danielrosehill/Open-Router-API-Pricing-Analysis huggingface dataset:
Model: Qwen: Qwen3 VL 235B A22B Thinking
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-VL-235B-A22B-Thinking
Description: Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math. The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning.

Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows, turning sketches or mockups into code and assisting with UI debugging, while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.
Context length: 262144 tokens
Tokenizer: Qwen3
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 262144.0
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.75
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 VL 235B A22B Instruct
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-VL-235B-A22B-Instruct
Description: Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning.

Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.
Context length: 262144 tokens
Tokenizer: Qwen3
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.22
Completion price per 1M tokens (USD): 0.88
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.55
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Model: Qwen: Qwen3 Max
Vendor: Qwen
Description: Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It delivers higher accuracy in math, coding, logic, and science tasks, follows complex instructions in Chinese and English more reliably, reduces hallucinations, and produces higher-quality responses for open-ended Q&A, writing, and conversation. The model supports over 100 languages with stronger translation and commonsense reasoning, and is optimized for retrieval-augmented generation (RAG) and tool calling, though it does not include a dedicated “thinking” mode.
Context length: 256000 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 1.2
Completion price per 1M tokens (USD): 6.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 3.6
Output/input cost ratio: 5.0
Supported parameters: max_tokens, presence_penalty, response_format, seed, temperature, tool_choice, tools, top_p
Model: Qwen: Qwen3 Coder Plus
Vendor: Qwen
Description: Qwen3 Coder Plus is Alibaba's proprietary version of the Open Source Qwen3 Coder 480B A35B. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.
Context length: 128000 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 65536.0
Prompt price per 1M tokens (USD): 1.0
Completion price per 1M tokens (USD): 5.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 3.0
Output/input cost ratio: 5.0
Supported parameters: max_tokens, presence_penalty, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_p
Model: OpenAI: GPT-5 Codex
Vendor: OpenAI
Description: GPT-5-Codex is a specialized version of GPT-5 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks. The model supports building projects from scratch, feature development, debugging, large-scale refactoring, and code review. Compared to GPT-5, Codex is more steerable, adheres closely to developer instructions, and produces cleaner, higher-quality code outputs. Reasoning effort can be adjusted with the `reasoning.effort` parameter. Read the [docs here](https://openrouter.ai/docs/use-cases/reasoning-tokens#reasoning-effort-level)

Codex integrates into developer environments including the CLI, IDE extensions, GitHub, and cloud tasks. It adapts reasoning effort dynamically—providing fast responses for small tasks while sustaining extended multi-hour runs for large projects. The model is trained to perform structured code reviews, catching critical flaws by reasoning over dependencies and validating behavior against tests. It also supports multimodal inputs such as images or screenshots for UI development and integrates tool use for search, dependency installation, and environment setup. Codex is intended specifically for agentic coding applications.
Context length: 400000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 128000.0
Prompt price per 1M tokens (USD): 1.25
Completion price per 1M tokens (USD): 10.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 5.625
Output/input cost ratio: 8.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: DeepSeek: DeepSeek V3.1 Terminus
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-V3.1-Terminus
Description: DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)

The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. 
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 163840.0
Prompt price per 1M tokens (USD): 0.2299999999999999
Completion price per 1M tokens (USD): 0.8999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.565
Output/input cost ratio: 3.91
Supported parameters: frequency_penalty, include_reasoning, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: DeepSeek: DeepSeek V3.1 Terminus (exacto)
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-V3.1-Terminus
Description: DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)

The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. 
Context length: 131072 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 65536.0
Prompt price per 1M tokens (USD): 0.27
Completion price per 1M tokens (USD): 1.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.635
Output/input cost ratio: 3.7
Supported parameters: frequency_penalty, include_reasoning, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: xAI: Grok 4 Fast
Vendor: xAI
Description: Grok 4 Fast is xAI's latest multimodal model with SOTA cost-efficiency and a 2M token context window. It comes in two flavors: non-reasoning and reasoning. Read more about the model on xAI's [news post](http://x.ai/news/grok-4-fast). Reasoning can be enabled using the `reasoning` `enabled` parameter in the API. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#controlling-reasoning-tokens)
Context length: 2000000 tokens
Tokenizer: Grok
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 30000.0
Prompt price per 1M tokens (USD): 0.1999999999999999
Completion price per 1M tokens (USD): 0.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.35
Output/input cost ratio: 2.5
Supported parameters: include_reasoning, logprobs, max_tokens, reasoning, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: Tongyi DeepResearch 30B A3B (free)
Vendor: Alibaba
Hugging Face ID: Alibaba-NLP/Tongyi-DeepResearch-30B-A3B
Description: Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks and delivers state-of-the-art performance on benchmarks like Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch, and FRAMES. This makes it superior for complex agentic search, reasoning, and multi-step problem-solving compared to prior models.

