French company Mistral AI has announced the launch of its latest models, Ministral 3B and Ministral 8B, on the first anniversary of the release of Mistral 7B. The AI company says these new state-of-the-art models are specifically designed for on-device computing and edge use cases, setting a new benchmark in the sub-10 billion parameter category. The company refers to them as "les Ministraux."
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Ministral 3B and 8B Models
Ministral 3B and 8B offer enhanced capabilities in knowledge, commonsense reasoning, and function-calling efficiency. They can be used or fine-tuned for a variety of uses, from orchestrating agentic workflows to creating specialist task workers.
Both models support a context length of up to 128k (currently 32k on vLLM), enabling them to handle complex tasks with ease. Notably, the Ministral 8B features a special interleaved sliding-window attention pattern for faster and more memory-efficient inference, the company said on Wednesday.
Privacy-First Inference for Critical Applications
Targeting local, privacy-first inference for critical applications, these models are ideal for use cases such as on-device translation, offline smart assistants, local analytics, and autonomous robotics.
"Les Ministraux were built to provide a compute-efficient and low-latency solution for these scenarios. From independent hobbyists to global manufacturing teams, les Ministraux deliver for a wide variety of use cases," the company explained.
Integration with Larger Models
Furthermore, when used alongside larger models like Mistral Large, the les Ministraux models serve as efficient intermediaries for function-calling in multi-step workflows, enabling low-latency input parsing and task routing.
"They can be fine-tuned to handle input parsing, task routing, and calling APIs based on user intent across multiple contexts with extremely low latency and cost," the company said.
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According to the company, performance benchmarks indicate that les Ministraux consistently outperform their peers across various tasks, as Mistral re-evaluated all models using its internal framework for fair comparison.
