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Mistral Small 3.1 - The Latest Compact LLM Combining Performance and Versatility

In March 2025, Mistral announced its new open-source LLM (Local Language Model), Mistral Small 3.1. Despite its 'small' designation, this model has garnered attention as a heavyweight compact model that takes both performance and versatility into account. In this article, we will discuss the structure, usability, and comparisons with competing models of Mistral Small 3.1.

🔍 Overview

Item Details
Model Name Mistral Small 3.1
Number of Parameters Approximately 24 billion (24B)
License Apache 2.0 (commercial use allowed)
Release Date March 2025
Supported Platforms Hugging Face, Ollama, Vertex AI, AWS, Google Cloud, etc.
Input Format Text and images (multi-modal support)
Maximum Context Length 128,000 tokens

Mistral Small 3.1 may be labeled as "lightweight", but it demands computing power that classifies it as a high-performance general AI.

⚙️ Features and Technical Strengths

1. Open Source & Free for Commercial Use

  • Thanks to the Apache 2.0 license, businesses and developers can freely integrate it into commercial services.
  • Easily accessible on platforms like Hugging Face and Ollama.

2. Multi-modal Input Support

  • Can handle not just text inputs, but also image inputs, making it suitable for chatbots, analytics tools, customer support bots, etc.

3. Up to 128K Context Window

  • Well-suited for handling long documents or conversation histories, advantageous for complex analysis, generating lengthy explanations, and maintaining coding contexts.

4. Relatively Low Execution Environment Requirements

  • Can run on a single RTX 4090 or a 32GB RAM Mac M2/M3 machine.
  • Of course, it is not an absolutely lightweight model, and the RTX 4090 is a very high-end piece of equipment.

5. Supports Both Local and Cloud Deployment

  • Supports local execution for individual developers and cloud deployment for enterprises.
  • Integrates with cloud ecosystems like Google Vertex AI, AWS Bedrock, Azure AI Foundry, etc.

6. Strengths in STEM and Coding Areas

  • Shows high precision and accuracy in handling mathematics, science, and programming languages.

📊 Performance and Benchmark Comparison

Mistral Small 3.1 competes with Gemma 2B, GPT-4o Mini, Claude 3 Sonnet, Command R, among others, across various benchmarks. It consistently records high performance within the compact model category.

Key Benchmark Scores (Comparison Metrics: MMLU / GSM8K / HumanEval, etc.)

Model MMLU (%) GSM8K (%) HumanEval (%) Average Performance Level
GPT-4o Mini Approximately 81 88 74 High
Claude 3 Sonnet Approximately 84 90 77 Very High
Mistral Small 3.1 Approximately 79 86 72 High
Phi-3 Mini Approximately 73 80 65 Medium High
Gemma 2B Approximately 68 75 58 Medium Low

⚠️ The numbers are referenced benchmark values based on public sources and may vary depending on the inference environment.

Mistral Small 3.1 particularly excels in the STEM areas (mathematics, coding, science, etc.) and is suitable for applications where context maintenance is needed due to its high token length support.

Conclusion

Mistral Small 3.1 is: - A high-performance model that can be run on mid-range GPUs, - Capable of handling text + image multi-modality, - Supports 128K long contexts, - A versatile LLM that can be freely used as open source.

If you are looking for a small AI model applicable to real-world scenarios, Mistral Small 3.1 is a very powerful option.