answers from the raw annotation Numerical Exact String Match
Counting Multi Image Data were collected from Mantis (Jiang et al., ) and adapted into direct numerical answer Numerical Exact String Match
Av Human Multiview Counting Images were collected from the nuScenes (Caesar et al., ) dataset. The annotator designed the questions and implemented a script to generate the answers from the raw annotation Numerical Exact String Match
Shape Composition Shapes Images were made by the annotator using Canva. Questions and answers were created by the annotator Structured Positive Int Match
Counting Single Image Data were collected from Mantis (Jiang et al., ) and adapted into direct numerical answer Numerical Exact String Match
CLEVRER Video Moving Object Count Video data are collected from MVBench (Li et al., ). Questions and answers are adapted for the direct numerical output Numerical Exact String Match
Shape Composition Colours Images were created by the annotator using Canva. Questions and answers were created by the annotator Structured Positive Int Match
Face Identity Matching Images were collected from CelebA (Liu et al., ). Questions and answers re-designed by the annotator for this specific task Numerical Set Equality
Rocks Samples Identify Images, questions, and answers were collected from the Web by the annotator Contextual Simple String Match
Animal Pose Estimation Images were collected from AP-10K (Yu et al., ). The annotator implemented a script to produce the answer from raw annotations for this task Numerical Sequence Coords Similarity
License Plate Recognition Images were collected from the Web. Questions and answers were created by the annotator Exact Exact Str Match Case Insensitive
Image Style Recognition Images were collected from the Web. Questions and answers were created by the annotator Exact Exact Str Match Case Insensitive
Long String Letter Recognition Data were designed by the annotator and generated automatically with code Exact Exact String Match
COCO Object Detection By Query Property Images were from MS-COCO (Lin et al., ). Questions and answers were re-designed by the annotator and adapted manually Numerical Exact String Match, Nbbox Iou Tuple
Widerface Face Count And Event Classification Images were collected from WiderFace (Yang et al., ). Questions and answers were designed and produced by the annotator Structured Exact String Match, Simple String Match
Handwritten Math Expression Extraction Data were collected from HME100K (Yuan et al., ) Contextual Latex Expr Equality
Geometry Reasoning Circled Letter Image were collected from Rahmanzadehgervi et al. () are manually created. Questions and answers were re-designed by the annotator Structured Exact String Match, Sequence Equality
Av Multicamera Tracking Predict Bbox Images were collected from the nuScenes (Caesar et al., ) dataset. The annotator designed the questions and implemented a script to generate the answers from the raw annotation Numerical Nbbox Iou Sequence
ASCII Art Understanding Data and annotations were collected and created by the annotator from various online resources MC Exact String Match
Face Keypoint Detection Raw data were from CelebA (Liu et al., ). The annotator wrote a script to produce the answers for this task Structured Sequence Coords Similarity
Extract Webpage Headline Images were collected from VisualWebBench (Liu et al., ). Questions and answers were adapted by the annotator Contextual Simple String Match
Waldo Images and annotations were collected and created by the annotator using various resources on the Web Structured Dict Nbbox Iou Tuple Agg Jaccard
Geographic Remote Sensing Land Cover Images and annotations were collected and converted from SATIN (Roberts et al., ) Contextual Sequence Equality
Signboard Identification Images were collected from the Internet. The annotator created the question-answer pairs Contextual Simple String Match
Long String Number Recognition Data were designed by the annotator and generated automatically with code Exact Exact String Match
Waybill Number Sequence Extraction Images were collected from the Internet. The annotator created the question-answer pairs Contextual Simple String Match
Single Person Pose Estimation hello, this is Source Description Structured Sequence Coords Similarity
COCO Person Detection Images were from MS-COCO (Lin et al., ). Questions and answers were re-designed by the annotator and adapted with a script Numerical Exact String Match, Nbbox Iou Tuple
Places365 Scene Type Classification Images were collected from Places365 (Zhou et al., ). Questions and answers were re-designed and generated by the annotator Exact Exact String Match
Visual Prediction Rater Openable Part Segmentation Images were collected using screenshots from arXiv papers’ qualitative results. Questions and answers were created by the annotator MC Sequence Equality
Visual Prediction Rater Panoptic Segmentation Images were collected using screenshots from qualitative results from the arXiv papers. Questions and answers were created by the annotator MC Sequence Accuracy Case Insensitive
Visual Prediction Rater Semantic Segmentation Images were collected using screenshots from the qualitative results of the arXiv papers. Questions and answers were created by the annotator MC Sequence Accuracy Case Insensitive
Video To Camera Trajectory Retrieval Data were collected from the project page of VD3D (Bahmani et al., ). Questions and answers designed and created by the annotator MC Exact String Match
Sceneqa Scene Transition Video Video data are collected from MVBench (Li et al., ). Questions and answers are adapted by the annotator into open-ended format Open GPT-4o as Judge
Video Segments Reordering Raw data come from UCF101 (Soomro et al., ). The annotator designed the task and re-organized the data to produce the question-answer pairs Structured Sequence Equality
Action Sequence Understanding Data were collected from MileBench (Song et al., ). Questions and answers were designed and created by the annotator Exact Exact String Match
Video Action Recognition Raw data come from UCF101 (Soomro et al., ). The annotator designed the task and re-organized the data to produce the question-answer pairs Structured Exact String Match
Google Streetview Line Sorting The data were taken from Google Maps. Questions and answers were created by the annotator Structured Sequence Equality
Next Action Prediction Data were collected from MileBench (Song et al., ). Questions and answers were designed and created by the annotator MC Exact String Match
Perception Test Video Action Count Video data are collected from MVBench (Li et al., ). Questions and answers are adapted by the annotator into direct numerical output format Numerical Exact String Match
Google Streetview Line Reasoning The data were taken from Google Maps. Questions and answers were created by the annotator MC Simple String Match
Video Camera Motion Description Videos were collected from VidOR (Shang et al., ). Questions and answers re-designed and created by the annotator Exact Exact String Match
Video Grounding Temporal Videos were collected from VidOR (Shang et al., ). Questions and answers re-designed and created by the annotator MC Simple String Match
Web Action Prediction Data were collected from VisualWebBench (Liu et al., ) MC Exact String Match
Cam Traj To Video Selection Data were collected from the project page of VD3D (Bahmani et al., ). Questions and answers designed and created by the annotator Contextual Simple String Match
Sta Action Localization Video Video data are collected from MVBench (Li et al., ). Questions and answers are repurposed for the contextual formatted output format Contextual Simple String Match
Contain Contain Images Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Contain Repeat Length Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Multi Contain Repeat Position Only Length Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Contain Length Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Contain Position Images Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Contain Position Length Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Xor Images Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Multi Contain Repeat Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Contain Contain Length Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Multi Contain Position Only Images were collected from the Web. Questions and constraints are designed by the annotator. This task has no reference answer Open Constrained Generation
