Mistral AI Challenges with Magistral – Launches Open Reasoning AI Model
French company Mistral AI has unveiled Magistral, its first model series focused on advanced reasoning. The series includes an open model with 24 billion parameters under the Apache 2.0 license.
What happened?
French AI company Mistral AI has launched its new model series, Magistral, which focuses on advanced reasoning. The series comprises two models: the proprietary enterprise model Magistral Medium and the open model Magistral Small with 24 billion parameters. The open variant is released under the Apache 2.0 license and is available on Hugging Face.
Key facts
| Lanseringsdatum | 10 juni 2025 |
|---|---|
| Öppen modell | Magistral Small (24B) |
| Licens för Small | Apache 2.0 |
| AIME2024 (Medium/Majority) | 90 % |
| AIME2024 (Small/Majority) | 83,3 % |
Why it matters
The launch marks a significant acceleration in the development of reasoning AI models, a field previously dominated by players such as OpenAI and DeepSeek. In evaluations such as the AIME2024 mathematics test, Magistral shows strong results (up to 90% with majority voting for Medium and 83.3% for Small), while also performing well in GPQA Diamond and LiveCodeBench.
Who is affected?
The launch primarily concerns AI developers, researchers, and enterprises that require models for complex problem-solving and coding. Magistral Small allows organisations to run a high-performance reasoning model locally without sharing data with external cloud services.
Impact on the EU
Magistral Small is covered by the open Apache 2.0 license, giving European developers and companies full freedom to run and fine-tune the model on their own infrastructure in compliance with the EU AI Act and GDPR. Magistral Medium is available in the EU via APIs and the Le Chat interface.
What else you should know
Mistral AI continues its strategy of combining commercial API models with open weights. In conjunction with the launch, the company also published a research paper detailing the architecture and training methodology behind the models' reasoning capabilities.
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