danielrosehill/Open-Router-API-Pricing-Analysis huggingface dataset:
Model: Kwaipilot: Kat Coder (free)
Vendor: Kwai
Description: KAT-Coder is the flagship coding model from Kwaipilot. It achieves an exceptional 73.4% resolution rate on SWE-Bench Verified.

KAT-Coder was trained using advanced multi-stage training including mid-training, supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and large-scale agentic reinforcement learning (RL) on enterprise codebases.
Context length: 256000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 32000.0
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: MoonshotAI: Kimi Linear 48B A3B Instruct
Vendor: Moonshot AI
Hugging Face ID: moonshotai/Kimi-Linear-48B-A3B-Instruct
Description: Kimi Linear is a hybrid linear attention architecture that outperforms traditional full attention methods across various contexts, including short, long, and reinforcement learning (RL) scaling regimes. At its core is Kimi Delta Attention (KDA)—a refined version of Gated DeltaNet that introduces a more efficient gating mechanism to optimize the use of finite-state RNN memory.

Kimi Linear achieves superior performance and hardware efficiency, especially for long-context tasks. It reduces the need for large KV caches by up to 75% and boosts decoding throughput by up to 6x for contexts as long as 1M tokens.
Context length: 1048576 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 1048576.0
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 0.6
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.4499999999999999
Output/input cost ratio: 2.0
Supported parameters: frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_logprobs, top_p
Model: Polaris Alpha
Vendor: OpenRouter
Description: This is a cloaked model provided to the community to gather feedback. A powerful, general-purpose model that excels across real-world tasks, with standout performance in coding, tool calling, and instruction following.

**Note:** All prompts and completions for this model are logged by the provider and may be used to improve the model.
Context length: 256000 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 128000.0
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: max_tokens, response_format, structured_outputs, tool_choice, tools
Model: MoonshotAI: Kimi K2 Thinking
Vendor: Moonshot AI
Hugging Face ID: moonshotai/Kimi-K2-Thinking
Description: Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in Kimi K2, it activates 32 billion parameters per forward pass and supports 256 k-token context windows. The model is optimized for persistent step-by-step thought, dynamic tool invocation, and complex reasoning workflows that span hundreds of turns. It interleaves step-by-step reasoning with tool use, enabling autonomous research, coding, and writing that can persist for hundreds of sequential actions without drift.

It sets new open-source benchmarks on HLE, BrowseComp, SWE-Multilingual, and LiveCodeBench, while maintaining stable multi-agent behavior through 200–300 tool calls. Built on a large-scale MoE architecture with MuonClip optimization, it combines strong reasoning depth with high inference efficiency for demanding agentic and analytical tasks.
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): 0.6
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, 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: Amazon: Nova Premier 1.0
Vendor: Amazon
Description: Amazon Nova Premier is the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models.
Context length: 1000000 tokens
Tokenizer: Nova
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 32000.0
Prompt price per 1M tokens (USD): 2.5
Completion price per 1M tokens (USD): 12.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 7.5
Output/input cost ratio: 5.0
Supported parameters: max_tokens, stop, temperature, tools, top_k, top_p
Model: Perplexity: Sonar Pro Search
Vendor: Perplexity
Description: Exclusively available on the OpenRouter API, Sonar Pro's new Pro Search mode is Perplexity's most advanced agentic search system. It is designed for deeper reasoning and analysis. Pricing is based on tokens plus $18 per thousand requests. This model powers the Pro Search mode on the Perplexity platform.

Sonar Pro Search adds autonomous, multi-step reasoning to Sonar Pro. So, instead of just one query + synthesis, it plans and executes entire research workflows using tools.
Context length: 200000 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 8000.0
Prompt price per 1M tokens (USD): 3.0
Completion price per 1M tokens (USD): 15.0
Image price (USD): 0.0
Request price (USD): 0.018
Median price per 1M tokens (USD): 9.0
Output/input cost ratio: 5.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, structured_outputs, temperature, top_k, top_p, web_search_options
Model: Mistral: Voxtral Small 24B 2507
Vendor: Mistral AI
Hugging Face ID: mistralai/Voxtral-Small-24B-2507
Description: Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio is priced at $100 per million seconds.
Context length: 32000 tokens
Tokenizer: Mistral
Modality: text->text
Input modalities: text, audio
Output modalities: text
Prompt price per 1M tokens (USD): 0.0999999999999999
Completion price per 1M tokens (USD): 0.3
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.1999999999999999
Output/input cost ratio: 3.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: OpenAI: gpt-oss-safeguard-20b
Vendor: OpenAI
Hugging Face ID: openai/gpt-oss-safeguard-20b
Description: gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks like content classification, LLM filtering, and trust & safety labeling.

