Mistral-Small-3.1-24B-Instruct-2503 is a 24 billion parameter instruction-finetuned model by Mistral AI, building upon Mistral Small 3. It features state-of-the-art vision understanding and an extended 128k token context window, alongside strong text performance. This model is optimized for fast-response conversational agents, low-latency function calling, and local inference, supporting a wide range of multilingual and advanced reasoning tasks.
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Overview
Mistral-Small-3.1-24B-Instruct-2503, developed by Mistral AI, is a 24 billion parameter instruction-finetuned model that significantly enhances its predecessor, Mistral Small 3. It introduces state-of-the-art vision understanding and expands its long context capabilities up to 128k tokens without compromising text performance. The model is designed to be "knowledge-dense" and can be deployed locally, fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.
Key Capabilities
- Vision: Analyzes images and provides insights based on visual content in addition to text.
- Multilingual: Supports dozens of languages, including English, French, German, Japanese, Korean, Chinese, and Arabic.
- Agent-Centric: Offers strong agentic capabilities with native function calling and JSON outputting.
- Advanced Reasoning: Provides state-of-the-art conversational and reasoning abilities.
- Extended Context: Features a 128k context window for long document understanding.
- Apache 2.0 License: Allows for both commercial and non-commercial use and modification.
Benchmark Highlights
The model demonstrates competitive performance across various benchmarks:
- Text: Achieves 80.62% on MMLU, 88.41% on HumanEval, and 69.30% on MATH.
- Vision: Scores 64.00% on MMMU and 68.91% on Mathvista, outperforming several comparable models.
- Multilingual: Shows strong average performance at 71.18% across European, East Asian, and Middle Eastern languages.
- Long Context: Achieves 93.96% on RULER 32K, indicating robust long-context understanding.
Good For
- Fast-response conversational agents.
- Low-latency function calling.
- Local inference for hobbyists and organizations with sensitive data.
- Programming and math reasoning.
- Long document understanding and visual analysis.