The model includes a fully automated synthetic data pipeline for scalable pre-training, fine-tuning, and reinforcement learning. It uses large-scale continual pre-training on diverse agentic data to boost reasoning and stay fresh. It also features end-to-end on-policy RL with a customized Group Relative Policy Optimization, including token-level gradients and negative sample filtering for stable training. The model supports ReAct for core ability checks and an IterResearch-based 'Heavy' mode for max performance through test-time scaling. It's ideal for advanced research agents, tool use, and heavy inference workflows.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Tongyi DeepResearch 30B A3B
Vendor: Alibaba
Hugging Face ID: Alibaba-NLP/Tongyi-DeepResearch-30B-A3B
Description: Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks and delivers state-of-the-art performance on benchmarks like Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch, and FRAMES. This makes it superior for complex agentic search, reasoning, and multi-step problem-solving compared to prior models.

The model includes a fully automated synthetic data pipeline for scalable pre-training, fine-tuning, and reinforcement learning. It uses large-scale continual pre-training on diverse agentic data to boost reasoning and stay fresh. It also features end-to-end on-policy RL with a customized Group Relative Policy Optimization, including token-level gradients and negative sample filtering for stable training. The model supports ReAct for core ability checks and an IterResearch-based 'Heavy' mode for max performance through test-time scaling. It's ideal for advanced research agents, tool use, and heavy inference workflows.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.09
Completion price per 1M tokens (USD): 0.3999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.245
Output/input cost ratio: 4.44
Supported parameters: frequency_penalty, include_reasoning, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 Coder Flash
Vendor: Qwen
Description: Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.
Context length: 128000 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 65536.0
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 1.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.9
Output/input cost ratio: 5.0
Supported parameters: max_tokens, presence_penalty, response_format, seed, temperature, tool_choice, tools, top_p
Model: Arcee AI: AFM 4.5B
Vendor: Arcee AI
Hugging Face ID: arcee-ai/AFM-4.5B
Description: AFM-4.5B is a 4.5 billion parameter instruction-tuned language model developed by Arcee AI. The model was pretrained on approximately 8 trillion tokens, including 6.5 trillion tokens of general data and 1.5 trillion tokens with an emphasis on mathematical reasoning and code generation. 
Context length: 65536 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.048
Completion price per 1M tokens (USD): 0.15
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.099
Output/input cost ratio: 3.12
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, stop, structured_outputs, temperature, top_k, top_p
Model: OpenGVLab: InternVL3 78B
Vendor: OpenGVLab
Hugging Face ID: OpenGVLab/InternVL3-78B
Description: The InternVL3 series is an advanced multimodal large language model (MLLM). Compared to InternVL 2.5, InternVL3 demonstrates stronger multimodal perception and reasoning capabilities. 

In addition, InternVL3 is benchmarked against the Qwen2.5 Chat models, whose pre-trained base models serve as the initialization for its language component. Benefiting from Native Multimodal Pre-Training, the InternVL3 series surpasses the Qwen2.5 series in overall text performance.
Context length: 32768 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.07
Completion price per 1M tokens (USD): 0.26
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.165
Output/input cost ratio: 3.71
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_p
Model: Qwen: Qwen3 Next 80B A3B Thinking
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-Next-80B-A3B-Thinking
Description: Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default. It’s designed for hard multi-step problems; math proofs, code synthesis/debugging, logic, and agentic planning, and reports strong results across knowledge, reasoning, coding, alignment, and multilingual evaluations. Compared with prior Qwen3 variants, it emphasizes stability under long chains of thought and efficient scaling during inference, and it is tuned to follow complex instructions while reducing repetitive or off-task behavior.

