Model Card for Mistral-Small-3.1-24B-Instruct-2503

Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks. This model is an instruction-finetuned version of: Mistral-Small-3.1-24B-Base-2503.

Mistral Small 3.1 can be deployed locally and is exceptionally “knowledge-dense,” fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.

It is ideal for: - Fast-response conversational agents. - Low-latency function calling. - Subject matter experts via fine-tuning. - Local inference for hobbyists and organizations handling sensitive data. - Programming and math reasoning. - Long document understanding. - Visual understanding.

For enterprises requiring specialized capabilities (increased context, specific modalities, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.

Learn more about Mistral Small 3.1 in our blog post.

Key Features Vision: Vision capabilities enable the model to analyze images and provide insights based on visual content in addition to text. Multilingual: Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi. Agent-Centric: Offers best-in-class agentic capabilities with native function calling and JSON outputting. Advanced Reasoning: State-of-the-art conversational and reasoning capabilities. Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. Context Window: A 128k context window. System Prompt: Maintains strong adherence and support for system prompts. Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. Benchmark Results When available, we report numbers previously published by other model providers, otherwise we re-evaluate them using our own evaluation harness.