Relative Depth Of Different Points Images were collected from BLINK (Fu et al., ). The annotator augmented each sample by adding one more reference point manually and adjusted the answers MC Exact String Match
Visual Prediction Rater Depth Estimation Images were collected by taking screenshots from depth estimation papers on arXiv. Questions and answers were created by the annotator MC Sequence Accuracy Case Insensitive
Visual Prediction Rater Novel View Synthesis Images were collected by taking screenshots from novel view synthesis papers on arXiv. Questions and answers were created by the annotator MC Sequence Equality
Pokemon 3d Recognition Images were created by the annotator from the Pokemon Go game. Questions and answers were designed by the annotator Structured Exact String Match
Av View Identification Images were collected from the nuScenes (Caesar et al., ) dataset. Questions and answers were designed and created by the annotator Contextual Sequence Accuracy Case Insensitive
Multiview Reasoning Camera Moving Images were collected from BLINK (Fu et al., ). Questions and answers were re-designed and augmented by the annotator Exact Exact String Match
3d Indoor Scene Text Bbox Prediction The data is adapted from Multi3DRefer (Zhang et al., ). Questions and answers were designed by the annotator and dataset annotation. Numerical Nbbox Iou Single
Google Streetview Circle Reasoning The data were taken from Google Maps. Questions and answers were created by the annotator MC Simple String Match
Google Streetview Direction Understanding The data were taken from Google StreetView. Questions and answers were created by the annotator Exact Exact String Match
Video Motion Matching Real 3d Videos were collected from the project page of  Shen et al. (). Questions and answers were created by the annotator MC Exact String Match
Video Motion Matching 3d Real Videos were collected from the project page of  Shen et al. (). Questions and answers were created by the annotator MC Exact String Match
Visual Prediction Rater 3d Assembled Quality Understanding Data were collected from the project page of Wang et al. (). Questions and answers were designed and created by the annotator MC Sequence Equality
Visual Prediction Rater Surface Normal Estimation Images were collected by taking screenshots from surface normal estimation papers on arXiv. Questions and answers were created by the annotator MC Sequence Accuracy Case Insensitive
Adapted Cvbench Depth Images were collected from CV-Bench (Tong et al., ). Answers were adapted by the annotator into exact text Exact Exact String Match
Visual Prediction Rater Plane Segmentation Images were collected by taking screenshots from plane segmentation papers on arXiv MC Sequence Accuracy Case Insensitive
3d Indoor Scene Text Bbox Selection Images were collected by taking screenshots from 3D scene understanding papers on arXiv. Questions and answers were designed and generated by the annotator MC Exact String Match
Google Streetview Circle Sorting The data were taken from Google Maps. Questions and answers were created by the annotator Structured Sequence Equality
Metrics
Paper Review Writing Data collected from OpenReview’s public paper reviews Open GPT-4o as Judge
Paper Review Rating Data collected from OpenReview’s public paper reviews Numerical Number Rel Diff Ratio
Paper Review Acceptance Data collected from OpenReview’s public paper reviews Exact Exact String Match
Autorater Artifact Images were collected from ImagenHub (Ku et al., ). Questions and answers adapted by the annotator MC Exact String Match
Autorater Control Images were collected from ImagenHub (Ku et al., ). Questions and answers adapted by the annotator Exact Exact String Match
Autorater Artifact Reason Images were collected from ImagenHub (Ku et al., ). The annotator created open-ended reference answer manually Open Constrained Generation
Autorater Aesthetics Images were collected from ImagenHub (Ku et al., ). Questions and answers adapted by the annotator Exact Exact String Match
Autorater Unmask Images were collected from ImagenHub (Ku et al., ). Questions and answers adapted by the annotator Exact Exact String Match
Autorater Subject Images were collected from ImagenHub (Ku et al., ). Questions and answers adapted by the annotator Exact Exact String Match
Autorater 3d Model Texturing Resources are collected from the user study of Perla et al. (). Questions and answers were designed and created by the annotator Contextual Sequence Equality
Autorater Semantics Images were collected from ImagenHub (Ku et al., ). Questions and answers adapted by the annotator Exact Exact String Match
Autorater Motion Guided Editing Images were collected by taking screenshots from image generation papers on arXiv MC Sequence Equality
Autorater Mask Images were collected from ImagenHub (Ku et al., ). Questions and answers adapted by the annotator Exact Exact String Match
Video Eval Visual Pref Video frames were collected from ImagenHub (He et al., ). Questions and answers adapted by the annotator MC Exact String Match
Generated Video Artifacts Videos were collected by running various text-to-video diffusion models online. Open-ended reference answers were written by the annotator manually Open GPT-4o as Judge
Video Eval Factual Pref Video frames were collected from ImagenHub (He et al., ). Questions and answers adapted by the annotator MC Exact String Match
Video Eval Dynamic Pref Video frames were collected from ImagenHub (He et al., ). Questions and answers adapted by the annotator MC Exact String Match
Vizwiz Quality Accessment For Blind Images were collected from Chiu et al. (). Questions and answers were adapted and re-designed by the annotator Contextual Set Equality
Reward Models T2i Reward Images were collected from RLAIF-V dataset (Yu et al., ). Questions and answers were adapted by the annotator Exact Exact String Match
Reward Models I2t Reward Images were collected from RLAIF-V dataset (Yu et al., ). Questions and answers were adapted by the annotator Exact Exact String Match
Science
Biology Exams V Data collected from EXAMS-V (Das et al., ) and MMMU-Pro (Yue et al., ), and the questions and answers are adapted to match strings Contextual Simple String Match
Pmc Vqa Medical Image Qa Data collected from NLVR2 dataset (Suhr et al., ), and the questions and answers are adapted to match strings Contextual Simple String Match
Medical Content Based Retrieval Radiology Data collected from ROCO dataset (Pelka et al., ), and the questions and answers are adapted to match strings MC Exact String Match
Medical Abdomen MRI Organ Recognition Data collected from GMAI-MMBench (Chen et al., ), and the questions and answers are adapted to match sequence accuracy Contextual Sequence Accuracy Case Insensitive
Medical Multi Organ Segmentation Rater Data collected from pdf screenshot, and the questions and answers are adapted to match strings MC Exact String Match
Medical Cell Recognition Data collected from GMAI-MMBench (Chen et al., ), and the questions and answers are adapted to match strings Exact Exact String Match
Medical Image Artifacts Indentification Data collected from GMAI-MMBench (Chen et al., ), and the questions and answers are adapted to match strings Exact Exact String Match
Medical Blood Vessels Recognition Data collected from GMAI-MMBench (Chen et al., ), and the questions and answers are adapted to match strings Structured Exact String Match
Healthcare Info Judgement Data collected from GMAI-MMBench (Chen et al., ), and the questions and answers are adapted to match strings MC Exact String Match
Electrocardiogram Data collected from MMMU (Yue et al., ), and the answers are open-ended Open GPT-4o as Judge
Medical Polyp Segmentation Single Object Rater Data collected from pdf screenshot, and the questions and answers are adapted to match sequence equality Structured Sequence Equality
