Fireship recaps a chaotic week in the open-weight model race: Mistral released Mistral Large 4, a trillion-parameter mixture-of-experts model with a million-token context window, one day after US startup Reflection AI finally shipped Beam, a 501B-parameter model trained on $6.3 billion worth of GPUs that Mistral promptly out-benchmarked. Meanwhile Moonshot AI closed a round at a $50 billion valuation while its Kimi K3 model already does what both Western labs are chasing. The underlying argument: privacy, surveillance, and sovereignty are why every government wants an open model of its own.
- Mistral Large 4 is a mixture-of-experts model with roughly a trillion parameters but only ~49 billion active per token, giving trillion-parameter knowledge at roughly the cost of a model 20x smaller — the same trick DeepSeek used.
- Reflection AI, backed by Nvidia and raising $4.6 billion on the premise that the best open model should come from the West, shipped Beam: 501B parameters, base model trained in under 4 weeks on ~24 trillion tokens, plus 4 weeks of post-training for code, tools, and long agent tasks.
- Beam benchmarked itself against an already-outdated Chinese model and was out-benchmarked by Mistral within 24 hours; it still requires an invite and the weights aren't released.
- Mistral's reported numbers are self-provided, the model isn't finished training, and weights arrive later this month under a custom license — so the claims are provisional.
- Because Beam's weights aren't out, the best open American model is arguably still Google's Gemma 4, released under Apache 2.0 in April.
- The privacy case for open weights: proprietary prompts go to a data center, get scanned by classifiers, potentially read by safety teams, and used for training — with examples of users facing police attention after chats with Claude.
- Governments face the same problem at scale: analyzing something like a national power grid with a foreign proprietary model means handing over private schematics, which is why countries want models they own or can trust.
- Kimi K3, Moonshot's 2.8 trillion-parameter open model from July, was popular enough to exhaust their GPUs; Microsoft, Amazon, and Google have all been negotiating to host it, and Moonshot went from a $4B to a $50B valuation with China's state AI fund involved.
It's the clearest statement of the actual stakes behind the open-model race, moving from individual privacy to national infrastructure security.
Even if you're not planning on committing a felony with proprietary models, you're still sending every prompt to a data center, which is scanned by a classifier, but potentially read by a safety team, and then used in whatever way the lab wants.
A compact explanation of mixture-of-experts economics immediately followed by a sharp caveat that Mistral's benchmarks are self-reported on an unfinished model.
You get a trillion parameters worth of knowledge for roughly the cost of running a model 20 times smaller, which if that sounds familiar, it's probably because it's the same trick DeepSeek used last year to tank Nvidia.
It was trained on 6.3 billion dollars worth of GPUs. It benchmarked itself against an already outdated Chinese model. And 24 hours later, it got out-benchmarked itself by Mistral.
The catch is the numbers really are, trust me, bros, because they were provided by Mistral and they actually haven't finished training yet.
If all you've got is a gaming PC, go with Gemma 4 and get your money up.