danielrosehill/Open-Router-API-Pricing-Analysis huggingface dataset:
Model: Baidu: ERNIE 4.5 21B A3B
Vendor: Baidu
Hugging Face ID: baidu/ERNIE-4.5-21B-A3B-PT
Description: A sophisticated text-based Mixture-of-Experts (MoE) model featuring 21B total parameters with 3B activated per token, delivering exceptional multimodal understanding and generation through heterogeneous MoE structures and modality-isolated routing. Supporting an extensive 131K token context length, the model achieves efficient inference via multi-expert parallel collaboration and quantization, while advanced post-training techniques including SFT, DPO, and UPO ensure optimized performance across diverse applications with specialized routing and balancing losses for superior task handling.
Context length: 120000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 8000.0
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, max_tokens, presence_penalty, repetition_penalty, seed, stop, temperature, tool_choice, tools, top_k, top_p
Model: Baidu: ERNIE 4.5 VL 28B A3B
Vendor: Baidu
Hugging Face ID: baidu/ERNIE-4.5-VL-28B-A3B-PT
Description: A powerful multimodal Mixture-of-Experts chat model featuring 28B total parameters with 3B activated per token, delivering exceptional text and vision understanding through its innovative heterogeneous MoE structure with modality-isolated routing. Built with scaling-efficient infrastructure for high-throughput training and inference, the model leverages advanced post-training techniques including SFT, DPO, and UPO for optimized performance, while supporting an impressive 131K context length and RLVR alignment for superior cross-modal reasoning and generation capabilities.
Context length: 30000 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): 0.14
Completion price per 1M tokens (USD): 0.56
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.35
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, tool_choice, tools, top_k, top_p
Model: Z.AI: GLM 4.5V
Vendor: Z-AI
Hugging Face ID: zai-org/GLM-4.5V
Description: GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding, image Q&A, OCR, and document parsing, with strong gains in front-end web coding, grounding, and spatial reasoning. It offers a hybrid inference mode: a "thinking mode" for deep reasoning and a "non-thinking mode" for fast responses. Reasoning behavior can be toggled via the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)
Context length: 65536 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 16384.0
Prompt price per 1M tokens (USD): 0.6
Completion price per 1M tokens (USD): 1.7999999999999998
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.2
Output/input cost ratio: 3.0
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: AI21: Jamba Mini 1.7
Vendor: AI21 Labs
Hugging Face ID: ai21labs/AI21-Jamba-Mini-1.7
Description: Jamba Mini 1.7 is a compact and efficient member of the Jamba open model family, incorporating key improvements in grounding and instruction-following while maintaining the benefits of the SSM-Transformer hybrid architecture and 256K context window. Despite its compact size, it delivers accurate, contextually grounded responses and improved steerability.
Context length: 256000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 4096.0
Prompt price per 1M tokens (USD): 0.1999999999999999
Completion price per 1M tokens (USD): 0.3999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.3
Output/input cost ratio: 2.0
Supported parameters: max_tokens, response_format, stop, temperature, tool_choice, tools, top_p
Model: AI21: Jamba Large 1.7
Vendor: AI21 Labs
Hugging Face ID: ai21labs/AI21-Jamba-Large-1.7
Description: Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context window, it delivers more accurate, contextually grounded responses and better steerability than previous versions.
Context length: 256000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 4096.0
Prompt price per 1M tokens (USD): 2.0
Completion price per 1M tokens (USD): 8.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 5.0
Output/input cost ratio: 4.0
Supported parameters: max_tokens, response_format, stop, temperature, tool_choice, tools, top_p
Model: OpenAI: GPT-5 Chat
Vendor: OpenAI
Description: GPT-5 Chat is designed for advanced, natural, multimodal, and context-aware conversations for enterprise applications.