Medical Abdomen Endscopy Organ Recognition Data collected from GMAI-MMBench (Chen et al., ), and the questions and answers are adapted to match sequence accuracy Contextual Sequence Accuracy Case Insensitive
Medical Keywords Based Retrieval Non Radiology Data collected from ROCO dataset (Pelka et al., ), and the questions and answers are adapted to match strings Exact Exact String Match
Medical Parasite Detection Data collected from pdf screenshot, and the questions and answers are adapted to match set equality Structured Set Equality
Medical Retrieval Given Surgeon Activity Data collected from GMAI-MMBench (Chen et al., ), and the questions and answers are adapted to match strings MC Exact String Match
Medical Counting Lymphocytes Data collected from GMAI-MMBench (Chen et al., ), and the questions and answers are adapted to match strings Numerical Exact String Match
Chemistry Exams V Data collected from EXAMS-V (Das et al., ) and MMMU-Pro (Yue et al., ), and the questions and answers are adapted to match strings MC Simple String Match
Science Molecule Chemistry Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator Contextual Simple String Match
Mmmu Pro Exam Screenshot Data collected from MMMU-Pro (Yue et al., ), and the questions and answers are adapted to match strings MC Exact String Match
Scibench W Solution Open Ended Data collected from Scibench (Wang et al., ), and the answers are open-ended Open GPT-4o as Judge, General Single Numerical Match
arXiv Vqa Data collected from screenshots by human annotator, and the questions and answers are adapted to match strings MC Exact String Match
Tqa Textbook Qa Data collected from Dvqa (Kafle et al., ), and the questions and answers are refractered from the original TQA dataset Contextual Multi Ref Phrase
Question Solution Solving Data collected from webpage screenshots by human annotator Contextual General Single Numerical Match
Quizlet Question Solving Data collected from webpage screenshots by human annotator Contextual General Single Numerical Match
Scibench Fundamental Wo Solution Data collected from Scibench (Wang et al., ) Numerical General Single Numerical Match
Mmmu Physics Chemistry Mcq Data collected from MMMU (Yue et al., ), and the questions and answers are adapted to match strings Exact Exact String Match
Circuit Diagram Understanding Data collected from webpage screenshots by human annotator Numerical Exact String Match
Science Basic Physics Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator Contextual Simple String Match
Physics Exams V Data collected from EXAMS-V (Das et al., ) and MMMU-Pro (Yue et al., ), and the questions and answers are adapted to match strings Contextual Simple String Match
Knowledge
Background Change Images and labels come from the MFCBench (Wang et al., ) dataset. Questions and annotations were adapted by a human annotator. MC Exact String Match
Out Of Context Images and labels come from the MFCBench (Wang et al., ) dataset. Questions and annotations were adapted by a human annotator. MC Exact String Match
Text Entity Replace Images and labels come from the MFCBench (Wang et al., ) dataset. Questions and annotations were adapted by a human annotator. MC Exact String Match
Text Style Images and labels come from the MFCBench (Wang et al., ) dataset. Questions and annotations were adapted by a human annotator. MC Exact String Match
Face Attribute Edit Images and labels come from the MFCBench (Wang et al., ) dataset. Questions and annotations were adapted by a human annotator. MC Exact String Match
Face Swap Images and labels come from the MFCBench (Wang et al., ) dataset. Questions and annotations were adapted by a human annotator. MC Exact String Match
Interpret Force Perspective Illusion Images come from various websites. Questions and annotations were created by a human annotator. Exact Exact String Match
Clip Stable Diffusion Generate Images and labels come from the MFCBench (Wang et al., ) dataset. Questions and annotations were adapted by a human annotator. MC Exact String Match
Unusual Images Images come from various websites. Questions and annotations were created by a human annotator. Open GPT-4o as Judge
Forensic Detection Of Different Images Images and labels come from the BLINK benchmark (Fu et al., ). Questions and annotations were adapted by a human annotator. MC Exact String Match
Veracity Images and labels come from the MFCBench (Wang et al., ) dataset. Questions and annotations were adapted by a human annotator. MC Exact String Match
Distinguish AI Generated Image Images come from various websites and image generators. Questions and annotations were created by a human annotator. Exact Exact String Match
Cultural Vqa Images and labels come from the CulturalVQA benchmark (Romero et al., ). Questions and annotations were adapted by a human annotator. Contextual Multi Ref Phrase
Human Relationship Reasoning Images come from various websites. Questions and annotations were created by a human annotator. Contextual Simple String Match
Sign Language Videos come from Dr. Bill Vicars’ “Signs” YouTube channel. Questions and annotations were created by a human annotator. Contextual Multi Ref Phrase
Ishihara Test Images come from various websites. Questions and annotations were created by a human annotator. Structured Set Precision
Llavaguard Images and labels come from the LlavaGuard benchmark (Helff et al., ). Questions were created by a human annotator. Structured Exact String Match
Red Teaming Racial Images and labels come from the Red Teaming benchmark (Li et al., ). Questions were created by a human annotator or generated by GPT-4. Open GPT-4o as Judge
Red Teaming Captcha Images and labels come from the Red Teaming benchmark (Li et al., ). Questions were created by a human annotator or generated by GPT-4. Open GPT-4o as Judge
Red Teaming Politics Images and labels come from the Red Teaming benchmark (Li et al., ). Questions were created by a human annotator or generated by GPT-4. Open GPT-4o as Judge
Mmsoc Hatefulmemes Images and labels come from the MMSoc benchmark (Jin et al., ). Questions and answers were adapted by a human annotator. MC Exact String Match
Red Teaming Visual Order B Images and labels come from the Red Teaming benchmark (Li et al., ). Questions were created by a human annotator or generated by GPT-4. Open GPT-4o as Judge
Red Teaming Celebrity Images and labels come from the Red Teaming benchmark (Li et al., ). Questions were created by a human annotator. or generated by GPT-4 Open GPT-4o as Judge
Mmsoc Memotion Images and labels come from the MMSoc benchmark (Jin et al., ). Questions and answers were adapted by a human annotator. Structured Exact String Match
Mmsoc Misinformation Politifact Images and labels come from the MMSoc benchmark (Jin et al., ). Questions and answers were adapted by a human annotator. MC Exact String Match
Red Teaming Jailbreak Images and labels come from the Red Teaming benchmark (Li et al., ). Questions were created by a human annotator or generated by GPT-4. Open GPT-4o as Judge
Red Teaming Visual Order A Images and labels come from the Red Teaming benchmark (Li et al., ). Questions were created by a human annotator or generated by GPT-4. Open GPT-4o as Judge
Mmsoc Misinformation Gossipcop Images and labels come from the MMSoc benchmark (Jin et al., ). Questions and answers were adapted by a human annotator. MC Exact String Match
Red Teaming Visualmisleading Images and labels come from the Red Teaming benchmark (Li et al., ). Questions were created by a human annotator. Open GPT-4o as Judge
Video Content Follow Up Videos taken from [YouTube](https://www.youtube.com/). Questions and answers created by human annnotator. Open GPT-4o as Judge
Meme Explain Images come from various websites. Questions were created by a human annotator. Open GPT-4o as Judge
Funny Image Title Images come from various websites. Questions were created by a human annotator. Open GPT-4o as Judge
Emotion Recognition Videos and labels come from the CAER dataset (Lee et al., ). Questions and answers were adapted by a human annotator. Exact Exact String Match
Image Humor Understanding Images come from various websites. Questions were created by a human annotator. Open GPT-4o as Judge
