danielrosehill/Open-Router-API-Pricing-Analysis huggingface dataset:
Model: Baidu: ERNIE 4.5 300B A47B 
Vendor: Baidu
Hugging Face ID: baidu/ERNIE-4.5-300B-A47B-PT
Description: ERNIE-4.5-300B-A47B is a 300B parameter Mixture-of-Experts (MoE) language model developed by Baidu as part of the ERNIE 4.5 series. It activates 47B parameters per token and supports text generation in both English and Chinese. Optimized for high-throughput inference and efficient scaling, it uses a heterogeneous MoE structure with advanced routing and quantization strategies, including FP8 and 2-bit formats. This version is fine-tuned for language-only tasks and supports reasoning, tool parameters, and extended context lengths up to 131k tokens. Suitable for general-purpose LLM applications with high reasoning and throughput demands.
Context length: 123000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 12000.0
Prompt price per 1M tokens (USD): 0.28
Completion price per 1M tokens (USD): 1.1
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.6900000000000001
Output/input cost ratio: 3.93
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_p
Model: TheDrummer: Anubis 70B V1.1
Vendor: TheDrummer
Hugging Face ID: TheDrummer/Anubis-70B-v1.1
Description: TheDrummer's Anubis v1.1 is an unaligned, creative Llama 3.3 70B model focused on providing character-driven roleplay & stories. It excels at gritty, visceral prose, unique character adherence, and coherent narratives, while maintaining the instruction following Llama 3.3 70B is known for.
Context length: 131072 tokens
Tokenizer: Llama3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.65
Completion price per 1M tokens (USD): 1.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.825
Output/input cost ratio: 1.54
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Inception: Mercury
Vendor: Inception
Description: Mercury is the first diffusion large language model (dLLM). Applying a breakthrough discrete diffusion approach, the model runs 5-10x faster than even speed optimized models like GPT-4.1 Nano and Claude 3.5 Haiku while matching their performance. Mercury's speed enables developers to provide responsive user experiences, including with voice agents, search interfaces, and chatbots. Read more in the [blog post]
(https://www.inceptionlabs.ai/blog/introducing-mercury) here. 
Context length: 128000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 16384.0
Prompt price per 1M tokens (USD): 0.25
Completion price per 1M tokens (USD): 1.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.625
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Mistral: Mistral Small 3.2 24B (free)
Vendor: Mistral AI
Hugging Face ID: mistralai/Mistral-Small-3.2-24B-Instruct-2506
Description: Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on WildBench and Arena Hard, reduces infinite generations, and delivers gains in tool use and structured output tasks.

It supports image and text inputs with structured outputs, function/tool calling, and strong performance across coding (HumanEval+, MBPP), STEM (MMLU, MATH, GPQA), and vision benchmarks (ChartQA, DocVQA).
Context length: 131072 tokens
Tokenizer: Mistral
Modality: text+image->text
Input modalities: image, 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, repetition_penalty, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Mistral: Mistral Small 3.2 24B
Vendor: Mistral AI
Hugging Face ID: mistralai/Mistral-Small-3.2-24B-Instruct-2506
Description: Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on WildBench and Arena Hard, reduces infinite generations, and delivers gains in tool use and structured output tasks.

It supports image and text inputs with structured outputs, function/tool calling, and strong performance across coding (HumanEval+, MBPP), STEM (MMLU, MATH, GPQA), and vision benchmarks (ChartQA, DocVQA).
Context length: 131072 tokens
Tokenizer: Mistral
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.06
Completion price per 1M tokens (USD): 0.18
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.12
Output/input cost ratio: 3.0
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: MiniMax: MiniMax M1
Vendor: Minimax
Description: MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it to process long sequences—up to 1 million tokens—while maintaining competitive FLOP efficiency. With 456 billion total parameters and 45.9B active per token, this variant is optimized for complex, multi-step reasoning tasks.