The model is suitable for agent frameworks and tool use (function calling), retrieval-heavy workflows, and standardized benchmarking where step-by-step solutions are required. It supports long, detailed completions and leverages throughput-oriented techniques (e.g., multi-token prediction) for faster generation. Note that it operates in thinking-only mode.
Context length: 262144 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 262144.0
Prompt price per 1M tokens (USD): 0.15
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.6749999999999999
Output/input cost ratio: 8.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 Next 80B A3B Instruct
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-Next-80B-A3B-Instruct
Description: Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought.

The model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.
Context length: 262144 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 262144.0
Prompt price per 1M tokens (USD): 0.0999999999999999
Completion price per 1M tokens (USD): 0.7999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.4499999999999999
Output/input cost ratio: 8.0
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Meituan: LongCat Flash Chat (free)
Vendor: Meituan
Hugging Face ID: meituan-longcat/LongCat-Flash-Chat
Description: LongCat-Flash-Chat is a large-scale Mixture-of-Experts (MoE) model with 560B total parameters, of which 18.6B–31.3B (≈27B on average) are dynamically activated per input. It introduces a shortcut-connected MoE design to reduce communication overhead and achieve high throughput while maintaining training stability through advanced scaling strategies such as hyperparameter transfer, deterministic computation, and multi-stage optimization.

This release, LongCat-Flash-Chat, is a non-thinking foundation model optimized for conversational and agentic tasks. It supports long context windows up to 128K tokens and shows competitive performance across reasoning, coding, instruction following, and domain benchmarks, with particular strengths in tool use and complex multi-step interactions.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Meituan: LongCat Flash Chat
Vendor: Meituan
Hugging Face ID: meituan-longcat/LongCat-Flash-Chat
Description: LongCat-Flash-Chat is a large-scale Mixture-of-Experts (MoE) model with 560B total parameters, of which 18.6B–31.3B (≈27B on average) are dynamically activated per input. It introduces a shortcut-connected MoE design to reduce communication overhead and achieve high throughput while maintaining training stability through advanced scaling strategies such as hyperparameter transfer, deterministic computation, and multi-stage optimization.

This release, LongCat-Flash-Chat, is a non-thinking foundation model optimized for conversational and agentic tasks. It supports long context windows up to 128K tokens and shows competitive performance across reasoning, coding, instruction following, and domain benchmarks, with particular strengths in tool use and complex multi-step interactions.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.15
Completion price per 1M tokens (USD): 0.75
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.45
Output/input cost ratio: 5.0
Supported parameters: max_tokens, temperature, top_p
Model: Qwen: Qwen Plus 0728
Vendor: Qwen
Description: Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.
Context length: 1000000 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.3999999999999999
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.7999999999999999
Output/input cost ratio: 3.0
Supported parameters: max_tokens, presence_penalty, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_p
Model: Qwen: Qwen Plus 0728 (thinking)
Vendor: Qwen
Description: Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.
Context length: 1000000 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.3999999999999999
Completion price per 1M tokens (USD): 4.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 2.2
Output/input cost ratio: 10.0
Supported parameters: include_reasoning, max_tokens, presence_penalty, reasoning, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_p
Model: NVIDIA: Nemotron Nano 9B V2 (free)
Vendor: NVIDIA
Hugging Face ID: nvidia/NVIDIA-Nemotron-Nano-9B-v2
Description: NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. 

The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so.
Context length: 128000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: include_reasoning, reasoning, response_format, structured_outputs, tool_choice, tools
Model: NVIDIA: Nemotron Nano 9B V2
Vendor: NVIDIA
Hugging Face ID: nvidia/NVIDIA-Nemotron-Nano-9B-v2
Description: NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. 

The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.04
Completion price per 1M tokens (USD): 0.16
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.1
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, temperature, tool_choice, tools, top_k, top_p
Model: MoonshotAI: Kimi K2 0905
Vendor: Moonshot AI
Hugging Face ID: moonshotai/Kimi-K2-Instruct-0905
Description: Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k.

This update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.
Context length: 262144 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 262144.0
Prompt price per 1M tokens (USD): 0.39
Completion price per 1M tokens (USD): 1.9
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.145
Output/input cost ratio: 4.87
Supported parameters: frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Model: MoonshotAI: Kimi K2 0905 (exacto)
Vendor: Moonshot AI
Hugging Face ID: moonshotai/Kimi-K2-Instruct-0905
Description: Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k.

