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      Mistral

      Browse models provided by Mistral (Terms of Service)

      19 models

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      • Mistral: Mistral Medium 3

        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.

        by mistralai131K context$0.40/M input tokens$2/M output tokens
      • Mistral: Ministral 8B

        Ministral 8B is a state-of-the-art language model optimized for on-device and edge computing. Designed for efficiency in knowledge-intensive tasks, commonsense reasoning, and function-calling, it features a specialized interleaved sliding-window attention mechanism, enabling faster and more memory-efficient inference. Ministral 8B excels in local, low-latency applications such as offline translation, smart assistants, autonomous robotics, and local analytics. The model supports up to 128k context length and can function as a performant intermediary in multi-step agentic workflows, efficiently handling tasks like input parsing, API calls, and task routing. It consistently outperforms comparable models like Mistral 7B across benchmarks, making it particularly suitable for compute-efficient, privacy-focused scenarios.

        by mistral131K context$0.10/M input tokens$0.10/M output tokens
      • Mistral: Mistral Small 3.1 24B

        Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. It provides state-of-the-art performance in text-based reasoning and vision tasks, including image analysis, programming, mathematical reasoning, and multilingual support across dozens of languages. Equipped with an extensive 128k token context window and optimized for efficient local inference, it supports use cases such as conversational agents, function calling, long-document comprehension, and privacy-sensitive deployments.

        by mistralai128K context$0.10/M input tokens$0.30/M output tokens$0.926/K input imgs
      • Mistral: Saba

        Mistral Saba is a 24B-parameter language model specifically designed for the Middle East and South Asia, delivering accurate and contextually relevant responses while maintaining efficient performance. Trained on curated regional datasets, it supports multiple Indian-origin languages—including Tamil and Malayalam—alongside Arabic. This makes it a versatile option for a range of regional and multilingual applications. Read more at the blog post here

        by mistralai32K context$0.20/M input tokens$0.60/M output tokens
      • Mistral: Mistral Small 3

        Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed for efficient local deployment. The model achieves 81% accuracy on the MMLU benchmark and performs competitively with larger models like Llama 3.3 70B and Qwen 32B, while operating at three times the speed on equivalent hardware. Read the blog post about the model here.

        by mistralai33K context$0.10/M input tokens$0.30/M output tokens
      • Mistral: Codestral 2501

        Mistral's cutting-edge language model for coding. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. Learn more on their blog post: https://mistral.ai/news/codestral-2501/

        by mistralai256K context$0.30/M input tokens$0.90/M output tokens
      • Mistral Large 2411

        Mistral Large 2 2411 is an update of Mistral Large 2 released together with Pixtral Large 2411 It provides a significant upgrade on the previous Mistral Large 24.07, with notable improvements in long context understanding, a new system prompt, and more accurate function calling.

        by mistralai128K context$2/M input tokens$6/M output tokens
      • Mistral Large 2407

        This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement here. It supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash. Its long context window allows precise information recall from large documents.

        by mistralai128K context$2/M input tokens$6/M output tokens
      • Mistral: Pixtral Large 2411

        Pixtral Large is a 124B parameter, open-weight, multimodal model built on top of Mistral Large 2. The model is able to understand documents, charts and natural images. The model is available under the Mistral Research License (MRL) for research and educational use, and the Mistral Commercial License for experimentation, testing, and production for commercial purposes.

        by mistralai128K context$2/M input tokens$6/M output tokens$2.888/K input imgs
      • Mistral: Ministral 8B

        Ministral 8B is an 8B parameter model featuring a unique interleaved sliding-window attention pattern for faster, memory-efficient inference. Designed for edge use cases, it supports up to 128k context length and excels in knowledge and reasoning tasks. It outperforms peers in the sub-10B category, making it perfect for low-latency, privacy-first applications.

        by mistralai128K context$0.10/M input tokens$0.10/M output tokens
      • Mistral: Ministral 3B

        Ministral 3B is a 3B parameter model optimized for on-device and edge computing. It excels in knowledge, commonsense reasoning, and function-calling, outperforming larger models like Mistral 7B on most benchmarks. Supporting up to 128k context length, it’s ideal for orchestrating agentic workflows and specialist tasks with efficient inference.

        by mistralai128K context$0.04/M input tokens$0.04/M output tokens
      • Mistral: Pixtral 12B

        The first multi-modal, text+image-to-text model from Mistral AI. Its weights were launched via torrent: https://x.com/mistralai/status/1833758285167722836.

        by mistralai4K context$0.15/M input tokens$0.15/M output tokens$0.217/K input imgs
      • Mistral: Mistral Nemo

        A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi. It supports function calling and is released under the Apache 2.0 license.

        by mistralai131K context$0.15/M input tokens$0.15/M output tokens
      • Mistral: Codestral Mamba

        A 7.3B parameter Mamba-based model designed for code and reasoning tasks. - Linear time inference, allowing for theoretically infinite sequence lengths - 256k token context window - Optimized for quick responses, especially beneficial for code productivity - Performs comparably to state-of-the-art transformer models in code and reasoning tasks - Available under the Apache 2.0 license for free use, modification, and distribution

        by mistralai256K context$0.25/M input tokens$0.25/M output tokens
      • Mistral: Mixtral 8x22B Instruct

        Mistral's official instruct fine-tuned version of Mixtral 8x22B. It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include: - strong math, coding, and reasoning - large context length (64k) - fluency in English, French, Italian, German, and Spanish See benchmarks on the launch announcement here. #moe

        by mistralai66K context$2/M input tokens$6/M output tokens
      • Mistral Large

        This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement here. It supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash. Its long context window allows precise information recall from large documents.

        by mistralai128K context$2/M input tokens$6/M output tokens
      • Mistral Medium

        This is Mistral AI's closed-source, medium-sided model. It's powered by a closed-source prototype and excels at reasoning, code, JSON, chat, and more. In benchmarks, it compares with many of the flagship models of other companies.

        by mistralai32K context$2.75/M input tokens$8.10/M output tokens
      • Mistral Tiny

        Note: This model is being deprecated. Recommended replacement is the newer Ministral 8B This model is currently powered by Mistral-7B-v0.2, and incorporates a "better" fine-tuning than Mistral 7B, inspired by community work. It's best used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial.

        by mistralai32K context$0.25/M input tokens$0.25/M output tokens
      • Mistral Small

        With 22 billion parameters, Mistral Small v24.09 offers a convenient mid-point between (Mistral NeMo 12B)[/mistralai/mistral-nemo] and (Mistral Large 2)[/mistralai/mistral-large], providing a cost-effective solution that can be deployed across various platforms and environments. It has better reasoning, exhibits more capabilities, can produce and reason about code, and is multiligual, supporting English, French, German, Italian, and Spanish.

        by mistralai32K context$0.20/M input tokens$0.60/M output tokens