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.

Pretrain Evals

Model	MMLU (5-shot)	MMLU Pro (5-shot CoT)	TriviaQA	GPQA Main (5-shot CoT)	MMMU
Small 3.1 24B Base	81.01%	56.03%	80.50%	37.50%	59.27%
Gemma 3 27B PT	78.60%	52.20%	81.30%	24.30%	56.10%

Instruction Evals

Text

Model	MMLU	MMLU Pro (5-shot CoT)	MATH	GPQA Main (5-shot CoT)	GPQA Diamond (5-shot CoT )	MBPP	HumanEval	SimpleQA (TotalAcc)
Small 3.1 24B Instruct	80.62%	66.76%	69.30%	44.42%	45.96%	74.71%	88.41%	10.43%
Gemma 3 27B IT	76.90%	67.50%	89.00%	36.83%	42.40%	74.40%	87.80%	10.00%
GPT4o Mini	82.00%	61.70%	70.20%	40.20%	39.39%	84.82%	87.20%	9.50%
Claude 3.5 Haiku	77.60%	65.00%	69.20%	37.05%	41.60%	85.60%	88.10%	8.02%
Cohere Aya-Vision 32B	72.14%	47.16%	41.98%	34.38%	33.84%	70.43%	62.20%	7.65%

Vision

Model	MMMU	MMMU PRO	Mathvista	ChartQA	DocVQA	AI2D	MM MT Bench
Small 3.1 24B Instruct	64.00%	49.25%	68.91%	86.24%	94.08%	93.72%	7.3
Gemma 3 27B IT	64.90%	48.38%	67.60%	76.00%	86.60%	84.50%	7
GPT4o Mini	59.40%	37.60%	56.70%	76.80%	86.70%	88.10%	6.6
Claude 3.5 Haiku	60.50%	45.03%	61.60%	87.20%	90.00%	92.10%	6.5
Cohere Aya-Vision 32B	48.20%	31.50%	50.10%	63.04%	72.40%	82.57%	4.1

Multilingual Evals

Model	Average	European	East Asian	Middle Eastern
Small 3.1 24B Instruct	71.18%	75.30%	69.17%	69.08%
Gemma 3 27B IT	70.19%	74.14%	65.65%	70.76%
GPT4o Mini	70.36%	74.21%	65.96%	70.90%
Claude 3.5 Haiku	70.16%	73.45%	67.05%	70.00%
Cohere Aya-Vision 32B	62.15%	64.70%	57.61%	64.12%

Long Context Evals

Model	LongBench v2	RULER 32K	RULER 128K
Small 3.1 24B Instruct	37.18%	93.96%	81.20%
Gemma 3 27B IT	34.59%	91.10%	66.00%
GPT4o Mini	29.30%	90.20%	65.8%
Claude 3.5 Haiku	35.19%	92.60%	91.90%

Basic Instruct Template (V7-Tekken)

<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]

<system_prompt>, <user message> and <assistant response> are placeholders.

Please make sure to use mistral-common as the source of truth

Usage

The model can be used with the following frameworks;

• vllm (recommended): See here

Note 1: We recommend using a relatively low temperature, such as temperature=0.15.

Note 2: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend the following system prompt:

system_prompt = """You are Mistral Small 3.1, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
You power an AI assistant called Le Chat.
Your knowledge base was last updated on 2023-10-01.
The current date is {today}.

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