Humor Explanation Images and labels come from a Humor Understanding benchmark derived from the New Yorker Caption Contest (Hessel et al., ). Questions were created by a human annotator. Open GPT-4o as Judge
Mvsa Sentiment Classification Images and labels come from the MVSA dataset (Niu et al., ). Questions and answers were adapted by a human annotator MC Exact String Match
Video Intent Recognition Video and labels come from the MIntRec dataset (Zhang et al., ). Questions and answers were adapted by a human annotator. Contextual Simple String Match
Humor Understand Caption Match Images and labels come from a Humor Understanding benchmark derived from the New Yorker Caption Contest (Hessel et al., ). Questions and answers were adapted by a human annotator. Exact Exact String Match
Figurative Speech Explanation Images come from various websites. Questions were created by a human annotator. Open GPT-4o as Judge
Muma Theory Of Mind Social Goal Images and labels come from the MuMA-ToM dataset  (Shi et al., ). Questions and answers were adapted by a human annotator. Contextual Simple String Match
Muma Theory Of Mind Belief Of Goal Images and labels come from the MuMA-ToM dataset  (Shi et al., ). Questions and answers were adapted by a human annotator. Contextual Simple String Match
Hashtag Recommendation Images and hashtags come from various social media websites. Questions were created by a human annotator. Structured Set Precision
Dish Ingredient Match Images and labels come from the [HelloFresh](https://www.hellofresh.com/recipes/) website. Questions were created by a human annotator. MC Exact String Match
Music Sheet Sentiment Images are music sheets posted to [Noteflight](https://www.noteflight.com/). Questions and answers were created by a human annotator. Exact Exact String Match
Music Sheet Author Images are music sheets posted to [Noteflight](https://www.noteflight.com/). Questions and answers were created by a human annotator. Exact Exact String Match
Music Sheet Note Count Images are music sheets posted to [Noteflight](https://www.noteflight.com/). Questions and answers were created by a human annotator. Numerical Exact String Match
Music Sheet Format Qa Images are music sheets posted to [Noteflight](https://www.noteflight.com/). Questions and answers were created by a human annotator. Numerical Exact String Match
Orchestra Score Recognition Images come from various websites. Questions were created by a human annotator. Structured Exact String Match, Simple String Match
Music Sheet Name Images are music sheets posted to [Noteflight](https://www.noteflight.com/). Questions and answers were created by a human annotator. Exact Exact String Match
Insect Order Classification Images and labels come from the BIOSCAN-1M dataset  (Gharaee et al., ). Questions and answers were adapted by a human annotator. Contextual Simple String Match
Signage Navigation Images come from various websites. Questions and answers were created by a human annotator. Exact Exact String Match
Song Title Identification From Lyrics Screenshots were taken by the human annotator on the [Spotify Web Player](https://open.spotify.com/). Questions and answers were created by the annotator. Structured Exact String Match
Knowledge Sign Recognition Images come from various websites. Questions were created by a human annotator. MC String Set Equality Comma
Brand Logo Recognition And Elaboration Images come from the FlickrLogo (Romberg et al., ) dataset and various websites. Questions were created by a human annotator. Structured Multi Ref Phrase
Logo2k Same Type Logo Retrieval Images come from the Logo2K+ dataset (Wang et al., ) dataset and various websites. Questions were created by a human annotator. Structured Exact Str Match Case Insensitive, Set Equality
Chinese Idiom Recognition Images come from various websites. Questions and answers were created by a human annotator. Exact Exact String Match
Multi Lingual Ruozhiba Explanation French Some images and labels are from the COIG-CQIA dataset (Bai et al., ) and some images are from [Baidu Tieba](https://tieba.baidu.com) and annotated by a human annotator. Open GPT-4o as Judge
Multi Lingual Ruozhiba Explanation Arabic Some images and labels are from the COIG-CQIA dataset (Bai et al., ) and some images are from [Baidu Tieba](https://tieba.baidu.com) and annotated by a human annotator. Open GPT-4o as Judge
Multi Lingual Ruozhiba Explanation Spanish Some images and labels are from the COIG-CQIA dataset (Bai et al., ) and some images are from [Baidu Tieba](https://tieba.baidu.com) and annotated by a human annotator. Open GPT-4o as Judge
Multi Lingual Ruozhiba Explanation English Some images and labels are from the COIG-CQIA dataset (Bai et al., ) and some images are from [Baidu Tieba](https://tieba.baidu.com) and annotated by a human annotator. Open GPT-4o as Judge
Multi Lingual Ruozhiba Explanation Japanese Some images and labels are from the COIG-CQIA dataset (Bai et al., ) and some images are from [Baidu Tieba](https://tieba.baidu.com) and annotated by a human annotator. Open GPT-4o as Judge
Multi Lingual Ruozhiba Explanation Russian Some images and labels are from the COIG-CQIA dataset (Bai et al., ) and some images are from [Baidu Tieba](https://tieba.baidu.com) and annotated by a human annotator. Open GPT-4o as Judge
Font Recognition Images and labels are taken from [Identifont](http://www.identifont.com). Questions are created by a human annotator. Exact Exact String Match
Traffic Accident Analysis Images and labels are taken from [Jia Kao Bao Dian](https://www.jiakaobaodian.com/sign/12/). Questions are created by a human annotator. Open GPT-4o as Judge
Multiple States Identify Asia Images come from various websites and were edited by the annotator. Questions and answers were created by a human annotator. Contextual Sequence Accuracy Case Insensitive
Multiple States Identify Americas Images come from various websites and were edited by the annotator. Questions and answers were created by a human annotator. Contextual Sequence Accuracy Case Insensitive
Multiple States Identify Europe Images come from various websites and were edited by the annotator. Questions and answers were created by a human annotator. Contextual Sequence Accuracy Case Insensitive
Multiple States Identify Africa Images come from various websites and were edited by the annotator. Questions and answers were created by a human annotator. Contextual Sequence Accuracy Case Insensitive
Worldle Images and labels are taken from [Worldle Daily](https://worldledaily.com/), a free Geoguessr alternative. Questions and answers are created by a human annotator. Structured Exact String Match
Location Vqa Images and labels come from various websites. Questions were created by a human annotator. Exact Exact String Match
Vibe Eval Open Images and labels come from the Vibe-Eval dataset Padlewski et al. (). Questions were created by a human annotator. Contextual Multi Ref Phrase
Vibe Eval Phrase Images and labels come from the Vibe-Eval dataset Padlewski et al. (). Questions were created by a human annotator. Open GPT-4o as Judge
Ancient Map Understanding Images and labels come from various websites. Questions were created by a human annotator. Exact Exact String Match
Rocks Samples Compare Images and labels come from [ChinaNeolithic.com](http://chinaneolithic.com/en/Rock/)’s online rock store. Questions were created by a human annotator. Contextual Simple String Match
Painting Qa Images and labels come from the MMMU benchmark Yue et al. (). Questions and answers were adapted by a human annotator. Exact Exact String Match
Art Explanation Images come from various websites. Questions were created by a human annotator. Open GPT-4o as Judge
Memorization Chinese Celebrity Images and labels come from various websites. Questions were created by a human annotator. Structured Multi Ref Phrase
Memorization Papers Images and labels come from various websites. Questions were created by a human annotator. Structured Simple String Match
Memorization Famous Treaty Images and labels come from various websites. Questions were created by a human annotator. Structured Exact String Match, Multi Ref Phrase