Learn more about this model in OpenAI's gpt-oss-safeguard [user guide](https://cookbook.openai.com/articles/gpt-oss-safeguard-guide).
Context length: 131072 tokens
Tokenizer: GPT
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 65536.0
Prompt price per 1M tokens (USD): 0.075
Completion price per 1M tokens (USD): 0.3
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.1875
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, stop, temperature, tool_choice, tools, top_p
Model: NVIDIA: Nemotron Nano 12B 2 VL (free)
Vendor: NVIDIA
Hugging Face ID: nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16
Description: NVIDIA Nemotron Nano 2 VL is a 12-billion-parameter open multimodal reasoning model designed for video understanding and document intelligence. It introduces a hybrid Transformer-Mamba architecture, combining transformer-level accuracy with Mamba’s memory-efficient sequence modeling for significantly higher throughput and lower latency.

The model supports inputs of text and multi-image documents, producing natural-language outputs. It is trained on high-quality NVIDIA-curated synthetic datasets optimized for optical-character recognition, chart reasoning, and multimodal comprehension.

Nemotron Nano 2 VL achieves leading results on OCRBench v2 and scores ≈ 74 average across MMMU, MathVista, AI2D, OCRBench, OCR-Reasoning, ChartQA, DocVQA, and Video-MME—surpassing prior open VL baselines. With Efficient Video Sampling (EVS), it handles long-form videos while reducing inference cost.

Open-weights, training data, and fine-tuning recipes are released under a permissive NVIDIA open license, with deployment supported across NeMo, NIM, and major inference runtimes.
Context length: 128000 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 128000.0
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: include_reasoning, reasoning, tool_choice, tools
Model: NVIDIA: Nemotron Nano 12B 2 VL
Vendor: NVIDIA
Hugging Face ID: nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16
Description: NVIDIA Nemotron Nano 2 VL is a 12-billion-parameter open multimodal reasoning model designed for video understanding and document intelligence. It introduces a hybrid Transformer-Mamba architecture, combining transformer-level accuracy with Mamba’s memory-efficient sequence modeling for significantly higher throughput and lower latency.

The model supports inputs of text and multi-image documents, producing natural-language outputs. It is trained on high-quality NVIDIA-curated synthetic datasets optimized for optical-character recognition, chart reasoning, and multimodal comprehension.

Nemotron Nano 2 VL achieves leading results on OCRBench v2 and scores ≈ 74 average across MMMU, MathVista, AI2D, OCRBench, OCR-Reasoning, ChartQA, DocVQA, and Video-MME—surpassing prior open VL baselines. With Efficient Video Sampling (EVS), it handles long-form videos while reducing inference cost.

Open-weights, training data, and fine-tuning recipes are released under a permissive NVIDIA open license, with deployment supported across NeMo, NIM, and major inference runtimes.
Context length: 131072 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Prompt price per 1M tokens (USD): 0.1999999999999999
Completion price per 1M tokens (USD): 0.6
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.3999999999999999
Output/input cost ratio: 3.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, temperature, top_k, top_p
Model: MiniMax: MiniMax M2 (free)
Vendor: Minimax
Hugging Face ID: MiniMaxAI/MiniMax-M2
Description: MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency.

The model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors.

Benchmarked by [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2), MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency.

To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our [docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks).
Context length: 204800 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: include_reasoning, max_tokens, reasoning, temperature, tool_choice, tools, top_p
Model: MiniMax: MiniMax M2
Vendor: Minimax
Hugging Face ID: MiniMaxAI/MiniMax-M2
Description: MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency.

The model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors.

Benchmarked by [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2), MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency.