Trained via a custom reinforcement learning pipeline (CISPO), M1 excels in long-context understanding, software engineering, agentic tool use, and mathematical reasoning. Benchmarks show strong performance across FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench, often outperforming other open models like DeepSeek R1 and Qwen3-235B.
Context length: 1000000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 40000.0
Prompt price per 1M tokens (USD): 0.3999999999999999
Completion price per 1M tokens (USD): 2.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.3
Output/input cost ratio: 5.5
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, tool_choice, tools, top_k, top_p
Model: Google: Gemini 2.5 Flash Lite Preview 06-17
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: file, image, text, audio
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: Google: Gemini 2.5 Flash
Vendor: Google
Description: Gemini 2.5 Flash 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: file, image, text, audio, video
Output modalities: text
Max completion tokens (top provider): 65535.0
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 Pro
Vendor: Google
Description: Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.
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): 1.25
Completion price per 1M tokens (USD): 10.0
Image price (USD): 0.00516
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, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: MoonshotAI: Kimi Dev 72B
Vendor: Moonshot AI
Hugging Face ID: moonshotai/Kimi-Dev-72B
Description: Kimi-Dev-72B is an open-source large language model fine-tuned for software engineering and issue resolution tasks. Based on Qwen2.5-72B, it is optimized using large-scale reinforcement learning that applies code patches in real repositories and validates them via full test suite execution—rewarding only correct, robust completions. The model achieves 60.4% on SWE-bench Verified, setting a new benchmark among open-source models for software bug fixing and code reasoning.
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): 0.29
Completion price per 1M tokens (USD): 1.15
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.72
Output/input cost ratio: 3.97
Supported parameters: frequency_penalty, include_reasoning, reasoning, response_format, structured_outputs, temperature, top_k, top_p
Model: OpenAI: o3 Pro
Vendor: OpenAI
Description: The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o3-pro model uses more compute to think harder and provide consistently better answers.

Note that BYOK is required for this model. Set up here: https://openrouter.ai/settings/integrations
Context length: 200000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: text, file, image
Output modalities: text
Max completion tokens (top provider): 100000.0
Prompt price per 1M tokens (USD): 20.0
Completion price per 1M tokens (USD): 80.0
Image price (USD): 0.0153
Request price (USD): 0.0
Median price per 1M tokens (USD): 50.0
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: xAI: Grok 3 Mini
Vendor: xAI
Description: A lightweight model that thinks before responding. Fast, smart, and great for logic-based tasks that do not require deep domain knowledge. The raw thinking traces are accessible.
Context length: 131072 tokens
Tokenizer: Grok
Modality: text->text
Input modalities: text
Output modalities: text
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: include_reasoning, logprobs, max_tokens, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: xAI: Grok 3
Vendor: xAI
Description: Grok 3 is the latest model from xAI. It's their flagship model that excels at enterprise use cases like data extraction, coding, and text summarization. Possesses deep domain knowledge in finance, healthcare, law, and science.


Context length: 131072 tokens
Tokenizer: Grok
Modality: text->text
Input modalities: 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: frequency_penalty, logprobs, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Model: Mistral: Magistral Small 2506
Vendor: Mistral AI
Hugging Face ID: mistralai/Magistral-Small-2506
Description: Magistral Small is a 24B parameter instruction-tuned model based on Mistral-Small-3.1 (2503), enhanced through supervised fine-tuning on traces from Magistral Medium and further refined via reinforcement learning. It is optimized for reasoning and supports a wide multilingual range, including over 20 languages.
Context length: 40000 tokens
Tokenizer: Mistral
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 40000.0
Prompt price per 1M tokens (USD): 0.5
Completion price per 1M tokens (USD): 1.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.0
Output/input cost ratio: 3.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: Mistral: Magistral Medium 2506 (thinking)
Vendor: Mistral AI
Description: Magistral is Mistral's first reasoning model. It is ideal for general purpose use requiring longer thought processing and better accuracy than with non-reasoning LLMs. From legal research and financial forecasting to software development and creative storytelling — this model solves multi-step challenges where transparency and precision are critical.
Context length: 40960 tokens
Tokenizer: Mistral
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 40000.0
Prompt price per 1M tokens (USD): 2.0
Completion price per 1M tokens (USD): 5.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 3.5
Output/input cost ratio: 2.5
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: Mistral: Magistral Medium 2506
Vendor: Mistral AI
Description: Magistral is Mistral's first reasoning model. It is ideal for general purpose use requiring longer thought processing and better accuracy than with non-reasoning LLMs. From legal research and financial forecasting to software development and creative storytelling — this model solves multi-step challenges where transparency and precision are critical.
Context length: 40960 tokens
Tokenizer: Mistral
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 40000.0
Prompt price per 1M tokens (USD): 2.0
Completion price per 1M tokens (USD): 5.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 3.5
Output/input cost ratio: 2.5
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: Google: Gemini 2.5 Pro Preview 06-05
Vendor: Google
Description: Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.