Memorization Indian Celebrity Images and labels come from various websites. Questions were created by a human annotator. Structured Exact String Match, Multi Ref Phrase
Soccer Offside Images come from various websites. Questions were created by a human annotator. MC Exact String Match
Deciphering Oracle Bone Images and labels come from the “Deciphering Oracle Bone Language with Diffusion Models” paper (Guan et al., ). Questions were created by a human annotator. Exact Exact String Match
Kvqa Knowledge Aware Qa Images and labels come from the MapQA dataset (Chang et al., ). Questions and answers were adapted by a human annotator. Contextual Simple String Match
Character Recognition In Tv Shows Screenshots were taken by the human annotator on the [Amazon Prime Video webpage](https://www.primevideo.com/). Questions and answers were created by the annotator. Contextual Set Equality
Actor Recognition In Movie Screenshots were taken by the human annotator on the [Amazon Prime Video webpage](https://www.primevideo.com/). Questions and answers were created by the annotator. Exact Exact String Match
Landmark Recognition And Qa Images and labels come from the Landmark v2 dataset (Weyand et al., ). Questions and answers were adapted by a human annotator. Structured Exact String Match, Multi Ref Phrase, Near Str Match
Famous Building Recognition Images and labels come from various websites. Questions were created by a human annotator. Structured Exact Str Match Case Insensitive, Exact String Match
Landmark Check Two Images Images and labels come from the Landmark v2 dataset (Weyand et al., ). Questions and answers were adapted by a human annotator. Structured Exact Str Match Case Insensitive
Defeasible Reasoning Images and labels come from various websites. Questions were created by a human annotator. Open GPT-4o as Judge
Poetry Limerick Images come from various websites. Questions and evaluation constraints were created by a human annotator. Open Constrained Generation
Poetry Shakespearean Sonnet Images come from various websites. Questions and evaluation constraints were created by a human annotator. Open Constrained Generation
Poetry Custom Rhyming Scheme Images come from various websites. Questions and evaluation constraints were created by a human annotator. Open Constrained Generation
Poetry Acrostic Alliteration Images come from various websites. Questions and evaluation constraints were created by a human annotator. Open Constrained Generation
Poetry Haiku Images come from various websites. Questions and evaluation constraints were created by a human annotator. Open Constrained Generation
Poetry Petrarchian Sonnet Optional Meter Images come from various websites. Questions and evaluation constraints were created by a human annotator. Open Constrained Generation
Poetry Acrostic Images come from various websites. Questions and evaluation constraints were created by a human annotator. Open Constrained Generation
Ascii Art 30 Images come from various websites. Reference ASCII art images were created using the [ASCII Art Archive’s “Image to ASCII Art” tool](https://www.asciiart.eu/image-to-ascii). Contextual ASCII Art GPT-4o Judge
Mathematics
Graph Shortest Path Kamada Kawai Data collected from [Visual Graph Arena Dataset](https://vga.csail.mit.edu/) by human annotator, and the questions and answers are adapted to match strings Numerical Exact String Match
Graph Shortest Path Planar Data collected from [Visual Graph Arena Dataset](https://vga.csail.mit.edu/) by human annotator, and the questions and answers are adapted to match strings Numerical Exact String Match
Graph Connectivity Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator Structured Exact String Match
Graph Theory Data collected from MathVision (Wang et al., ), and the questions and answers are adapted by human annotator Exact Exact String Match
Graph Isomorphism Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator MC Exact String Match
Graph Hamiltonian Cycle Data collected from [Visual Graph Arena Dataset](https://vga.csail.mit.edu/) by human annotator, and the questions and answers are adapted to match set precision Structured Exact String Match, Set Precision
Graph Hamiltonian Path Data collected from [Visual Graph Arena Dataset](https://vga.csail.mit.edu/) by human annotator, and the questions and answers are adapted to match set precision Structured Exact String Match, Set Precision
Graph Chordless Cycle Data collected from [Visual Graph Arena Dataset](https://vga.csail.mit.edu/) by human annotator, and the questions and answers are adapted to match strings Numerical Exact String Match
Topological Sort Data collected from screenshots by human annotator Structured Set Equality
Graph Maxflow Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator Numerical Exact String Match
Scibench Calculus Wo Solution Data collected from Scibench (Wang et al., ) Numerical General Single Numerical Match
Clevr Arithmetic Data collected from Clevr (Johnson et al., ) Numerical Exact String Match
Iconqa Count And Reasoning Data collected from IConQA (Lu et al., ), with annotation refractered from the original IConQA dataset Numerical Multi Ref Phrase
Number Comparison Data collected from screenshots by human annotator Numerical Exact String Match
Math Exams V Data collected from MMMU-Pro (Yue et al., ), and the questions and answers are adapted to match numerical data MC General Single Numerical Match
Theoremqa Data collected from screenshots by human annotator Contextual Boxed Single Numerical Match
Math Data collected from screenshots by human annotator Numerical Boxed Single Numerical Match
Math Parity Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator MC Exact String Match
Math Breakpoint Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator Numerical Exact String Match
Math Convexity Value Estimation Data collected from IsoBench (Fu et al., ), and the questions and answers are adapted by human annotator Structured Exact String Match, Number Rel Diff Ratio
Geometry Reasoning Count Line Intersections Data collected from Vision language models are blind (Rahmanzadehgervi et al., ), and the questions and answers are adapted by human annotator Structured Exact String Match
Geometry Length Data collected from MathVision (Wang et al., ), and the questions and answers are adapted by human annotator Contextual General Single Numerical Match
Geometry Reasoning Nested Squares Data collected from Vision language models are blind (Rahmanzadehgervi et al., ), and the questions and answers are adapted by human annotator Structured Exact String Match
Geometry Transformation Data collected from MathVision (Wang et al., ), and the questions and answers are adapted by human annotator Contextual General Single Numerical Match
Geometry Reasoning Overlapped Circle Data collected from Vision language models are blind (Rahmanzadehgervi et al., ), and the questions and answers are adapted by human annotator Structured Exact String Match
Geometry Area Data collected from MathVision (Wang et al., ), and the questions and answers are adapted by human annotator Numerical Exact String Match
Geometry Reasoning Grid Data collected from Vision language models are blind (Rahmanzadehgervi et al., ), and the questions and answers are adapted by human annotator Structured Exact String Match
Polygon Interior Angles Data collected from screenshots by human annotator Numerical Angle Seq Float Rmse
Geometry Solid Data collected from MathVision (Wang et al., ), and the questions and answers are adapted by human annotator Contextual General Single Numerical Match
Geometry Analytic Data collected from MathVision (Wang et al., ), and the questions and answers are adapted by human annotator Contextual General Single Numerical Match
Geometry Descriptive Data collected from MathVision (Wang et al., ), and the questions and answers are adapted by human annotator Contextual General Single Numerical Match
Counterfactual Arithmetic Data collected from screenshots by human annotator Numerical Exact String Match
Algebra Data collected from MathVision (Wang et al., ), and the questions and answers are adapted by human annotator Contextual General Single Numerical Match