To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our [docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks).
Context length: 204800 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): 0.255
Completion price per 1M tokens (USD): 1.02
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.6375
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Model: LiquidAI/LFM2-8B-A1B
Vendor: Liquid AI
Hugging Face ID: LiquidAI/LFM2-8B-A1B
Description: Model created via inbox interface
Context length: 32768 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0499999999999999
Completion price per 1M tokens (USD): 0.0999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.075
Output/input cost ratio: 2.0
Supported parameters: frequency_penalty, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: LiquidAI/LFM2-2.6B
Vendor: Liquid AI
Hugging Face ID: LiquidAI/LFM2-2.6B
Description: LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
Context length: 32768 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0499999999999999
Completion price per 1M tokens (USD): 0.0999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.075
Output/input cost ratio: 2.0
Supported parameters: frequency_penalty, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: IBM: Granite 4.0 Micro
Vendor: IBM
Hugging Face ID: ibm-granite/granite-4.0-h-micro
Description: Granite-4.0-H-Micro is a 3B parameter from the Granite 4 family of models. These models are the latest in a series of models released by IBM. They are fine-tuned for long context tool calling. 
Context length: 131000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.017
Completion price per 1M tokens (USD): 0.11
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0635
Output/input cost ratio: 6.47
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, seed, temperature, top_k, top_p
Model: Deep Cogito: Cogito V2 Preview Llama 405B
Vendor: DeepCogito
Hugging Face ID: deepcogito/cogito-v2-preview-llama-405B
Description: Cogito v2 405B is a dense hybrid reasoning model that combines direct answering capabilities with advanced self-reflection. It represents a significant step toward frontier intelligence with dense architecture delivering performance competitive with leading closed models. This advanced reasoning system combines policy improvement with massive scale for exceptional capabilities.

Context length: 32768 tokens
Tokenizer: Llama3
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 3.5
Completion price per 1M tokens (USD): 3.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 3.5
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: OpenAI: GPT-5 Image Mini
Vendor: OpenAI
Description: GPT-5 Image Mini combines OpenAI's advanced language capabilities, powered by [GPT-5 Mini](https://openrouter.ai/openai/gpt-5-mini), with GPT Image 1 Mini for efficient image generation. This natively multimodal model features superior instruction following, text rendering, and detailed image editing with reduced latency and cost. It excels at high-quality visual creation while maintaining strong text understanding, making it ideal for applications that require both efficient image generation and text processing at scale.
Context length: 400000 tokens
Tokenizer: GPT
Modality: text+image->text+image
Input modalities: file, image, text
Output modalities: image, text
Max completion tokens (top provider): 128000.0
Prompt price per 1M tokens (USD): 2.5
Completion price per 1M tokens (USD): 2.0
Image price (USD): 2.5e-06
Request price (USD): 0.0
Median price per 1M tokens (USD): 2.25
Output/input cost ratio: 0.8
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: Anthropic: Claude Haiku 4.5
Vendor: Anthropic
Description: Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance across reasoning, coding, and computer-use tasks, Haiku 4.5 brings frontier-level capability to real-time and high-volume applications.

It introduces extended thinking to the Haiku line; enabling controllable reasoning depth, summarized or interleaved thought output, and tool-assisted workflows with full support for coding, bash, web search, and computer-use tools. Scoring >73% on SWE-bench Verified, Haiku 4.5 ranks among the world’s best coding models while maintaining exceptional responsiveness for sub-agents, parallelized execution, and scaled deployment.
Context length: 200000 tokens
Tokenizer: Claude
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 64000.0
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: include_reasoning, max_tokens, reasoning, stop, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 VL 8B Thinking
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-VL-8B-Thinking
Description: Qwen3-VL-8B-Thinking is the reasoning-optimized variant of the Qwen3-VL-8B multimodal model, designed for advanced visual and textual reasoning across complex scenes, documents, and temporal sequences. It integrates enhanced multimodal alignment and long-context processing (native 256K, expandable to 1M tokens) for tasks such as scientific visual analysis, causal inference, and mathematical reasoning over image or video inputs.

Compared to the Instruct edition, the Thinking version introduces deeper visual-language fusion and deliberate reasoning pathways that improve performance on long-chain logic tasks, STEM problem-solving, and multi-step video understanding. It achieves stronger temporal grounding via Interleaved-MRoPE and timestamp-aware embeddings, while maintaining robust OCR, multilingual comprehension, and text generation on par with large text-only LLMs.
Context length: 256000 tokens
Tokenizer: Qwen3
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.18
Completion price per 1M tokens (USD): 2.1
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.14
Output/input cost ratio: 11.67
Supported parameters: include_reasoning, max_tokens, presence_penalty, reasoning, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_p
Model: Qwen: Qwen3 VL 8B Instruct
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-VL-8B-Instruct
Description: Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon temporal reasoning, DeepStack for fine-grained visual-text alignment, and text-timestamp alignment for precise event localization.