Context length: 128000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: file, image, text
Output modalities: text
Max completion tokens (top provider): 16384.0
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: max_tokens, response_format, seed, structured_outputs
Model: OpenAI: GPT-5
Vendor: OpenAI
Description: GPT-5 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: text, image, file
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: OpenAI: GPT-5 Mini
Vendor: OpenAI
Description: GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost. GPT-5 Mini is the successor to OpenAI's o4-mini model.
Context length: 400000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: text, image, file
Output modalities: text
Max completion tokens (top provider): 128000.0
Prompt price per 1M tokens (USD): 0.25
Completion price per 1M tokens (USD): 2.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.125
Output/input cost ratio: 8.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: OpenAI: GPT-5 Nano
Vendor: OpenAI
Description: GPT-5-Nano is the smallest and fastest variant in the GPT-5 system, optimized for developer tools, rapid interactions, and ultra-low latency environments. While limited in reasoning depth compared to its larger counterparts, it retains key instruction-following and safety features. It is the successor to GPT-4.1-nano and offers a lightweight option for cost-sensitive or real-time applications.
Context length: 400000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: text, image, file
Output modalities: text
Max completion tokens (top provider): 128000.0
Prompt price per 1M tokens (USD): 0.0499999999999999
Completion price per 1M tokens (USD): 0.3999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.2249999999999999
Output/input cost ratio: 8.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: OpenAI: gpt-oss-120b
Vendor: OpenAI
Hugging Face ID: openai/gpt-oss-120b
Description: gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.
Context length: 131072 tokens
Tokenizer: GPT
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.04
Completion price per 1M tokens (USD): 0.3999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.2199999999999999
Output/input cost ratio: 10.0
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-oss-120b (exacto)
Vendor: OpenAI
Hugging Face ID: openai/gpt-oss-120b
Description: gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.
Context length: 131072 tokens
Tokenizer: GPT
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0499999999999999
Completion price per 1M tokens (USD): 0.24
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.145
Output/input cost ratio: 4.8
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: OpenAI: gpt-oss-20b (free)
Vendor: OpenAI
Hugging Face ID: openai/gpt-oss-20b
Description: gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.
Context length: 131072 tokens
Tokenizer: GPT
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: OpenAI: gpt-oss-20b
Vendor: OpenAI
Hugging Face ID: openai/gpt-oss-20b
Description: gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.
Context length: 131072 tokens
Tokenizer: GPT
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.03
Completion price per 1M tokens (USD): 0.14
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.085
Output/input cost ratio: 4.67
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: Anthropic: Claude Opus 4.1
Vendor: Anthropic
Description: Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains in multi-file code refactoring, debugging precision, and detail-oriented reasoning. The model supports extended thinking up to 64K tokens and is optimized for tasks involving research, data analysis, and tool-assisted reasoning.
Context length: 200000 tokens
Tokenizer: Claude
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 32000.0
Prompt price per 1M tokens (USD): 15.0
Completion price per 1M tokens (USD): 75.0
Image price (USD): 0.024
Request price (USD): 0.0
Median price per 1M tokens (USD): 45.0
Output/input cost ratio: 5.0
Supported parameters: include_reasoning, max_tokens, reasoning, stop, temperature, tool_choice, tools, top_k, top_p
Model: Mistral: Codestral 2508
Vendor: Mistral AI
Description: Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.