This update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.
Context length: 262144 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.6
Completion price per 1M tokens (USD): 2.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.55
Output/input cost ratio: 4.17
Supported parameters: frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: Deep Cogito: Cogito V2 Preview Llama 70B
Vendor: DeepCogito
Hugging Face ID: deepcogito/cogito-v2-preview-llama-70B
Description: Cogito v2 70B is a dense hybrid reasoning model that combines direct answering capabilities with advanced self-reflection. Built with iterative policy improvement, it delivers strong performance across reasoning tasks while maintaining efficiency through shorter reasoning chains and improved intuition.
Context length: 32768 tokens
Tokenizer: Llama3
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.88
Completion price per 1M tokens (USD): 0.88
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.88
Output/input cost ratio: 1.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Cogito V2 Preview Llama 109B
Vendor: DeepCogito
Hugging Face ID: deepcogito/cogito-v2-preview-llama-109B-MoE
Description: An instruction-tuned, hybrid-reasoning Mixture-of-Experts model built on Llama-4-Scout-17B-16E. Cogito v2 can answer directly or engage an extended “thinking” phase, with alignment guided by Iterated Distillation & Amplification (IDA). It targets coding, STEM, instruction following, and general helpfulness, with stronger multilingual, tool-calling, and reasoning performance than size-equivalent baselines. The model supports long-context use (up to 10M tokens) and standard Transformers workflows. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)
Context length: 32767 tokens
Tokenizer: Llama4
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Prompt price per 1M tokens (USD): 0.18
Completion price per 1M tokens (USD): 0.59
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.385
Output/input cost ratio: 3.28
Supported parameters: frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, stop, temperature, tool_choice, tools, top_k, top_p
Model: Deep Cogito: Cogito V2 Preview Deepseek 671B
Vendor: DeepCogito
Hugging Face ID: deepcogito/cogito-v2-preview-deepseek-671B-MoE
Description: Cogito v2 is a multilingual, instruction-tuned Mixture of Experts (MoE) large language model with 671 billion parameters. It supports both standard and reasoning-based generation modes. The model introduces hybrid reasoning via Iterated Distillation and Amplification (IDA)—an iterative self-improvement strategy designed to scale alignment with general intelligence. Cogito v2 has been optimized for STEM, programming, instruction following, and tool use. It supports 128k context length and offers strong performance in both multilingual and code-heavy environments. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 1.25
Completion price per 1M tokens (USD): 1.25
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.25
Output/input cost ratio: 1.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, stop, temperature, top_k, top_p
Model: StepFun: Step3
Vendor: Stepfun AI
Hugging Face ID: stepfun-ai/step3
Description: Step3 is a cutting-edge multimodal reasoning model—built on a Mixture-of-Experts architecture with 321B total parameters and 38B active. It is designed end-to-end to minimize decoding costs while delivering top-tier performance in vision–language reasoning. Through the co-design of Multi-Matrix Factorization Attention (MFA) and Attention-FFN Disaggregation (AFD), Step3 maintains exceptional efficiency across both flagship and low-end accelerators.
Context length: 65536 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 65536.0
Prompt price per 1M tokens (USD): 0.5700000000000001
Completion price per 1M tokens (USD): 1.42
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.995
Output/input cost ratio: 2.49
Supported parameters: frequency_penalty, include_reasoning, reasoning, response_format, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 30B A3B Thinking 2507
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-30B-A3B-Thinking-2507
Description: Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking. The model is designed specifically for “thinking mode,” where internal reasoning traces are separated from final answers.

