Mistral Large 4 is now available on Hugging Face and through Mistral’s API. It accepts images and text as input and generates code or text, using 52 billion active parameters per task.
The release follows a preview 3 weeks earlier, with further reinforcement learning guided by customer feedback. Mistral reports that results on Terminal-Bench 4.0, which tests coding agents in a terminal, rose from 28% to 41%, compared with GLM-5.3 at 40%.
For builders, token use and control over reasoning are worth testing alongside coding accuracy. Results reported by Mistral on the Coding Agent Index from Artificial Analysis put the model alongside GLM-5.3, with average token consumption per task 47% lower; Mistral has also added low and medium reasoning modes, but API pricing has not yet been published.