## Appendix H Author Contribution Statement

All authors contributed at least 30 tasks to MEGA-Bench and participated in task brainstorming, annotation, and validation. They also engaged in discussions on data annotation and provided feedback on the project. The following authors made additional contributions to various aspects of the project:

Jiacheng Chen co-designed the project with Wenhu Chen, led the benchmark construction process, and coordinated collaboration among all contributors. Jiacheng Chen created and maintained the annotation GUI tool, GitHub repository, draft task taxonomy tree, and results visualization page to facilitate data annotation and improve data quality. Jiacheng Chen led the development and maintenance of the benchmark evaluation pipeline, including querying various VLMs and customizing evaluation metrics, and conducted the main experiments and analyses. Jiacheng Chen also led the writing of the paper, coordinating core contributors and incorporating their input into the manuscript.

Tianhao Liang co-led the benchmark data organization, implemented most of the model query pipelines under a consistent and unified framework, and conducted major experiments and analyses with Jiacheng Chen. Tianhao Liang helped maintain the evaluation pipeline and implemented the code execution metric. Tianhao Liang made significant efforts in data quality control, error analysis, and creating figures and tables for the paper.

Sherman Siu made major contributions to the benchmark construction process, including task reviewing, annotator coordination, data quality control, metric implementation, and code maintenance. Sherman Siu contributed and designed a bunch of complex and novel planning tasks. Sherman Siu also analyzed the number of examples per task to investigate the variance of the benchmark score and contributed to writing the main paper.

Zhengqing Wang contributed around 40 tasks, including several complex traditional computer vision tasks. Zhengqing Wang organized the benchmark statistics for the Appendix, participated in error case analysis, and developed the interactive functionalities of the project page.

