Mistral Small 3.1
Version: 1
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Key capabilities
About this model
Mistral Small 3.1 (25.03) is a versatile model designed for various tasks such as programming, mathematical reasoning, document understanding, and dialogue. In addition to its strong multimodal capabilities, Mistral Small 3.1 (25.03) retains the robust text performance of Mistral Small 3. It excels in knowledge benchmarks like MMLU and MMLU-Pro, graduate-level question answering (GPQA), reading comprehension (TriviaQA), and math and coding tasks (MATH, HumanEval). Mistral Small 3.1 (25.03) often matches or outperforms much larger models, including 70B parameter Llama models, as well as closed-source models like GPT4o mini and Claude 3.5 Haiku. With our improved training methodologies, we observe strong vision capabilities in the model. Not only does Mistral Small 3.1 (25.03) outperforms light-weight models like GPT4o mini, it also rivals larger models like qwen-vl-2 on visual knowledge and reasoning tasks. Moreover, with our innovations in the past few months, Mistral Small 3.1 (25.03) demonstrates performance on par with Pixtral Large we released last year across the board.Key model capabilities
Mistral Small 3.1 (25.03) is a great versatile model for tasks such as:- Programming
- Math reasoning
- Dialogue
- Long document understanding
- Visual understanding
- Summarization
- Low-latency applications
Use cases
See Responsible AI for additional considerations for responsible use.Key use cases
Mistral Small 3.1 (25.03) is a great versatile model for tasks such as programming, mathematical reasoning, dialogue, long document understanding, visual understanding, summarization, and low-latency applications. Mistral Small 3.1 (25.03) was designed with low-latency applications in mind and delivers best-in-class efficiency compared to models of the same quality.Out of scope use cases
The provider has not supplied this information.Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
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The provider has not supplied this information.Input formats
Mistral Small 3.1 (25.03) can process and understand visual inputs as well as long documents.Output formats
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The provider has not supplied this information.Long context
Mistral Small 3.1 (25.03) features an extended context length of up to 128k. It can now process and understand long documents, further expanding its range of applications. Mistral Small 3.1 (25.03) is our best generalist model for long-context tasks. It demonstrates 100% retrieval capability on passkey evaluations up to 128k context. Compared to both closed-source and open-source competitor models, Mistral Small 3.1 (25.03) excels in question-answering over long documents (LongBench v2) and in reasoning over entire contexts with challenging latent structure evaluations (Michelangelo Latent List). Most notably, Mistral Small 3.1 (25.03) improves upon Mistral Large in long-context capabilities. NOTE: For competitors, we run all evals using our stack as they did not report these evals| Model | Michelangelo Latent List 128k | LongBench v2 128k |
|---|---|---|
| Mistral Small 3.1 (25.03) Instruct | 23.59% | 36.78% |
| GPT4o mini (up to 128k context) | 10.53% | 29.30% |
| Gemini 2.0 Flash-Lite (up to 1M context) | 25.22% | - |
| Qwen2.5 7B Instruct (with YaRN) (up to 128k context) | - | 30% |
| Qwen2.5 32B Instruct (with YaRN) (up to 128k context) | 31.66% | - |
| Qwen2.5 72B Instruct (with YaRN) (up to 128k context) | - | 42.10% |
