Mistral AI

Devstral Medium builds upon the strengths of Devstral Small and takes performance to the next level with a score of 61.6% on SWE-Bench Verified. Devstral Medium is available through the Mistral public API, and offers exceptional performance at a competitive price point, making it an ideal choice for businesses and developers looking for a high-quality, cost-effective model.

Devstral Small 1.1 (also called devstral-small-2507) is based on the Mistral-Small-3.1 foundation model and contains approximately 24 billion parameters. It supports a 128k token context window, which allows it to handle multi-file code inputs and long prompts typical in software engineering workflows. The model is fine-tuned specifically for structured outputs, including XML and function-calling formats. This makes it compatible with agent frameworks such as OpenHands and suitable for tasks like program navigation, multi-step edits, and code search. It is licensed under Apache 2.0 and available for both research and commercial use.

Pretrained base model version of Mistral Small 3.1. Features improved text performance, multimodal understanding, multilingual capabilities, and an expanded 128k token context window compared to Mistral Small 3. Designed for fine-tuning.

Mistral Small 3 is a 24B-parameter LLM licensed under Apache-2.0. It focuses on low-latency, high-efficiency instruction following, maintaining performance comparable to larger models. It provides quick, accurate responses for conversational agents, function calling, and domain-specific fine-tuning. Suitable for local inference when quantized, it rivals models 2–3× its size while using significantly fewer compute resources.

The Ministral-8B-Instruct-2410 is an instruct fine-tuned model for local intelligence, on-device computing, and at-the-edge use cases, significantly outperforming existing models of similar size.

A 12B parameter multimodal model with a 400M parameter vision encoder, capable of understanding both natural images and documents. Excels at multimodal tasks while maintaining strong text-only performance. Supports variable image sizes and multiple images in context.

A state-of-the-art 12B multilingual model with a 128k context window, designed for global applications and strong in multiple languages.

An enterprise-grade 22B parameter model optimized for tasks like translation, summarization, and sentiment analysis. Offers significant improvements in human alignment, reasoning capabilities, and code generation compared to previous versions.

A 22B parameter code generation model trained on 80+ programming languages including Python, Java, C, C++, JavaScript, and Bash. Supports both instruction-following and fill-in-the-middle (FIM) capabilities for code completion and generation tasks.

A 124B parameter multimodal model built on top of Mistral Large 2, featuring frontier-level image understanding capabilities. Excels at understanding documents, charts, and natural images while maintaining strong text-only performance. Features a 123B multimodal decoder and 1B parameter vision encoder with a 128K context window supporting up to 30 high-resolution images.