Mistral 发布开放权重为Mistral大4提供公司"Le Chonk"AI型号的主权控制。

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Mistral releases open weights for Mistral Large 4, providing sovereign control for the company’s ‘Le Chonk’ AI model

Mistral AI SAS today announced the availability of the open weights of its frontier model Mistral Large 4, nicknamed “Le Chonk,” the company’s most capable large language model to date, initially released in preview three weeks ago.

With 1 trillion parameters and 52 billion active parameters, ML4 is natively multimodal and has received three additional weeks of reinforcement training and customer testing. The company says it’s becoming one of the best open agentic coding models in the world, offering leading performance at a small fraction of the token cost with state-of-the-art cyber defense capabilities.

“ML4 OSS showcases our science team’s ability to innovate at speed,” said Vice President of Science Pierre Stock. “Building on the ML4 Preview, the open-weight version continues reinforcement learning training and delivers substantial gains with no signs of saturation.”

The company trained ML4 on 3,800 Nvidia Corp. Grace Blackwell graphics processing units in its European data centers to achieve high-fidelity performance for tool use and coding, driven by end-to-end control of its infrastructure and the ability to optimize for hardware during model development.

Open weights mean companies can take the model and deploy it on their own hardware, within their own firewalls, and run sensitive data without exposing it to the cloud or outside parties. With Mistral’s model, companies will have a Western-developed model to compete with the multitude of ultra-large Chinese coding models on the market, such as the GLM family from Beijing Zhipu Huazhang Technology Co. Ltd. and the Kimi family from Beijing Moonshot AI Technology Co. Ltd.

“Open source is essential to ensure state-of-the-art AI does not end up concentrated in the hands of a few actors,” said co-founder and Chief Executive Arthur Mensch. “Other labs have raised hundreds of billions to develop equivalent capabilities; in three years, Mistral raised roughly $6 billion.”

Because of its training regimen, ML4 stands out among many other frontier models, rivaling even top models such as Chinese-built GLM-5.3 on the Artificial Analysis Coding Agent Index while using 47% fewer tokens per task on average.

On Terminal-Bench 4.0, which tests agentic coding in the terminal, ML4 climbed from 28% to 41%, tying GLM-5.3 as the best model for coding agents.

Mistral offers three reasoning modes for ML4: low, medium and high. Teams can use this to match reasoning depth to the task without adjusting prompts, which can be less effective, allowing reasoning to fit quick everyday tasks to deep work on extremely complex problems. This allows developers and enterprise users to control token use per task.

The company said the release of ML4 represents a new generation of Mistral models spanning all modalities. It follows the recent launch of Voxtral 3, a family of voice models and voice agents. Mistral plans to continue to release additional models from this family in the coming months.

Imags: SiliconANGLE/Mistral AI Vibe; Mistral AI

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