Kimi K3 arrived with 2.8 trillion parameters, 1 million tokens of memory, and the subtle energy of a neighbor parking a battleship in the driveway.
Here's what happened
- Moonshot AI, a Chinese AI lab, released Kimi K3, an open 3T-class model with native vision, a 1M-token context window, and full weights due by July 27. Open weights let teams download, host, modify, and fine-tune model files instead of renting API access.
- Moonshot says K3 uses sparse experts (specialized sub-models that only partially activate per task) to reduce runtime costs for a large model.
- K3 beat GPT-5.6 Sol on BrowseComp and Automation Bench in Moonshot's table, while Artificial Analysis said it used 21% fewer output tokens than Kimi K2.6.
- The catch: Simon Willison found it capable but costly, with a single SVG test consuming more than 16,000 output tokens. Moonshot recommends 64 or more accelerators for serious deployment.
How to try it
- Use Kimi K3 on kimi.com, Kimi Code, or the Kimi API.
- Watch for open weights by July 27 if your team wants to host or customize it.
- Watch this hands-on Kimi K3 walkthrough for design, 3D, and coding tests against Fable 5, plus the argument that cheaper open intelligence changes distribution as much as capability.
Why this matters
Chinese AI companies like Z.ai and Moonshot keep edging toward frontier quality (and yes, it’s a big deal). But let’s point out something else:
Thinking Machines released Inkling yesterday, a 975B-parameter open-weights model built for customization through Tinker. Thinking Machines says it used open models, including older Kimi K2.5, to bootstrap early post-training data. Kimi is already part of the supply chain for other US model companies.
So, before government or Anthropic people scare anyone otherwise, remember: if a Chinese open model nears frontier quality, global companies (including US ones!) get another powerful base layer they can adapt and run privately. This is why you don’t want to ban open-weight Chinese models, government people. When China opens them, American companies that adopt them win.
Our take
A new frontier-level open model reduces US labs’ pricing and distribution leverage. This is also good for American companies whose engineering departments are like frogs about to be boiled alive when these companies go public.
Kimi K3 still has to prove itself outside demos and vendor benchmarks, and open does not mean free, because deployment still costs compute (this is why frontier intelligence models need not be just open, but small, too; we’ll get there).
Paired with Inkling, K3 points to the next fight: closed labs selling polished assistants versus open ecosystems selling control, customization, and compounding advantage. Which system do you want to build your company on top of?
Editor’s note: This article originally appeared on our sister publication, The Neuron.