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.
Parameters
24B
parameters
Release date
30 January 2025
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud📱 On-device
Overview
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 use✓ Fine-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