The model supports a native 256K-token context window, extensible to 1M tokens, and handles both static and dynamic media inputs for tasks like document parsing, visual question answering, spatial reasoning, and GUI control. It achieves text understanding comparable to leading LLMs while expanding OCR coverage to 32 languages and enhancing robustness under varied visual conditions.
Context length: 131072 tokens
Tokenizer: Qwen3
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.08
Completion price per 1M tokens (USD): 0.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.29
Output/input cost ratio: 6.25
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: OpenAI: GPT-5 Image
Vendor: OpenAI
Description: [GPT-5](https://openrouter.ai/openai/gpt-5) Image combines OpenAI's most advanced language model with state-of-the-art image generation capabilities. It offers major improvements in reasoning, code quality, and user experience while incorporating GPT Image 1's superior instruction following, text rendering, and detailed image editing.
Context length: 400000 tokens
Tokenizer: GPT
Modality: text+image->text+image
Input modalities: image, text, file
Output modalities: image, text
Max completion tokens (top provider): 128000.0
Prompt price per 1M tokens (USD): 10.0
Completion price per 1M tokens (USD): 10.0
Image price (USD): 1e-05
Request price (USD): 0.0
Median price per 1M tokens (USD): 10.0
Output/input cost ratio: 1.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: inclusionAI: Ring 1T
Vendor: Inclusion AI
Hugging Face ID: inclusionAI/Ring-1T
Description: Ring-1T has undergone continued scaling with large-scale verifiable reward reinforcement learning (RLVR) training, further unlocking the natural language reasoning capabilities of the trillion-parameter foundation model. Through RLHF training, the model's general abilities have also been refined, making this release of Ring-1T more balanced in performance across various tasks.

Ring-1T adopts the Ling 2.0 architecture and is trained on the Ling-1T-base foundation model, which contains 1 trillion total parameters with 50 billion activated parameters, supporting a context window of up to 128K tokens.
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): 0.5700000000000001
Completion price per 1M tokens (USD): 2.28
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.4250000000000005
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, reasoning, response_format, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: inclusionAI: Ling-1T
Vendor: Inclusion AI
Hugging Face ID: inclusionAI/Ling-1T
Description: Ling-1T is a trillion-parameter open-weight large language model developed by inclusionAI and released under the MIT license. It represents the first flagship non-thinking model in the Ling 2.0 series, built around a sparse-activation architecture with roughly 50 billion active parameters per token. The model supports up to 128 K tokens of context and emphasizes efficient reasoning through an “Evolutionary Chain-of-Thought (Evo-CoT)” training strategy.

Pre-trained on more than 20 trillion reasoning-dense tokens, Ling-1T achieves strong results across code generation, mathematics, and logical reasoning benchmarks while maintaining high inference efficiency. It employs FP8 mixed-precision training, MoE routing with QK normalization, and MTP layers for compositional reasoning stability. The model also introduces LPO (Linguistics-unit Policy Optimization) for post-training alignment, enhancing sentence-level semantic control.

Ling-1T can perform complex text generation, multilingual reasoning, and front-end code synthesis with a focus on both functionality and aesthetics.
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): 0.5700000000000001
Completion price per 1M tokens (USD): 2.28
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.4250000000000005
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, response_format, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: OpenAI: o3 Deep Research
Vendor: OpenAI
Description: o3-deep-research is OpenAI's advanced model for deep research, designed to tackle complex, multi-step research tasks.

Note: This model always uses the 'web_search' tool which adds additional cost.
Context length: 200000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 100000.0
Prompt price per 1M tokens (USD): 10.0
Completion price per 1M tokens (USD): 40.0
Image price (USD): 0.00765
Request price (USD): 0.0
Median price per 1M tokens (USD): 25.0
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: OpenAI: o4 Mini Deep Research
Vendor: OpenAI
Description: o4-mini-deep-research is OpenAI's faster, more affordable deep research model—ideal for tackling complex, multi-step research tasks.

