Three weeks is a short time in model development, yet that was all Mistral needed to move Large 4 from 28% to 41% on a coding test that measures how well a model works inside a terminal. The extra training was guided by feedback from governments and enterprises, and it also changed how the model spends its effort: users can now pick low, medium, or high reasoning, and the high setting got 50% more efficient along the way.

The efficiency story goes further than speed. Mistral reports that the model reaches results comparable to GLM-5.3 while using 47% fewer tokens per task, a gap that grows quickly when coding agents run for hours. The article explains what tokens are, why they drive cost, and how the cybersecurity test behind the 79% score works, including the bugs it asks the model to find and patch.

There is also a debate attached to the release. Mistral's vice president of science compares AI to electricity and argues that nobody should depend on a supplier who can cut off access, while critics point out that published weights can never be recalled. The full piece lays out both positions, along with what it takes to run a trillion-parameter model on real hardware.