Context length: 1048576 tokens
Tokenizer: Gemini
Modality: text+image->text
Input modalities: file, image, text, audio
Output modalities: text
Max completion tokens (top provider): 65536.0
Prompt price per 1M tokens (USD): 1.25
Completion price per 1M tokens (USD): 10.0
Image price (USD): 0.00516
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, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: DeepSeek: DeepSeek R1 0528 Qwen3 8B (free)
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/deepseek-r1-0528-qwen3-8b
Description: DeepSeek-R1-0528 is a lightly upgraded release of DeepSeek R1 that taps more compute and smarter post-training tricks, pushing its reasoning and inference to the brink of flagship models like O3 and Gemini 2.5 Pro.
It now tops math, programming, and logic leaderboards, showcasing a step-change in depth-of-thought.
The distilled variant, DeepSeek-R1-0528-Qwen3-8B, transfers this chain-of-thought into an 8 B-parameter form, beating standard Qwen3 8B by +10 pp and tying the 235 B “thinking” giant on AIME 2024.
Context length: 131072 tokens
Tokenizer: Qwen
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: DeepSeek: DeepSeek R1 0528 Qwen3 8B
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/deepseek-r1-0528-qwen3-8b
Description: DeepSeek-R1-0528 is a lightly upgraded release of DeepSeek R1 that taps more compute and smarter post-training tricks, pushing its reasoning and inference to the brink of flagship models like O3 and Gemini 2.5 Pro.
It now tops math, programming, and logic leaderboards, showcasing a step-change in depth-of-thought.
The distilled variant, DeepSeek-R1-0528-Qwen3-8B, transfers this chain-of-thought into an 8 B-parameter form, beating standard Qwen3 8B by +10 pp and tying the 235 B “thinking” giant on AIME 2024.
Context length: 32768 tokens
Tokenizer: Qwen
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.02
Completion price per 1M tokens (USD): 0.0999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.06
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, top_k, top_p
Model: DeepSeek: R1 0528 (free)
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-R1-0528
Description: May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass.

Fully open-source model.
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: DeepSeek: R1 0528
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-R1-0528
Description: May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass.

Fully open-source model.
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): 0.3999999999999999
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_k, top_logprobs, top_p
Model: Anthropic: Claude Opus 4
Vendor: Anthropic
Description: Claude Opus 4 is benchmarked as the world’s best coding model, at time of release, bringing sustained performance on complex, long-running tasks and agent workflows. It sets new benchmarks in software engineering, achieving leading results on SWE-bench (72.5%) and Terminal-bench (43.2%). Opus 4 supports extended, agentic workflows, handling thousands of task steps continuously for hours without degradation. 

Read more at the [blog post here](https://www.anthropic.com/news/claude-4)
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: Anthropic: Claude Sonnet 4
Vendor: Anthropic
Description: Claude Sonnet 4 significantly enhances the capabilities of its predecessor, Sonnet 3.7, excelling in both coding and reasoning tasks with improved precision and controllability. Achieving state-of-the-art performance on SWE-bench (72.7%), Sonnet 4 balances capability and computational efficiency, making it suitable for a broad range of applications from routine coding tasks to complex software development projects. Key enhancements include improved autonomous codebase navigation, reduced error rates in agent-driven workflows, and increased reliability in following intricate instructions. Sonnet 4 is optimized for practical everyday use, providing advanced reasoning capabilities while maintaining efficiency and responsiveness in diverse internal and external scenarios.

Read more at the [blog post here](https://www.anthropic.com/news/claude-4)
Context length: 1000000 tokens
Tokenizer: Claude
Modality: text+image->text
Input modalities: image, text, 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): 0.0048
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: Mistral: Devstral Small 2505
Vendor: Mistral AI
Hugging Face ID: mistralai/Devstral-Small-2505
Description: Devstral-Small-2505 is a 24B parameter agentic LLM fine-tuned from Mistral-Small-3.1, jointly developed by Mistral AI and All Hands AI for advanced software engineering tasks. It is optimized for codebase exploration, multi-file editing, and integration into coding agents, achieving state-of-the-art results on SWE-Bench Verified (46.8%).