Note: This model always uses the 'web_search' tool which adds additional cost.
Context length: 200000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: file, image, text
Output modalities: text
Max completion tokens (top provider): 100000.0
Prompt price per 1M tokens (USD): 2.0
Completion price per 1M tokens (USD): 8.0
Image price (USD): 0.00153
Request price (USD): 0.0
Median price per 1M tokens (USD): 5.0
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: NVIDIA: Llama 3.3 Nemotron Super 49B V1.5
Vendor: NVIDIA
Hugging Face ID: nvidia/Llama-3_3-Nemotron-Super-49B-v1_5
Description: Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B-parameter, English-centric reasoning/chat model derived from Meta’s Llama-3.3-70B-Instruct with a 128K context. It’s post-trained for agentic workflows (RAG, tool calling) via SFT across math, code, science, and multi-turn chat, followed by multiple RL stages; Reward-aware Preference Optimization (RPO) for alignment, RL with Verifiable Rewards (RLVR) for step-wise reasoning, and iterative DPO to refine tool-use behavior. A distillation-driven Neural Architecture Search (“Puzzle”) replaces some attention blocks and varies FFN widths to shrink memory footprint and improve throughput, enabling single-GPU (H100/H200) deployment while preserving instruction following and CoT quality.

In internal evaluations (NeMo-Skills, up to 16 runs, temp = 0.6, top_p = 0.95), the model reports strong reasoning/coding results, e.g., MATH500 pass@1 = 97.4, AIME-2024 = 87.5, AIME-2025 = 82.71, GPQA = 71.97, LiveCodeBench (24.10–25.02) = 73.58, and MMLU-Pro (CoT) = 79.53. The model targets practical inference efficiency (high tokens/s, reduced VRAM) with Transformers/vLLM support and explicit “reasoning on/off” modes (chat-first defaults, greedy recommended when disabled). Suitable for building agents, assistants, and long-context retrieval systems where balanced accuracy-to-cost and reliable tool use matter.

Context length: 131072 tokens
Tokenizer: Llama3
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0999999999999999
Completion price per 1M tokens (USD): 0.3999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.2499999999999999
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, temperature, tool_choice, tools, top_k, top_p
Model: Baidu: ERNIE 4.5 21B A3B Thinking
Vendor: Baidu
Hugging Face ID: baidu/ERNIE-4.5-21B-A3B-Thinking
Description: ERNIE-4.5-21B-A3B-Thinking is Baidu's upgraded lightweight MoE model, refined to boost reasoning depth and quality for top-tier performance in logical puzzles, math, science, coding, text generation, and expert-level academic benchmarks.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 65536.0
Prompt price per 1M tokens (USD): 0.07
Completion price per 1M tokens (USD): 0.28
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.175
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Google: Gemini 2.5 Flash Image (Nano Banana)
Vendor: Google
Description: Gemini 2.5 Flash Image, a.k.a. "Nano Banana," is now generally available. It is a state of the art image generation model with contextual understanding. It is capable of image generation, edits, and multi-turn conversations. Aspect ratios can be controlled with the [image_config API Parameter](https://openrouter.ai/docs/features/multimodal/image-generation#image-aspect-ratio-configuration)
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: Qwen: Qwen3 VL 30B A3B Thinking
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-VL-30B-A3B-Thinking
Description: Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.
Context length: 131072 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): 0.1999999999999999
Completion price per 1M tokens (USD): 1.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.6
Output/input cost ratio: 5.0
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 30B A3B Instruct
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-VL-30B-A3B-Instruct
Description: Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.
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): 0.15
Completion price per 1M tokens (USD): 0.6
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.375
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: OpenAI: GPT-5 Pro
Vendor: OpenAI
Description: GPT-5 Pro is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy in high-stakes use cases. It supports test-time routing features and advanced prompt understanding, including user-specified intent like "think hard about this." Improvements include reductions in hallucination, sycophancy, and better performance in coding, writing, and health-related tasks.
Context length: 400000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 128000.0
Prompt price per 1M tokens (USD): 15.0
Completion price per 1M tokens (USD): 120.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 67.5
Output/input cost ratio: 8.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: Z.AI: GLM 4.6
Vendor: Z-AI
Description: Compared with GLM-4.5, this generation brings several key improvements:

Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.
Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.
Context length: 202752 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 202752.0
Prompt price per 1M tokens (USD): 0.3999999999999999
Completion price per 1M tokens (USD): 1.75
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.075
Output/input cost ratio: 4.38
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_a, top_k, top_logprobs, top_p
Model: Z.AI: GLM 4.6 (exacto)
Vendor: Z-AI
Description: Compared with GLM-4.5, this generation brings several key improvements:

Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.
Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.
Context length: 202752 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.4499999999999999
Completion price per 1M tokens (USD): 1.9
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.1749999999999998
Output/input cost ratio: 4.22
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: Anthropic: Claude Sonnet 4.5
Vendor: Anthropic
Description: Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with improvements across system design, code security, and specification adherence. The model is designed for extended autonomous operation, maintaining task continuity across sessions and providing fact-based progress tracking.