Kai Wang contributed around 40 tasks with diverse data sources and output formats. Kai Wang helped organize the benchmark construction process and actively checked the annotation quality of other annotators.

Yubo Wang assisted with the experiments of open-source models.

Yuansheng Ni helped organize the Appendix and polished §B to §F.

Wang Zhu implemented the evaluation metric for symbolic planning tasks and helped with the paper writing.

Hexiang Hu participated in discussions of the project’s initial idea and continuously provided thoughts and resources for diverse tasks to facilitate the benchmark construction process. Hexiang Hu wrote a significant portion of the main paper, advised on experimental design, and helped present tables and figures.

Xiang Yue discussed the high-level directions and goals of the project with Jiacheng Chen and Wenhu Chen. Xiang Yue provided insightful thoughts for multi-dimensional results analysis and assisted with the experiments of open-source models.

Wenhu Chen proposed the initial concepts of the project, continuously advised on project progress while refining its strategic scope and direction, and called up and organized all contributors. Wenhu Chen contributed approximately 50 diverse tasks, generated ideas for new tasks, and distributed them to other annotators. Wenhu Chen wrote the initial draft of the main paper to establish the high-level structure and guided the organization and analysis of the results.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: see above
soundness: 3
strengths: # Overall assessment

This work presents an interesting contribution in a much-needed space (benchmarks for multimodal large models). To address the current scattershot approach to multimodal model benchmarking, the authors attempt to create a single, highly diverse, comprehensive benchmark for a variety of image-language tasks (including video). To construct the benchmark the authors develop and refine a task taxonomy, but some details around the taxonomy and its construction are unclear. I have concerns about how the benchmark would be used in practice related to the 40 different evaluation metrics, and the distribution over various attributes (number of images, task type, etc.) but am willing to increase my score based on discussion with authors and other reviewers.

# Major comments

* The quality of the benchmark ultimately relies on the authors' proposed taxonomy, as this forms the basis for all data collection. However, I found the description of the annotation process somewhat disappointing; it effectively amounts to "the Feynman Method" (write down the problem, think hard about it, write down the solution). Critically, the authors provide no discussion or framing around the "conceptualization stage" for how they identified the top levels of the taxonomy (perception, planning, reasoning), nor how the annotators were selected or why they are representative of the whole of relevant multimodal knowledge (the sample of annotators could also bias the coverage in various ways). Please provide a clear discussion of (a) what the levels of the taxonomy are (please give the full list) and (b) how these levels were identified and why they comprise a holistic benchmark and (c) the disciplines of the annotators (since the authors state they are graduate or above from diverse disciplines).

* The diversity of output formats is an interesting contribution. However, the diveristy of evaluation metrics (over 40 metrics?!) also makes this benchmark somewhat unwieldy, and raises concerns about usability. These issues arise even in the authors' main findings, stated at the end of Section 1. For example, it is very difficult to understand what it means that GPT-4o is 3.5% better than Claude 3.5? What makes this a "significant margin"? If Qwen2-VL is 10% better than other open source models, what does this mean? T

* It is not clear whether all tasks in the benchmark have a single, objective answer. This makes it difficult to assess models' capabilities (for example, failure to write a latex equation may simply be due to a difference in formatting; writing a story containing two animals hinges on many different criteria which are difficult to assess).

* The advantages of a single, diverse, high-coverage benchmark are outlined nicely in the introduction. However, the paper's contribution hinges on whether it does indeed  achieve strong coverage of a "diverse" suite of tasks. Ultimately, this is nearly impossible to assess, but I have some concerns about the "concepttualization" process above that make me unsure that this benchmark is as comprehensive as the authors claim. On the other hand, the existing benchmarks are also imperfect (and a direct comparison to existing benchmarks in terms of content and task design would make it easier to assess whther the benefits of the new benchmark outweigh the potential downsides and complexity).

* It is unclear why certain task distributions are set as the authors designed them in the benchmark. For example, why are only 4% of tasks 6-8 images, while 8% are 9+ images? Why are 16% of tasks open-ended while 22% are structured? These design decisions can have significant effects when averaging over benchmarks, as will likely occur with this benchmark.

* The empirical study is useful, appears comprehensive, and leads to some interesting conclusions.

* It seems unlikely that the benchmark will last very long by relying on GPT-4o as judge. Is it possible to substitute the LLM judge in the benchmark if a future best frontier model emerges?


# Minor comments

* Another relevant multimodal baseline the authors may want to reference: Bitton, Yonatan, et al. "Visit-bench: A dynamic benchmark for evaluating instruction-following vision-and-language models." Advances in Neural Information Processing Systems 36 (2023): 26898-26922.

# Typos etc

* "these models have shown great potential to solve any desired task with a well-designed prompt" - this is editorlaizing somewhat; please revise.

* L111-113: "comprehensive studies...have discovered" passive voice, consider revising
summary: This work presents a new benchmark for multimodal LLMs. The authors attempt to create a novel, diverse, comprehensive benchmark for vision-language reasoning using a several-stage process for designing the benchmark, refining the questions, and developing appropriate metrics. The authors conduct a comprehensive large-scale evaluation of current SOTA multimodal models using the benchmark.
weaknesses: see above

---

confidence: 3
contribution: 3
initial_rating: 8
presentation: 4
questions: 1. Could you explain more about the background of your 16 annotators and how you make sure for all task instances, the instruction and solution align with each other?
2. For the open-ended tasks, you mentioned using an LLM-assisted metric. How do you handle the potential for bias in the evaluation process, given that the scoring is dependent on a proprietary LLM? If we use different LLM as judges, will their ratings differ a lot from each other?
3. What are the considerations and challenges you preview when scaling MEGA-BENCH even further? How do you plan to maintain the benchmark's relevance and diversity as new multimodal tasks emerge?
soundness: 4
strengths: 1. MEGA-BENCH has a large scale and coverage, containing over 500 diverse real-world tasks, which allows for an in-depth assessment of multimodal models across various applications and skills.

2. It offers a sophisticated, fine-grained analysis capability by categorizing tasks along multiple dimensions, providing a nuanced understanding of model performance in specific areas and revealing strengths and weaknesses that aggregate scores might obscure.

