Mistral AI announced Mistral Large 4 on October 6, a model with one trillion parameters in total. Only 49 billion of them work on any single request, which is how a model this large keeps the cost of each answer under control. The company trained it from scratch on 3,800 Nvidia Grace Blackwell GPUs in its data centers in France, and the same cluster now serves the public preview. The article walks through how that design works using a simple hospital comparison, so the idea of experts switching on and off becomes easy to picture.

The company reports 62% on DeepSWE v1.1, a coding benchmark, a little ahead of several well-known open-weight models, and it pitches the model heavily for cybersecurity work. Mistral itself calls the benchmark figures preliminary, and the cyber claims arrived without published numbers. The article keeps those two things apart, so you can see what has been confirmed by several outlets and what still rests on the company's word.

The weights are scheduled for October 27, and that is the date when researchers and companies can start testing the model themselves. We also explain what open-weight means in practice and how the €3 billion Series D round connects to the cluster behind this release. If you follow GPUs and data centers, the training setup is probably the most interesting part of the story.