Mistral’s new Large 4 trails some Chinese open models in independent tests
- ID
- 33557
- Status
- summarized
- Published
- 09 Oct 2026, 7:00 PM
- Fetched
- 09 Oct 2026, 7:18 PM
- Provider
- Tom's Hardware
- Category
- technology
- Original URL
- https://www.tomshardware.com/tech-industry/artificial-intelligence/independent-tests-rank-mistrals-new-trillion-parameter-large-4-the-best-ai-model-outside-the-u-s-and-china-but-chinese-open-weights-still-overcome-europes-best-efforts
- Source URL
- https://www.tomshardware.com/feeds/all
Summary
- Score
- 6.0
- Created
- 09 Oct 2026, 7:18 PM
- Tags
- Audience
- developersai_ml_learnerssaas_founders
What happened
Mistral released Mistral Large 4 (ML4), a trillion-parameter mixture-of-experts model trained on Nvidia Grace Blackwell GPUs, claiming it 'pushes the frontier of open-weight performance.' Independent benchmarker Artificial Analysis ranked ML4 as the best model from outside the U.S. and China on its index with a score of 38, but several Chinese open-weight models still beat it overall. ML4's single strongest result was on AA's CyberGym-E2E-AA end-to-end cybersecurity measure.
Why it matters
The 'best model outside the U.S. and China' headline is an index ranking, not a task-level result: ML4 scored 38 on the Artificial Analysis Intelligence Index and lost to Chinese open models overall while winning on the cybersecurity eval. If you are picking an open-weight model for a coding or agent workload, that split means you should run your own eval on your task instead of switching on the regional ranking. The text also gives no license terms, inference pricing, or serving requirements for a trillion-parameter MoE, so treat any assumption that ML4 is cheap to self-host as unverified.
Discussion angle
ML4 topped one cybersecurity eval but lost the overall index to Chinese open models — what would your own eval harness look like for deciding between them, and how much does a trillion-parameter MoE actually cost you to serve versus an API call?