3. The benchmark's design emphasizes cost-effectiveness and efficiency, demonstrating that increasing task diversity is more valuable for gaining performance insights than simply adding more examples per task.
summary: The paper presents MEGA-BENCH, a comprehensive multimodal benchmark scaling up to over 500 real-world tasks, designed to assess the diverse capabilities of vision-language models. It offers a fine-grained analysis across various dimensions, including application, input type, output format, and skills, and provides customized metrics for different output formats. The benchmark reveals significant performance variations among state-of-the-art models, emphasizing the importance of task diversity over increasing examples per task for insightful model evaluation.
weaknesses: 1. While MEGA-BENCH offers a vast array of tasks, its large scale may lead to increased computational costs and complexity in evaluation, potentially limiting its accessibility for further research and extensive exploration.
2. MEGA-BENCH's focus on breadth may result in some tasks being too specific or niche, which could limit the generalizability of the benchmark results to a broader range of multimodal problems and applications.

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: Q1: There are many different input/output formats and metrics in Mega Bench. How does Mega Bench address the challenge of "Unmanageable Setups" mentioned in the introduction?

Q2: Are there any copyright / privacy concerns for the tasks in the bench mark?
soundness: 3
strengths: S1: The proposed open-source benchmark includes a large number of diverse tasks for LLMs that can potentially address the limitations of existing benchmarks. It provides valuable resource for the community.

S2: The paper also provides an extensive experiment and analysis of popular LLMs using Mega Bench. It yields many interesting findings.

S3: This paper is well-written and easy to read.
summary: The paper presents Mega Bench, a comprehensive benchmark for evaluating multimodal models on over 500 tasks. Mega Bench features a wide range of output formats and uses multiple metrics for evaluation. It includes a detailed capability report for popular language and vision models. The benchmark's assessment of leading models reveals significant performance differences and the importance of task diversity.
weaknesses: ### Major weaknesses

W1: The rationale behind the task taxonomy tree is not well-explained. Section 3.1 can be strengthened by discussing the design considerations for the draft taxonomy tree. For example, why do we want perception, planning, reasoning? Are these the limitations of existing benchmarks? How do we know this taxonomy is comprehensive and reflects the real usage of LLMs?

W2: The introduction highlights Mega Bench's contributions in multimodal tasks. However, there is limited information regarding non-text tasks in Section 3. I recommend adding a few non-text tasks in Figure 4 and discussing the image and video tasks included in Mega Bench in Section 3.

W3: It is unconvincing that Mega Bench makes significant contributions over existing benchmarks. In the introduction, the paper lists four limitations of existing benchmarks: (1) limited output diversity, (2) lack of task coverage, (3) expensive inference cost, and (4) unmanageable setups. Section 3 and 4 explain how Mega Bench address limitations (1) and (2), but (3) and (4) remain unaddressed in the paper. I recommend discussing what makes Mega Bench less expensive and easier to run compared to other popular benchmarks.

### Minor weaknesses

M1: Replace $ (L83).

M2: The claim that "many examples or tasks are highly similar in the capabilities that they assess" requires evidence to back it up (L83-83).

M3: The tasks in Mega Bench have a lot in common with those in Big Bench. A detailed comparison to Big Bench would be beneficial.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Has the authors' team conducted any analysis on the environmental impact of the computational resources required for the benchmarking process? If so, could they share some insights?

Are there plans to release the annotation tools, pre-processing pipelines, and evaluation metrics as open-source to facilitate community-wide reproducibility and further development?

Could the authors discuss how the tasks in MEGA-BENCH map to real-world applications? Are there any tasks that are particularly relevant to current industry needs or future technological trends?
soundness: 2
strengths: The creation of MEGA-BENCH is an original contribution to the field of multimodal AI evaluation. It scales up the number of tasks to an unprecedented level, offering a comprehensive assessment of model capabilities across a vast array of real-world applications. The approach of embracing diverse output formats and developing over 40 metrics to accommodate these is innovative, moving beyond the limitations of traditional multi-choice question-based benchmarks.

The quality of the work is evident in the meticulous construction of the benchmark. With 507 realistic tasks and over 8,000 samples collected from 16 expert annotators, the dataset is both extensive and rich in diversity. The rigorous annotation process, including the development of an annotation GUI and a taxonomy tree, ensures high-quality data that is well-suited for evaluating multimodal models.

The paper is well-structured and clearly articulated. The figures and tables are effectively used to convey complex information in a digestible manner. The taxonomy tree and the breakdown of tasks across different dimensions are particularly clear, aiding the reader in understanding the scope and organization of MEGA-BENCH.
summary: The paper introduces MEGA-BENCH, a comprehensive multimodal evaluation suite that encompasses over 500 real-world tasks, addressing the diverse daily use cases of end users. Its goal is to optimize for high-quality data samples that cover a wide range of multimodal tasks while facilitating cost-effective and accurate model evaluation. The authors have compiled 507 realistic tasks with over 8,000 samples from 16 expert annotators, embracing various output formats and developing over 40 metrics to accommodate these formats. MEGA-BENCH provides a fine-grained capability report across multiple dimensions, enabling in-depth interaction with and visualization of model capabilities. The paper also evaluates various state-of-the-art vision-language models using MEGA-BENCH, revealing significant performance variations among models that were previously thought to be similar.
weaknesses: The paper presents a snapshot of model performance but does not address how these benchmarks might be used to track performance over training time. A good benchmark should be verified by scaling laws.

Meta-review: The paper introduces MEGA-BENCH, a comprehensive multimodal benchmark designed to evaluate the diverse capabilities of vision-language models across over 500 tasks. The major advantages of the benchmark are as follows: (i) scope and scale: it includes 507 realistic tasks with over 8,000 samples; (ii) metrics: the benchmark supports diverse output formats and include over 40 evaluation criteria; (iii) structured and interpretable design: it adopts a taxonomy tree to categorize tasks and ensures diversity across applications, input types, and output formats. While the reviewers initially have some concerns regarding the overlap with existing benchmarks, the complexity of evaluation and the practicality of the design, the authors carefully addressed most of them during the rebuttal discussion phase. The paper, at the end, received unanimous positive reviews. The ACs agreed with the reviewers. The ACs urge the authors to incorporate the feedbacks from the reviewers into their final camera ready version. The ACs recommend acceptance.

Decision: Accept (Poster)