Devstral supports a 128k context window and uses a custom Tekken tokenizer. It is text-only, with the vision encoder removed, and is suitable for local deployment on high-end consumer hardware (e.g., RTX 4090, 32GB RAM Macs). Devstral is best used in agentic workflows via the OpenHands scaffold and is compatible with inference frameworks like vLLM, Transformers, and Ollama. It is released under the Apache 2.0 license.
Context length: 128000 tokens
Tokenizer: Mistral
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.06
Completion price per 1M tokens (USD): 0.12
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.09
Output/input cost ratio: 2.0
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: Google: Gemma 3n 4B (free)
Vendor: Google
Hugging Face ID: google/gemma-3n-E4B-it
Description: Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. It supports multimodal inputs—including text, visual data, and audio—enabling diverse tasks such as text generation, speech recognition, translation, and image analysis. Leveraging innovations like Per-Layer Embedding (PLE) caching and the MatFormer architecture, Gemma 3n dynamically manages memory usage and computational load by selectively activating model parameters, significantly reducing runtime resource requirements.

This model supports a wide linguistic range (trained in over 140 languages) and features a flexible 32K token context window. Gemma 3n can selectively load parameters, optimizing memory and computational efficiency based on the task or device capabilities, making it well-suited for privacy-focused, offline-capable applications and on-device AI solutions. [Read more in the blog post](https://developers.googleblog.com/en/introducing-gemma-3n/)
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: Google: Gemma 3n 4B
Vendor: Google
Hugging Face ID: google/gemma-3n-E4B-it
Description: Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. It supports multimodal inputs—including text, visual data, and audio—enabling diverse tasks such as text generation, speech recognition, translation, and image analysis. Leveraging innovations like Per-Layer Embedding (PLE) caching and the MatFormer architecture, Gemma 3n dynamically manages memory usage and computational load by selectively activating model parameters, significantly reducing runtime resource requirements.

This model supports a wide linguistic range (trained in over 140 languages) and features a flexible 32K token context window. Gemma 3n can selectively load parameters, optimizing memory and computational efficiency based on the task or device capabilities, making it well-suited for privacy-focused, offline-capable applications and on-device AI solutions. [Read more in the blog post](https://developers.googleblog.com/en/introducing-gemma-3n/)
Context length: 32768 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.02
Completion price per 1M tokens (USD): 0.04
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.03
Output/input cost ratio: 2.0
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, stop, temperature, top_k, top_p
Model: OpenAI: Codex Mini
Vendor: OpenAI
Description: codex-mini-latest is a fine-tuned version of o4-mini specifically for use in Codex CLI. For direct use in the API, we recommend starting with gpt-4.1.
Context length: 200000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 100000.0
Prompt price per 1M tokens (USD): 1.5
Completion price per 1M tokens (USD): 6.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 3.75
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: Meta: Llama 3.3 8B Instruct (free)
Vendor: Meta
Description: A lightweight and ultra-fast variant of Llama 3.3 70B, for use when quick response times are needed most.
Context length: 128000 tokens
Tokenizer: Llama3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 4028.0
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, repetition_penalty, response_format, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Nous: DeepHermes 3 Mistral 24B Preview
Vendor: Nous Research
Hugging Face ID: NousResearch/DeepHermes-3-Mistral-24B-Preview
Description: DeepHermes 3 (Mistral 24B Preview) is an instruction-tuned language model by Nous Research based on Mistral-Small-24B, designed for chat, function calling, and advanced multi-turn reasoning. It introduces a dual-mode system that toggles between intuitive chat responses and structured “deep reasoning” mode using special system prompts. Fine-tuned via distillation from R1, it supports structured output (JSON mode) and function call syntax for agent-based applications.

DeepHermes 3 supports a **reasoning toggle via system prompt**, allowing users to switch between fast, intuitive responses and deliberate, multi-step reasoning. When activated with the following specific system instruction, the model enters a *"deep thinking"* mode—generating extended chains of thought wrapped in `<think></think>` tags before delivering a final answer. 