| Llama 3.3 70B Instruct (up to 128k context) | 3.98% | 29.80% |
| Mistral Large (24.11) | 15.85% | 34.40% |
Optimizing model performance
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Training, testing and validation
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Distribution channels
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Safety techniques
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Acceptable use policy
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Source: Mistral AI Vision Evals With our improved training methodologies, we observe strong vision capabilities in the model. Not only does Mistral Small 3.1 (25.03) outperforms light-weight models like GPT4o mini, it also rivals larger models like qwen-vl-2 on visual knowledge and reasoning tasks. Moreover, with our innovations in the past few months, Mistral Small 3.1 (25.03) demonstrates performance on par with Pixtral Large we released last year across the board.| Model | MMMU | MMMU Pro | Mathvista | ChartQA | DocVQA | AI2D |
|---|---|---|---|---|---|---|
| Mistral Small 3.1 (25.03) Instruct | 64.00 | 49.25 | 68.91 | 86.24 | 94.08 | 93.72 |
| GPT4o mini | 60.00 | 37.60 | 52.50 | - | - | - |
| Qwen2-VL 7B | 54.10 | 30.50 | 58.20 | 83.00 | 94.50 | 83.00 |
| Qwen2.5-VL 7B | 58.60 | 38.30 | 68.20 | 87.30 | 95.70 | 83.90 |
| Qwen2-VL 72B | 64.50 | 46.20 | 70.50 | 88.30 | 96.50 | 88.10 |
| Qwen2.5-VL 72B | 70.20 | 51.10 | 74.80 | 89.50 | 96.40 | 88.70 |
| Claude 3.5 Haiku | 60.50 | - | 61.60 | 87.20 | 90.00 | 92.10 |
| Gemini 2.0 Flash-Lite | 68.00 | - | - | - | - | - |
| Pixtral Large | 64.00 | - | 69.40 | 88.10 | 93.30 | 93.80 |
| Model | MMLU (5-shot) | MMLU Pro (5-shot CoT) | GPQA Main (5-shot CoT) | TriviaQA (5-shot) |
|---|---|---|---|---|
| Mistral Small 3.1 (25.03) Base | 81.01% | 56.03% | 37.50% | 80.50% |
| Mistral Small 3 Base | 80.73% | 54.37% | 34.37% | 80.32% |
| Gemma 2 27B | 75.20% | - | - | 83.70% |
| Qwen 2.5 32B | 83.30% | 55.10% | 48.00% | - |
| Llama 3.1 70B | 79.30% | 53.80% | - | - |
| Model | MMLU Pro (5-shot CoT) | MATH | HumanEval | GPQA Main (5-shot CoT) |
|---|---|---|---|---|
| Mistral Small 3.1 (25.03) Instruct | 66.76% | 69.30% | 88.41% | 44.42% |
| Mistral Small 3 (25.01) Instruct | 66.30% | 70.60% | 84.80% | 45.30% |
| Gemma 2 27B Instruct | - | - | - | - |
| Qwen2.5 32B Instruct | 69.00% | 83.10% | 88.40% | 49.50% |
| Llama 3.3 70B Instruct | 68.90% | 77.00% | 88.40% | - |
| GPT4o mini | - | 70.20% | 87.20% | 40.20% |
| Claude 3.5 Haiku | 65.00% | 69.40% | 88.10% | - |
| Gemini 2.0 Flash-Lite | 71.60% | 86.80% | - | - |
| Model | Michelangelo Latent List 128k | LongBench v2 128k |
|---|---|---|
| Mistral Small 3.1 (25.03) Instruct | 23.59% | 36.78% |
| GPT4o mini (up to 128k context) | 10.53% | 29.30% |
| Gemini 2.0 Flash-Lite (up to 1M context) | 25.22% | - |
| Qwen2.5 7B Instruct (with YaRN) (up to 128k context) | - | 30% |
| Qwen2.5 32B Instruct (with YaRN) (up to 128k context) | 31.66% | - |
| Qwen2.5 72B Instruct (with YaRN) (up to 128k context) | - | 42.10% |
| Llama 3.3 70B Instruct (up to 128k context) | 3.98% | 29.80% |
| Mistral Large (24.11) | 15.85% | 34.40% |
Benchmarking methodology
Source: Mistral AI The provider has not supplied this information.Public data summary
Source: Mistral AI The provider has not supplied this information.Model Specifications
Context Length128000
Quality Index0.70
LicenseCustom
Training DataOctober 2023
Last UpdatedAugust 2025
Input TypeText,Image
Output TypeText
ProviderMistral AI
Languages27 Languages
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