Mistral Small 3.1 24B
Mistral's efficiency model. Context window: 128K tokens.
Scores by test
Methodology →What you need to know
Mistral Small 3.1 24B is optimized for high-volume long-context processing and structured data generation. It achieves a perfect 5/5 score in long context handling and strong 4/5 ratings in faithfulness, structured output, and multilingual capabilities. These metrics indicate the model is reliable for extracting information from large documents and adhering to specific formatting requirements.
The model's pricing is competitive, with a blended cost of $0.508/MTok, making it an affordable option for high-throughput tasks. However, this low cost comes with significant functional trade-offs. It fails in technical execution areas, scoring 1/5 in tool calling, tabular data processing, and safety calibration. It is not a viable candidate for agentic workflows that require external API interactions or precise data manipulation.
Overall performance is low, with an average internal score of 2.77/5.0 and the lowest overall rank among compared models. While it excels at reading and formatting, it struggles with persona consistency and creative problem solving.
Use this model if you need a low-cost solution for processing long documents, multilingual translation, or generating structured text. Skip this model if your application requires tool use, data analysis of tables, or strict safety guardrails.
Strengths — Top 3
Relative weaknesses — Bottom 3
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