Model Releases
Mistral Small 3.0: The Open-Source Frontier Model Arrives
Mistral AI releases Small 3.0 with Apache 2.0 licensing and state-of-the-art efficiency. Developers get a 24B parameter model challenging Big Tech rivals.
January 15, 2025
Model ReleaseMistral Small 3.0
Introduction
Mistral AI has officially unveiled the Mistral Small 3.0 model on January 15, 2025, marking a significant shift in the open-weight landscape. This release addresses the growing demand for efficient, high-performance models that rival proprietary giants without the licensing restrictions. For developers, this means a powerful tool for local deployment and fine-tuning that maintains Apache 2.0 compliance.
The industry hit a significant bend in the road toward artificial general intelligence in 2024, and Mistral is closing in on Big AI rivals with this new frontier. Unlike previous iterations, Small 3.0 is designed to offer state-of-the-art performance in a compact package. It is not just an update; it is a strategic move to democratize access to high-quality reasoning capabilities.
This model bridges the gap between the efficiency of small models and the intelligence of frontier models. By releasing under an Apache 2.0 license, Mistral ensures that the community can build upon this foundation freely. This sets a new standard for open-source AI development in 2025.
- Release Date: 2025-01-15
- License: Apache 2.0
- Parameters: 24 Billion
Key Features & Architecture
The architecture of Mistral Small 3.0 focuses on density and efficiency. It utilizes a 24B parameter count that balances computational cost with output quality. The model employs a mixture-of-experts (MoE) structure to activate only necessary components during inference, reducing latency significantly. This makes it ideal for edge devices and smaller clusters.
Context window capabilities have been expanded to support long-form reasoning tasks. The model handles complex documents and extended conversation histories without losing coherence. Additionally, the model includes native multimodal capabilities, allowing it to process text alongside structured data inputs effectively.
Key technical specifications include a maximum output token limit designed for practical application scenarios. The training data cutoff ensures the model is up-to-date with the latest technological advancements. Developers can expect a robust foundation for building custom agents and RAG pipelines.
- Parameters: 24B
- Context Window: 128k
- Architecture: MoE
- Multimodal: Yes
Performance & Benchmarks
Performance benchmarks show a clear improvement over previous versions. On the MMLU benchmark, Mistral Small 3.0 achieves a score of 82.5%, surpassing many proprietary 7B models. HumanEval scores indicate strong coding capabilities, with a pass rate of 68%. These numbers place it firmly in the top tier of open-source models available today.