Model Releases
Mistral AI Unveils Magistral Small: 24B Reasoning Powerhouse
Mistral AI releases Magistral Small, a 24B parameter reasoning model with extended thinking capabilities, open-sourced under Apache 2.0.
June 5, 2025
Model ReleaseMagistral Small
Introduction
Mistral AI has officially announced the release of Magistral Small on June 5, 2025, marking a pivotal moment for open-source AI infrastructure. This 24-billion parameter model represents a significant leap in reasoning capabilities compared to previous iterations in the Small series. Unlike standard chat models, Magistral Small is specifically engineered to handle complex logical tasks through extended thinking processes.
The release comes amidst a competitive landscape where hardware efficiency is paramount. By optimizing for cost-sensitive use cases without sacrificing intelligence, Mistral positions this model as a viable alternative to proprietary giants. Developers can now access high-performance reasoning tools without the heavy licensing costs associated with closed-source models.
- Release Date: 2025-06-05
- Model Type: Reasoning Model
- Open Source: Yes (Apache 2.0)
Key Features & Architecture
Magistral Small utilizes a hybrid MoE (Mixture of Experts) architecture designed to balance compute efficiency with high-quality output generation. The model features a native context window of 128K tokens, allowing it to process extensive documentation and codebases in a single pass. A standout feature is its 'extended thinking' mode, which allows the model to draft intermediate reasoning steps before finalizing an answer.
Licensing remains a critical factor for enterprise adoption. Mistral has chosen the Apache 2.0 license for Magistral Small, ensuring that the weights are fully open for commercial use, modification, and distribution. This decision democratizes access to advanced reasoning capabilities while maintaining the sovereignty of European AI development.
- Parameters: 24B
- Context Window: 128K
- License: Apache 2.0
- Capability: Extended Thinking
Performance & Benchmarks
In terms of raw performance, Magistral Small demonstrates exceptional strength in STEM domains. It achieves a score of 85.4 on the MMLU benchmark, outperforming several proprietary models in its class. The model's reasoning engine is particularly robust in mathematical problem-solving and logical deduction tasks, where it scores significantly higher than baseline small language models.
Code generation and evaluation metrics also show impressive results. On HumanEval, the model achieves a pass rate of 88%, indicating strong capability for software engineering tasks. Furthermore, in the SWE-bench evaluation, Magistral Small successfully resolves complex software issues, proving its utility for developer-centric workflows.