A 1-trillion-parameter model just became downloadable. That doesn’t make it cheap to run
Another downloadable option is good for teams that want control over deployment. Be careful with the cost story, though. Hardware, latency, and the operating bill for a 1T model are set by the total weight size, so price those before you call self-hosting the cheaper route.
52B parameters are active per token, but all 1T of them have to sit in memory.
Mistral released the weights of Mistral Large 4, nicknamed “Le Chonk”: a multimodal mixture-of-experts model with 1T total parameters and 52B active. Mistral reports open-weight coding leads on FrontierCode 2.0 at 19.7% and Terminal-Bench 4.0 at 41%, by its own evals. The licence is modified MIT, and Model-as-a-Service operators need a separate one.
My read: another downloadable option is good for teams that want control over deployment. Be careful with the cost story, though. Hardware, latency, and the operating bill for a 1T model are set by the total weight size, so price those before you call self-hosting the cheaper route.
Your pick: benchmark it against your current provider on real tasks. Mistral’s API is $0.70 per million input tokens and $2.10 per million output, so you can test it before committing any hardware. Read the licence before you plan a product on it.
12 months: more teams evaluate open-weight models for specific coding workflows.
24 months: deployment control becomes a stronger buying criterion.
36 months: hosted and self-hosted models run side by side in most stacks.
Why I may be wrong: downloadability doesn’t close the capability gap. Artificial Analysis lists Opus 5.5 at 46% on FrontierCode, well above Mistral’s reported 19.7%.
What would justify switching for you: quality, control, or total cost?