[Blog Post](https://mistral.ai/news/codestral-25-08)
Context length: 256000 tokens
Tokenizer: Mistral
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 0.8999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.6
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: Qwen: Qwen3 Coder 30B A3B Instruct
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-Coder-30B-A3B-Instruct
Description: Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the Qwen3 architecture, it supports a native context length of 256K tokens (extendable to 1M with Yarn) and performs strongly in tasks involving function calls, browser use, and structured code completion.

This model is optimized for instruction-following without “thinking mode”, and integrates well with OpenAI-compatible tool-use formats. 
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): 0.06
Completion price per 1M tokens (USD): 0.25
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.155
Output/input cost ratio: 4.17
Supported parameters: frequency_penalty, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 30B A3B Instruct 2507
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-30B-A3B-Instruct-2507
Description: Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and agentic tool use. Post-trained on instruction data, it demonstrates competitive performance across reasoning (AIME, ZebraLogic), coding (MultiPL-E, LiveCodeBench), and alignment (IFEval, WritingBench) benchmarks. It outperforms its non-instruct variant on subjective and open-ended tasks while retaining strong factual and coding performance.
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): 0.08
Completion price per 1M tokens (USD): 0.33
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.205
Output/input cost ratio: 4.12
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: Z.AI: GLM 4.5
Vendor: Z-AI
Hugging Face ID: zai-org/GLM-4.5
Description: GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly enhanced capabilities in reasoning, code generation, and agent alignment. It supports a hybrid inference mode with two options, a "thinking mode" designed for complex reasoning and tool use, and a "non-thinking mode" optimized for instant responses. 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: 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): 0.35
Completion price per 1M tokens (USD): 1.55
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.95
Output/input cost ratio: 4.43
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 4.5 Air (free)
Vendor: Z-AI
Hugging Face ID: zai-org/GLM-4.5-Air
Description: GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter size. GLM-4.5-Air also supports hybrid inference modes, offering a "thinking mode" for advanced reasoning and tool use, and a "non-thinking mode" for real-time interaction. 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: 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: Z.AI: GLM 4.5 Air
Vendor: Z-AI
Hugging Face ID: zai-org/GLM-4.5-Air
Description: GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter size. GLM-4.5-Air also supports hybrid inference modes, offering a "thinking mode" for advanced reasoning and tool use, and a "non-thinking mode" for real-time interaction. 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: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 98304.0
Prompt price per 1M tokens (USD): 0.13
Completion price per 1M tokens (USD): 0.85
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.49
Output/input cost ratio: 6.54
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 235B A22B Thinking 2507
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-235B-A22B-Thinking-2507
Description: Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144 tokens of context. This "thinking-only" variant enhances structured logical reasoning, mathematics, science, and long-form generation, showing strong benchmark performance across AIME, SuperGPQA, LiveCodeBench, and MMLU-Redux. It enforces a special reasoning mode (</think>) and is designed for high-token outputs (up to 81,920 tokens) in challenging domains.

The model is instruction-tuned and excels at step-by-step reasoning, tool use, agentic workflows, and multilingual tasks. This release represents the most capable open-source variant in the Qwen3-235B series, surpassing many closed models in structured reasoning use cases.
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): 0.11
Completion price per 1M tokens (USD): 0.6
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.355
Output/input cost ratio: 5.45
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: Z.AI: GLM 4 32B 
Vendor: Z-AI
Description: GLM 4 32B is a cost-effective foundation language model.

It can efficiently perform complex tasks and has significantly enhanced capabilities in tool use, online search, and code-related intelligent tasks.

It is made by the same lab behind the thudm models.
Context length: 128000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0999999999999999
Completion price per 1M tokens (USD): 0.0999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0999999999999999
Output/input cost ratio: 1.0
Supported parameters: max_tokens, temperature, tool_choice, tools, top_p
Model: Qwen: Qwen3 Coder 480B A35B (free)
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-Coder-480B-A35B-Instruct
Description: Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts).

Pricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.
Context length: 262000 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 262000.0
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, seed, stop, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 Coder 480B A35B
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-Coder-480B-A35B-Instruct
Description: Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts).

Pricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.
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): 0.22
Completion price per 1M tokens (USD): 0.95
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.585
Output/input cost ratio: 4.32
Supported parameters: frequency_penalty, 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: Qwen: Qwen3 Coder 480B A35B (exacto)
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-Coder-480B-A35B-Instruct
Description: Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts).

Pricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.
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): 0.38
Completion price per 1M tokens (USD): 1.53
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.955
Output/input cost ratio: 4.03
Supported parameters: frequency_penalty, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: ByteDance: UI-TARS 7B 
Vendor: ByteDance
Hugging Face ID: ByteDance-Seed/UI-TARS-1.5-7B
Description: UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. Built by ByteDance, it builds upon the UI-TARS framework with reinforcement learning-based reasoning, enabling robust action planning and execution across virtual interfaces.

This model achieves state-of-the-art results on a range of interactive and grounding benchmarks, including OSworld, WebVoyager, AndroidWorld, and ScreenSpot. It also demonstrates perfect task completion across diverse Poki games and outperforms prior models in Minecraft agent tasks. UI-TARS-1.5 supports thought decomposition during inference and shows strong scaling across variants, with the 1.5 version notably exceeding the performance of earlier 72B and 7B checkpoints.
Context length: 128000 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 2048.0
Prompt price per 1M tokens (USD): 0.0999999999999999
Completion price per 1M tokens (USD): 0.1999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.15
Output/input cost ratio: 2.0
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Google: Gemini 2.5 Flash Lite
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): 65535.0
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
Model: Qwen: Qwen3 235B A22B Instruct 2507
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-235B-A22B-Instruct-2507
Description: Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. The model supports a native 262K context length and does not implement "thinking mode" (<think> blocks).

Compared to its base variant, this version delivers significant gains in knowledge coverage, long-context reasoning, coding benchmarks, and alignment with open-ended tasks. It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.
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): 0.08
Completion price per 1M tokens (USD): 0.55
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.315
Output/input cost ratio: 6.88
Supported parameters: frequency_penalty, 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: Switchpoint Router
Vendor: Switchpoint
Description: Switchpoint AI's router instantly analyzes your request and directs it to the optimal AI from an ever-evolving library. 

As the world of LLMs advances, our router gets smarter, ensuring you always benefit from the industry's newest models without changing your workflow.

This model is configured for a simple, flat rate per response here on OpenRouter. It's powered by the full routing engine from [Switchpoint AI](https://www.switchpoint.dev).
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.85
Completion price per 1M tokens (USD): 3.4
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 2.125
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, seed, stop, temperature, top_k, top_p
Model: MoonshotAI: Kimi K2 0711 (free)
Vendor: Moonshot AI
Hugging Face ID: moonshotai/Kimi-K2-Instruct
Description: Kimi K2 Instruct 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 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks. It supports long-context inference up to 128K tokens and is designed with a novel training stack that includes the MuonClip optimizer for stable large-scale MoE training.
Context length: 32768 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: max_tokens, seed, stop, temperature
Model: MoonshotAI: Kimi K2 0711
Vendor: Moonshot AI
Hugging Face ID: moonshotai/Kimi-K2-Instruct
Description: Kimi K2 Instruct 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 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks. It supports long-context inference up to 128K tokens and is designed with a novel training stack that includes the MuonClip optimizer for stable large-scale MoE training.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.5
Completion price per 1M tokens (USD): 2.4
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.45
Output/input cost ratio: 4.8
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: THUDM: GLM 4.1V 9B Thinking
Vendor: THUDM
Hugging Face ID: THUDM/GLM-4.1V-9B-Thinking
Description: GLM-4.1V-9B-Thinking is a 9B parameter vision-language model developed by THUDM, based on the GLM-4-9B foundation. It introduces a reasoning-centric "thinking paradigm" enhanced with reinforcement learning to improve multimodal reasoning, long-context understanding (up to 64K tokens), and complex problem solving. It achieves state-of-the-art performance among models in its class, outperforming even larger models like Qwen-2.5-VL-72B on a majority of benchmark tasks. 
Context length: 65536 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 8000.0
Prompt price per 1M tokens (USD): 0.035
Completion price per 1M tokens (USD): 0.1379999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0865
Output/input cost ratio: 3.94
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Mistral: Devstral Medium
Vendor: Mistral AI
Description: Devstral Medium is a high-performance code generation and agentic reasoning model developed jointly by Mistral AI and All Hands AI. Positioned as a step up from Devstral Small, it achieves 61.6% on SWE-Bench Verified, placing it ahead of Gemini 2.5 Pro and GPT-4.1 in code-related tasks, at a fraction of the cost. It is designed for generalization across prompt styles and tool use in code agents and frameworks.

