Mistral puts frontier-class weights on four GPUs with Medium 3.5
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Mistral puts frontier-class weights on four GPUs with Medium 3.5

Mistral has released [Medium 3.5](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/), a 128 billion parameter dense model with a 256k context window that folds instruction-following, reasoning and coding into a single set of weights. It posts 77.6 per cent on SWE-Bench Verified and 91.4 on the agentic telecom benchmark. Two release details matter more than the benchmarks. The weights are open, [published on Hugging Face](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) under a modified MIT licence that permits commercial use with carve-outs for high-revenue companies. And the model self-hosts on as few as four GPUs, with API pricing at $1.50 per million input tokens and $7.50 output for those who would rather not. The release also ships Vibe remote agents, asynchronous sandboxed cloud coding sessions that integrate with GitHub, Jira and Slack. For regulated Australian entities weighing data residency, APRA-style vendor concentration questions and exit strategies, a frontier-class model that runs inside your own perimeter is no longer hypothetical. The procurement conversation has a new comparison point.

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