Mistral AI’s first reasoning model (10 Jun 2025): transparent, multilingual chain-of-thought. Variants: Small 24B (Apache 2.0) and Medium (enterprise).
Parameters
24B (Magistral Small); Medium – nieujawnione
parameters
Release date
10 June 2025
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud
Overview
Access & deployment
DownloadAPIHosted
LocalCloud
Weights: Open weights
Key parameters
🧩 Parameters: 24B (Magistral Small); Medium – nieujawnione
✓ Fine-tuning
📥 Input: text
Technical specification
Parameters
24B (Magistral Small); Medium – nieujawnione
parameters
License
Apache 2.0 (Magistral Small); własnościowa (Magistral Medium)
Hardware requirements
Magistral Small has open weights (Apache 2.0) on Hugging Face and can be self-deployed (24B parameters — a GPU with sufficient VRAM). Magistral Medium (proprietary) is available via the Mistral API (la Plateforme), Le Chat and clouds (Amazon SageMaker, with announced IBM watsonx, Azure AI and Google Cloud).
Features:✓ Fine-tuning
Modalities
⬇ Input
text
⬆ Output
textcode
Capabilities and applications
Native model capabilities
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Advanced reasoning
The ability to perform multi-step, structured reasoning: analysing problems, planning steps, and drawing conclusions from hypotheses. Reasoning-first models (e.g. GPT-5.1 Thinking) dedicate a portion of inference to chains of thought before responding.
Category: reasoning
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Mathematical reasoning
The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.
Category: reasoning
Extended thinking mode
A reasoning-model variant with a larger inference budget: more thinking cycles, higher answer precision at the cost of response time. Choice between 'standard' and 'extended' thinking is left to the user (e.g. the selector in GPT-5.2 Pro).
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
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
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
Language modeling
Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.
Category: language
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Benchmark results
2 benchmarks
AIME 2024
accuracy · pass@1 (Magistral Medium)
73.6%
📄 Mistral AI — Magistral (mistral.ai/news/magistral)
90% with majority voting (maj@64).
AIME 2024
accuracy · pass@1 (Magistral Small)
70.7%
📄 Mistral AI — Magistral (mistral.ai/news/magistral)
83.3% with majority voting (maj@64).
Technical architecture
Core Architecture
Training Techniques