Devstral Medium is available via API only (not open-weight), and supports enterprise deployment on private infrastructure, with optional fine-tuning capabilities.
Context length: 131072 tokens
Tokenizer: Mistral
Modality: text->text
Input modalities: text
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
Model: Mistral: Devstral Small 1.1
Vendor: Mistral AI
Hugging Face ID: mistralai/Devstral-Small-2507
Description: Devstral Small 1.1 is a 24B parameter open-weight language model for software engineering agents, developed by Mistral AI in collaboration with All Hands AI. Finetuned from Mistral Small 3.1 and released under the Apache 2.0 license, it features a 128k token context window and supports both Mistral-style function calling and XML output formats.

Designed for agentic coding workflows, Devstral Small 1.1 is optimized for tasks such as codebase exploration, multi-file edits, and integration into autonomous development agents like OpenHands and Cline. It achieves 53.6% on SWE-Bench Verified, surpassing all other open models on this benchmark, while remaining lightweight enough to run on a single 4090 GPU or Apple silicon machine. The model uses a Tekken tokenizer with a 131k vocabulary and is deployable via vLLM, Transformers, Ollama, LM Studio, and other OpenAI-compatible runtimes.

Context length: 128000 tokens
Tokenizer: Mistral
Modality: text->text
Input modalities: text
Output modalities: text
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, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Venice: Uncensored (free)
Vendor: Cognitive Computations
Hugging Face ID: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
Description: Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an “uncensored” instruct-tuned LLM, preserving user control over alignment, system prompts, and behavior. Intended for advanced and unrestricted use cases, Venice Uncensored emphasizes steerability and transparent behavior, removing default safety and alignment layers typically found in mainstream assistant models.
Context length: 32768 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: frequency_penalty, max_tokens, presence_penalty, response_format, stop, structured_outputs, temperature, top_k, top_p
Model: xAI: Grok 4
Vendor: xAI
Description: Grok 4 is xAI's latest reasoning model with a 256k context window. It supports parallel tool calling, structured outputs, and both image and text inputs. Note that reasoning is not exposed, reasoning cannot be disabled, and the reasoning effort cannot be specified. Pricing increases once the total tokens in a given request is greater than 128k tokens. See more details on the [xAI docs](https://docs.x.ai/docs/models/grok-4-0709)
Context length: 256000 tokens
Tokenizer: Grok
Modality: text+image->text
Input modalities: image, text
Output modalities: text
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, logprobs, max_tokens, reasoning, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: Google: Gemma 3n 2B (free)
Vendor: Google
Hugging Face ID: google/gemma-3n-E2B-it
Description: Gemma 3n E2B IT is a multimodal, instruction-tuned model developed by Google DeepMind, designed to operate efficiently at an effective parameter size of 2B while leveraging a 6B architecture. Based on the MatFormer architecture, it supports nested submodels and modular composition via the Mix-and-Match framework. Gemma 3n models are optimized for low-resource deployment, offering 32K context length and strong multilingual and reasoning performance across common benchmarks. This variant is trained on a diverse corpus including code, math, web, and multimodal data.
Context length: 8192 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 2048.0
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, response_format, seed, stop, temperature, top_p
Model: Tencent: Hunyuan A13B Instruct
Vendor: Tencent
Hugging Face ID: tencent/Hunyuan-A13B-Instruct
Description: Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark performance across mathematics, science, coding, and multi-turn reasoning tasks, while maintaining high inference efficiency via Grouped Query Attention (GQA) and quantization support (FP8, GPTQ, etc.).
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): 0.14
Completion price per 1M tokens (USD): 0.5700000000000001
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.355
Output/input cost ratio: 4.07
Supported parameters: frequency_penalty, include_reasoning, reasoning, response_format, structured_outputs, temperature, top_k, top_p
Model: TNG: DeepSeek R1T2 Chimera (free)
Vendor: TNG Technology
Hugging Face ID: tngtech/DeepSeek-TNG-R1T2-Chimera
Description: DeepSeek-TNG-R1T2-Chimera is the second-generation Chimera model from TNG Tech. It is a 671 B-parameter mixture-of-experts text-generation model assembled from DeepSeek-AI’s R1-0528, R1, and V3-0324 checkpoints with an Assembly-of-Experts merge. The tri-parent design yields strong reasoning performance while running roughly 20 % faster than the original R1 and more than 2× faster than R1-0528 under vLLM, giving a favorable cost-to-intelligence trade-off. The checkpoint supports contexts up to 60 k tokens in standard use (tested to ~130 k) and maintains consistent <think> token behaviour, making it suitable for long-context analysis, dialogue and other open-ended generation tasks.
Context length: 163840 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: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: TNG: DeepSeek R1T2 Chimera
Vendor: TNG Technology
Hugging Face ID: tngtech/DeepSeek-TNG-R1T2-Chimera
Description: DeepSeek-TNG-R1T2-Chimera is the second-generation Chimera model from TNG Tech. It is a 671 B-parameter mixture-of-experts text-generation model assembled from DeepSeek-AI’s R1-0528, R1, and V3-0324 checkpoints with an Assembly-of-Experts merge. The tri-parent design yields strong reasoning performance while running roughly 20 % faster than the original R1 and more than 2× faster than R1-0528 under vLLM, giving a favorable cost-to-intelligence trade-off. The checkpoint supports contexts up to 60 k tokens in standard use (tested to ~130 k) and maintains consistent <think> token behaviour, making it suitable for long-context analysis, dialogue and other open-ended generation 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): 0.3
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: Morph: Morph V3 Large
Vendor: Morph
Description: Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations.