Compared to earlier Qwen3-30B releases, this version improves performance across logical reasoning, mathematics, science, coding, and multilingual benchmarks. It also demonstrates stronger instruction following, tool use, and alignment with human preferences. With higher reasoning efficiency and extended output budgets, it is best suited for advanced research, competitive problem solving, and agentic applications requiring structured long-context reasoning.
Context length: 262144 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.09
Completion price per 1M tokens (USD): 0.3
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.195
Output/input cost ratio: 3.33
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: xAI: Grok Code Fast 1
Vendor: xAI
Description: Grok Code Fast 1 is a speedy and economical reasoning model that excels at agentic coding. With reasoning traces visible in the response, developers can steer Grok Code for high-quality work flows.
Context length: 256000 tokens
Tokenizer: Grok
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 10000.0
Prompt price per 1M tokens (USD): 0.1999999999999999
Completion price per 1M tokens (USD): 1.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.85
Output/input cost ratio: 7.5
Supported parameters: include_reasoning, logprobs, max_tokens, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: Nous: Hermes 4 70B
Vendor: Nous Research
Hugging Face ID: NousResearch/Hermes-4-70B
Description: Hermes 4 70B is a hybrid reasoning model from Nous Research, built on Meta-Llama-3.1-70B. It introduces the same hybrid mode as the larger 405B release, allowing the model to either respond directly or generate explicit <think>...</think> reasoning traces before answering. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)

This 70B variant is trained with the expanded post-training corpus (~60B tokens) emphasizing verified reasoning data, leading to improvements in mathematics, coding, STEM, logic, and structured outputs while maintaining general assistant performance. It supports JSON mode, schema adherence, function calling, and tool use, and is designed for greater steerability with reduced refusal rates.
Context length: 131072 tokens
Tokenizer: Llama3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.11
Completion price per 1M tokens (USD): 0.38
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.245
Output/input cost ratio: 3.45
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Nous: Hermes 4 405B
Vendor: Nous Research
Hugging Face ID: NousResearch/Hermes-4-405B
Description: Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. It introduces a hybrid reasoning mode, where the model can choose to deliberate internally with <think>...</think> traces or respond directly, offering flexibility between speed and depth. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)

The model is instruction-tuned with an expanded post-training corpus (~60B tokens) emphasizing reasoning traces, improving performance in math, code, STEM, and logical reasoning, while retaining broad assistant utility. It also supports structured outputs, including JSON mode, schema adherence, function calling, and tool use. Hermes 4 is trained for steerability, lower refusal rates, and alignment toward neutral, user-directed behavior.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.75
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Google: Gemini 2.5 Flash Image Preview (Nano Banana)
Vendor: Google
Description: Gemini 2.5 Flash Image Preview, a.k.a. "Nano Banana," is a state of the art image generation model with contextual understanding. It is capable of image generation, edits, and multi-turn conversations.
Context length: 32768 tokens
Tokenizer: Gemini
Modality: text+image->text+image
Input modalities: image, text
Output modalities: image, text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 2.5
Image price (USD): 0.001238
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.4
Output/input cost ratio: 8.33
Supported parameters: max_tokens, response_format, seed, structured_outputs, temperature, top_p
Model: DeepSeek: DeepSeek V3.1 (free)
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-V3.1
Description: DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)

The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. 

It succeeds the [DeepSeek V3-0324](/deepseek/deepseek-chat-v3-0324) model and performs well on a variety of tasks.
Context length: 163800 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: include_reasoning, max_tokens, reasoning, seed, stop, temperature
Model: DeepSeek: DeepSeek V3.1
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-V3.1
Description: DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)

The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. 

It succeeds the [DeepSeek V3-0324](/deepseek/deepseek-chat-v3-0324) model and performs well on a variety of tasks.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 163840.0
Prompt price per 1M tokens (USD): 0.1999999999999999
Completion price per 1M tokens (USD): 0.7999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.4999999999999999
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Model: OpenAI: GPT-4o Audio
Vendor: OpenAI
Description: The gpt-4o-audio-preview model adds support for audio inputs as prompts. This enhancement allows the model to detect nuances within audio recordings and add depth to generated user experiences. Audio outputs are currently not supported. Audio tokens are priced at $40 per million input audio tokens.
Context length: 128000 tokens
Tokenizer: GPT
Modality: text->text
Input modalities: audio, text
Output modalities: text
Max completion tokens (top provider): 16384.0
Prompt price per 1M tokens (USD): 2.5
Completion price per 1M tokens (USD): 10.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 6.25
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, logit_bias, logprobs, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: Mistral: Mistral Medium 3.1
Vendor: Mistral AI
Description: Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases.

The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3.1 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.
Context length: 131072 tokens
Tokenizer: Mistral
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Prompt price per 1M tokens (USD): 0.3999999999999999
Completion price per 1M tokens (USD): 2.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.2
Output/input cost ratio: 5.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p