Robots Atlas>ROBOTS ATLAS
Mistral Small 3

Mistral Small 3

Mistral Small 3 (mistral-small-2501, 24B) · Family: Mistral
Mistral AI’s latency-optimized model (24B, Apache 2.0): ~150 tok/s, 81% MMLU, matches Llama 3.3 70B while 3× faster; runs locally.
✓ Active✓ Public access⚖ Open sourceLLMTool-using model📁 Mistral
Parameters
24B
parameters
Release date
30 January 2025
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud📱 On-device

Overview

Mistral Small 3 is a latency-optimized 24-billion-parameter language model from Mistral AI, released on 30 January 2025 under the Apache 2.0 license (both the base and instruction-tuned versions). It was designed for fast conversational responses and low-latency function calling in task automation.

The model achieves 81% on the MMLU benchmark and generates about 150 tokens per second. According to Mistral AI, it matches the quality of the much larger Llama 3.3 70B while being more than 3× faster on the same hardware, and it is competitive with GPT-4o mini and Qwen 32B.

Thanks to its compact size, Mistral Small 3 can run locally — for example on a single NVIDIA RTX 4090 or a MacBook with 32 GB of memory — making it a good choice for local deployments, domain-specific fine-tuning and privacy-sensitive use cases.

The model is available with open weights (base and instruct versions) on Hugging Face, via the Mistral API (la Plateforme; mistral-small-latest / mistral-small-2501), and through partners: Ollama, Kaggle, Together AI, Fireworks AI and IBM watsonx (with announced support for NVIDIA NIM, Amazon SageMaker, Groq, Databricks and Snowflake, among others).

Classification
LLMTool-using model
Family: Mistral
Access & deployment
DownloadAPIHosted
LocalCloudOn-device
Weights: Open source
Key parameters
🧩 Parameters: 24B
Tools · ✓ Fine-tuning
📥 Input: text

Technical specification

Parameters
24B
parameters
License
Apache 2.0
Hardware requirements
An open-weights model (Apache 2.0) on Hugging Face. Thanks to its compact size (24B parameters, when quantized) it runs locally — e.g. on a single NVIDIA RTX 4090 or a MacBook with 32 GB RAM. Also available via the Mistral API (la Plateforme) and cloud partners.
Features:Tool useFine-tuning
Modalities
⬇ Input
text
⬆ Output
textcode

Capabilities and applications

Native model capabilities
Language modeling
Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.
Category: language
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Function Calling
Category: planning
Instruction following
Precisely following instructions contained in the prompt: response format, length, style, constraints (e.g. 'reply in six words'). GPT-5.1 significantly improved this capability compared to GPT-5.
Category: language
Multilingual
Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.
Category: language
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language

Benchmark results

1 benchmark
MMLU
accuracy · instruct version
81.0%
📄 Mistral AI — Mistral Small 3 (mistral.ai/news/mistral-small-3)

Technical architecture

Core Architecture
Training Techniques