The lead
Kimi K3 open weights land today at 2.8 trillion parameters
Even squeezed to four bit MXFP4 precision the weights need roughly 1.4 terabytes of fast memory before you load a single token of context, so almost nobody will run this at home. Moonshot's own benchmarks put K3 ahead of Claude Opus 4.8 and GPT-5.5, behind Fable 5 and GPT-5.6 Sol, and it debuted first on Arena's frontend code board.
Moonshot promised the full weights for K3 by 27 July, eleven days after the hosted launch. It is a sparse mixture of experts model, 2.8 trillion total parameters with only 16 of 896 experts firing per token, a one million token context and native vision.
KRALYSPrice leverage at Kralys: when the next AI vendor quote arrives, the argument is that a frontier class model is now downloadable for free, so the fee has to buy hosting and support, not capability.