System Prompt: You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.

Context length: 32768 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.15
Completion price per 1M tokens (USD): 0.59
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.37
Output/input cost ratio: 3.93
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: Mistral: Mistral Medium 3
Vendor: Mistral AI
Description: Mistral Medium 3 is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases.

The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.
Context length: 131072 tokens
Tokenizer: Mistral
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Prompt price per 1M tokens (USD): 0.3999999999999999
Completion price per 1M tokens (USD): 2.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.2
Output/input cost ratio: 5.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: Google: Gemini 2.5 Pro Preview 05-06
Vendor: Google
Description: Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.
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): 1.25
Completion price per 1M tokens (USD): 10.0
Image price (USD): 0.00516
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, stop, structured_outputs, temperature, tool_choice, tools, top_p
Model: Arcee AI: Spotlight
Vendor: Arcee AI
Description: Spotlight is a 7‑billion‑parameter vision‑language model derived from Qwen 2.5‑VL and fine‑tuned by Arcee AI for tight image‑text grounding tasks. It offers a 32 k‑token context window, enabling rich multimodal conversations that combine lengthy documents with one or more images. Training emphasized fast inference on consumer GPUs while retaining strong captioning, visual‐question‑answering, and diagram‑analysis accuracy. As a result, Spotlight slots neatly into agent workflows where screenshots, charts or UI mock‑ups need to be interpreted on the fly. Early benchmarks show it matching or out‑scoring larger VLMs such as LLaVA‑1.6 13 B on popular VQA and POPE alignment tests. 
Context length: 131072 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Max completion tokens (top provider): 65537.0
Prompt price per 1M tokens (USD): 0.18
Completion price per 1M tokens (USD): 0.18
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.18
Output/input cost ratio: 1.0
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, stop, temperature, top_k, top_p
Model: Arcee AI: Maestro Reasoning
Vendor: Arcee AI
Description: Maestro Reasoning is Arcee's flagship analysis model: a 32 B‑parameter derivative of Qwen 2.5‑32 B tuned with DPO and chain‑of‑thought RL for step‑by‑step logic. Compared to the earlier 7 B preview, the production 32 B release widens the context window to 128 k tokens and doubles pass‑rate on MATH and GSM‑8K, while also lifting code completion accuracy. Its instruction style encourages structured "thought → answer" traces that can be parsed or hidden according to user preference. That transparency pairs well with audit‑focused industries like finance or healthcare where seeing the reasoning path matters. In Arcee Conductor, Maestro is automatically selected for complex, multi‑constraint queries that smaller SLMs bounce. 
Context length: 131072 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): 0.8999999999999999
Completion price per 1M tokens (USD): 3.3000000000000003
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 2.1
Output/input cost ratio: 3.67
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, stop, temperature, top_k, top_p
Model: Arcee AI: Virtuoso Large
Vendor: Arcee AI
Description: Virtuoso‑Large is Arcee's top‑tier general‑purpose LLM at 72 B parameters, tuned to tackle cross‑domain reasoning, creative writing and enterprise QA. Unlike many 70 B peers, it retains the 128 k context inherited from Qwen 2.5, letting it ingest books, codebases or financial filings wholesale. Training blended DeepSeek R1 distillation, multi‑epoch supervised fine‑tuning and a final DPO/RLHF alignment stage, yielding strong performance on BIG‑Bench‑Hard, GSM‑8K and long‑context Needle‑In‑Haystack tests. Enterprises use Virtuoso‑Large as the "fallback" brain in Conductor pipelines when other SLMs flag low confidence. Despite its size, aggressive KV‑cache optimizations keep first‑token latency in the low‑second range on 8× H100 nodes, making it a practical production‑grade powerhouse.
Context length: 131072 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 64000.0
Prompt price per 1M tokens (USD): 0.75
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.975
Output/input cost ratio: 1.6
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, stop, temperature, tool_choice, tools, top_k, top_p
Model: Arcee AI: Coder Large
Vendor: Arcee AI
Description: Coder‑Large is a 32 B‑parameter offspring of Qwen 2.5‑Instruct that has been further trained on permissively‑licensed GitHub, CodeSearchNet and synthetic bug‑fix corpora. It supports a 32k context window, enabling multi‑file refactoring or long diff review in a single call, and understands 30‑plus programming languages with special attention to TypeScript, Go and Terraform. Internal benchmarks show 5–8 pt gains over CodeLlama‑34 B‑Python on HumanEval and competitive BugFix scores thanks to a reinforcement pass that rewards compilable output. The model emits structured explanations alongside code blocks by default, making it suitable for educational tooling as well as production copilot scenarios. Cost‑wise, Together AI prices it well below proprietary incumbents, so teams can scale interactive coding without runaway spend. 
Context length: 32768 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): 0.7999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.6499999999999999
Output/input cost ratio: 1.6
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, stop, temperature, top_k, top_p
Model: Microsoft: Phi 4 Reasoning Plus
Vendor: Microsoft
Hugging Face ID: microsoft/Phi-4-reasoning-plus
Description: Phi-4-reasoning-plus is an enhanced 14B parameter model from Microsoft, fine-tuned from Phi-4 with additional reinforcement learning to boost accuracy on math, science, and code reasoning tasks. It uses the same dense decoder-only transformer architecture as Phi-4, but generates longer, more comprehensive outputs structured into a step-by-step reasoning trace and final answer.