Sonnet 4.5 also introduces stronger agentic capabilities, including improved tool orchestration, speculative parallel execution, and more efficient context and memory management. With enhanced context tracking and awareness of token usage across tool calls, it is particularly well-suited for multi-context and long-running workflows. Use cases span software engineering, cybersecurity, financial analysis, research agents, and other domains requiring sustained reasoning and tool use.
Context length: 1000000 tokens
Tokenizer: Claude
Modality: text+image->text
Input modalities: text, image, file
Output modalities: text
Max completion tokens (top provider): 64000.0
Prompt price per 1M tokens (USD): 3.0
Completion price per 1M tokens (USD): 15.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 9.0
Output/input cost ratio: 5.0
Supported parameters: include_reasoning, max_tokens, reasoning, stop, temperature, tool_choice, tools, top_k, top_p
Model: DeepSeek: DeepSeek V3.2 Exp
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-V3.2-Exp
Description: DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism designed to improve training and inference efficiency in long-context scenarios while maintaining output quality. 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 was trained under conditions aligned with V3.1-Terminus to enable direct comparison. Benchmarking shows performance roughly on par with V3.1 across reasoning, coding, and agentic tool-use tasks, with minor tradeoffs and gains depending on the domain. This release focuses on validating architectural optimizations for extended context lengths rather than advancing raw task accuracy, making it primarily a research-oriented model for exploring efficient transformer designs.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.27
Completion price per 1M tokens (USD): 0.3999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.3349999999999999
Output/input cost ratio: 1.48
Supported parameters: frequency_penalty, include_reasoning, 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: TheDrummer: Cydonia 24B V4.1
Vendor: TheDrummer
Hugging Face ID: thedrummer/cydonia-24b-v4.1
Description: Uncensored and creative writing model based on Mistral Small 3.2 24B with good recall, prompt adherence, and intelligence.
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): 0.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.4
Output/input cost ratio: 1.67
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Relace: Relace Apply 3
Vendor: Relace
Description: Relace Apply 3 is a specialized code-patching LLM that merges AI-suggested edits straight into your source files. It can apply updates from GPT-4o, Claude, and others into your files at 10,000 tokens/sec on average.

The model requires the prompt to be in the following format: 
<instruction>{instruction}</instruction>
<code>{initial_code}</code>
<update>{edit_snippet}</update>

Zero Data Retention is enabled for Relace. Learn more about this model in their [documentation](https://docs.relace.ai/api-reference/instant-apply/apply)
Context length: 256000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 128000.0
Prompt price per 1M tokens (USD): 0.85
Completion price per 1M tokens (USD): 1.25
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.05
Output/input cost ratio: 1.47
Supported parameters: max_tokens, seed, stop
Model: Google: Gemini 2.5 Flash Preview 09-2025
Vendor: Google
Description: Gemini 2.5 Flash Preview September 2025 Checkpoint is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater accuracy and nuanced context handling. 

Additionally, Gemini 2.5 Flash is configurable through the "max tokens for reasoning" parameter, as described in the documentation (https://openrouter.ai/docs/use-cases/reasoning-tokens#max-tokens-for-reasoning).
Context length: 1048576 tokens
Tokenizer: Gemini
Modality: text+image->text
Input modalities: image, file, text, audio, video
Output modalities: text
Max completion tokens (top provider): 65536.0
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: include_reasoning, max_tokens, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: Google: Gemini 2.5 Flash Lite Preview 09-2025
Vendor: Google
Description: Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, "thinking" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the [Reasoning API parameter](https://openrouter.ai/docs/use-cases/reasoning-tokens) to selectively trade off cost for intelligence. 
Context length: 1048576 tokens
Tokenizer: Gemini
Modality: text+image->text
Input modalities: text, image, file, audio, video
Output modalities: text
Max completion tokens (top provider): 65536.0
Prompt price per 1M tokens (USD): 0.0999999999999999
Completion price per 1M tokens (USD): 0.3999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.2499999999999999
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p