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 specialized for high-fidelity processing of large datasets, distinguished by a perfect 5/5 score in long context handling and a 128K window. Its primary technical strengths lie in faithfulness and structured output, making it reliable for extracting specific information from long documents without introducing hallucinations.
The model fails significantly in operational autonomy and data manipulation. With a 1/5 score in tool calling and tabular data, it cannot be used for agentic workflows that require API interactions or the processing of spreadsheets. Furthermore, a 1/5 in safety calibration indicates a lack of robust guardrails, which may be a liability for public-facing applications.
At a blended cost of $0.504/MTok, the model is priced as a mid-tier option, but its performance is inconsistent. While it handles multilingual tasks and structured formatting well, its low overall rank (#130 of 130) and poor scores in persona consistency and creative problem solving suggest it lacks the general reasoning capabilities of its peers in this price bracket.
Use this model if you need a cost-effective way to summarize or extract structured data from very long texts where factual accuracy is critical. Skip this model if your project requires tool use, tabular analysis, strict safety filtering, or complex creative reasoning.
Strengths — Top 3
Relative weaknesses — Bottom 3
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