While it offers improved benchmark scores over Phi-4-reasoning across tasks like AIME, OmniMath, and HumanEvalPlus, its responses are typically ~50% longer, resulting in higher latency. Designed for English-only applications, it is well-suited for structured reasoning workflows where output quality takes priority over response speed.
Context length: 32768 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.07
Completion price per 1M tokens (USD): 0.35
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.21
Output/input cost ratio: 5.0
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: Inception: Mercury Coder
Vendor: Inception
Description: Mercury Coder is the first diffusion large language model (dLLM). Applying a breakthrough discrete diffusion approach, the model runs 5-10x faster than even speed optimized models like Claude 3.5 Haiku and GPT-4o Mini while matching their performance. Mercury Coder's speed means that developers can stay in the flow while coding, enjoying rapid chat-based iteration and responsive code completion suggestions. On Copilot Arena, Mercury Coder ranks 1st in speed and ties for 2nd in quality. Read more in the [blog post here](https://www.inceptionlabs.ai/blog/introducing-mercury).
Context length: 128000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 16384.0
Prompt price per 1M tokens (USD): 0.25
Completion price per 1M tokens (USD): 1.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.625
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 4B (free)
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-4B
Description: Qwen3-4B is a 4 billion parameter dense language model from the Qwen3 series, designed to support both general-purpose and reasoning-intensive tasks. It introduces a dual-mode architecture—thinking and non-thinking—allowing dynamic switching between high-precision logical reasoning and efficient dialogue generation. This makes it well-suited for multi-turn chat, instruction following, and complex agent workflows.
Context length: 40960 tokens
Tokenizer: Qwen3
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, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: DeepSeek: DeepSeek Prover V2
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-Prover-V2-671B
Description: DeepSeek Prover V2 is a 671B parameter model, speculated to be geared towards logic and mathematics. Likely an upgrade from [DeepSeek-Prover-V1.5](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-RL) Not much is known about the model yet, as DeepSeek released it on Hugging Face without an announcement or description.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.5
Completion price per 1M tokens (USD): 2.18
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.34
Output/input cost ratio: 4.36
Supported parameters: frequency_penalty, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, temperature, top_k, top_p
Model: Meta: Llama Guard 4 12B
Vendor: Meta
Hugging Face ID: meta-llama/Llama-Guard-4-12B
Description: Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM—generating text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Llama Guard 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities. Specifically, it combines features from previous Llama Guard models, providing content moderation for English and multiple supported languages, along with enhanced capabilities to handle mixed text-and-image prompts, including multiple images. Additionally, Llama Guard 4 is integrated into the Llama Moderations API, extending robust safety classification to text and images.
Context length: 163840 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text
Output modalities: text
Prompt price per 1M tokens (USD): 0.18
Completion price per 1M tokens (USD): 0.18
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.18
Output/input cost ratio: 1.0
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, temperature, top_k, top_p
Model: Qwen: Qwen3 30B A3B (free)
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-30B-A3B
Description: Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance.

Significantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.
Context length: 40960 tokens
Tokenizer: Qwen3
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