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 Morph. Learn more about this model in their [documentation](https://docs.morphllm.com/quickstart)
Context length: 262144 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): 0.8999999999999999
Completion price per 1M tokens (USD): 1.9
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.4
Output/input cost ratio: 2.11
Supported parameters: max_tokens, stop, temperature
Model: Morph: Morph V3 Fast
Vendor: Morph
Description: Morph's fastest apply model for code edits. ~10,500 tokens/sec with 96% accuracy for rapid code transformations.

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 Morph. Learn more about this model in their [documentation](https://docs.morphllm.com/quickstart)
Context length: 81920 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 38000.0
Prompt price per 1M tokens (USD): 0.7999999999999999
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.0
Output/input cost ratio: 1.5
Supported parameters: max_tokens, stop, temperature
Model: Baidu: ERNIE 4.5 VL 424B A47B 
Vendor: Baidu
Hugging Face ID: baidu/ERNIE-4.5-VL-424B-A47B-PT
Description: ERNIE-4.5-VL-424B-A47B is a multimodal Mixture-of-Experts (MoE) model from Baidu’s ERNIE 4.5 series, featuring 424B total parameters with 47B active per token. It is trained jointly on text and image data using a heterogeneous MoE architecture and modality-isolated routing to enable high-fidelity cross-modal reasoning, image understanding, and long-context generation (up to 131k tokens). Fine-tuned with techniques like SFT, DPO, UPO, and RLVR, this model supports both “thinking” and non-thinking inference modes. Designed for vision-language tasks in English and Chinese, it is optimized for efficient scaling and can operate under 4-bit/8-bit quantization.
Context length: 123000 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 16000.0
Prompt price per 1M tokens (USD): 0.42
Completion price per 1M tokens (USD): 1.25
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.835
Output/input cost ratio: 2.98
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p