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Magistral

Magistral

Magistral Small (24B) / Magistral Medium · Family: Mistral
Mistral AI’s first reasoning model (10 Jun 2025): transparent, multilingual chain-of-thought. Variants: Small 24B (Apache 2.0) and Medium (enterprise).
✓ Active✓ Public access⚖ Open weightsLLMReasoning model📁 Mistral
Parameters
24B (Magistral Small); Medium – nieujawnione
parameters
Release date
10 June 2025
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud

Overview

Magistral is Mistral AI’s first reasoning model, released on 10 June 2025. It was designed for domain-specific, transparent and multilingual reasoning — it produces an explicit, multi-step chain-of-thought that can be traced in the user’s own language.

The model comes in two variants: Magistral Small (24B parameters, open weights under the Apache 2.0 license, self-deployable) and Magistral Medium (a more capable, proprietary enterprise variant). Magistral supports reasoning in many languages, including English, French, Spanish, German, Italian, Arabic, Russian and Simplified Chinese.

Magistral is available in Le Chat (a “Think” mode and “Flash Answers”), via the API on Mistral’s platform (la Plateforme, in preview), and on Amazon SageMaker (with announced support for IBM watsonx, Azure AI and Google Cloud). Magistral Small weights can be downloaded from Hugging Face. With “Flash Answers”, Magistral Medium achieves up to 10× higher token throughput in Le Chat than most competing solutions.

On the AIME 2024 math benchmark, Magistral Medium scores 73.6% (90% with majority voting maj@64), and Magistral Small 70.7% (83.3% with maj@64). The model was trained using reinforcement learning (including with verifiable rewards, RLVR), which developed its reasoning capabilities.

Classification
LLMReasoning model
Family: Mistral
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