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Morning Edition · Tuesday, July 28, 2026Published at 1:47 AM EDT · New York
Moonshot Releases Kimi K3 Weights, a 2.8-Trillion-Parameter Model That Leads Open-Weight Rankings
The mixture-of-experts model scores 57 on the Artificial Analysis Intelligence Index, edging Claude Opus 4.8 while trailing Claude Fable 5 and GPT-5.6 Sol.
Moonshot AI published the weights of Kimi K3 on Hugging Face. It is a mixture-of-experts (MoE) model with roughly 2.8 trillion total parameters, and it activates about 104 billion of them per token by routing each one through 16 of 896 experts. The model ships with a context window of 1,048,576 tokens under a modified MIT license that permits commercial use. The training data and training code are not included, which makes this an open-weight release rather than a fully open one.
The independent benchmarking firm Artificial Analysis scored Kimi K3 at 57 on its Intelligence Index. That places it fourth overall, behind Claude Fable 5 (about 60) and two configurations of GPT-5.6 Sol, and just above Claude Opus 4.8 (about 56). The result makes K3 the strongest open-weight model measured on the index, not the strongest model overall. Artificial Analysis also recorded an Elo rating of 1,668 on the GDPval v2 agentic-task benchmark, up from 1,190 for the prior K2.6, and a first-place finish on the Frontend Code Arena at 1,679 Elo, ahead of Fable 5.
The distinction between the best open-weight model and the best model overall matters for a careful reading. K3 matches a closed frontier tier that United States labs released months ago, not the current best, and its tendency to produce false statements has not been independently measured. What is verifiable today is that a downloadable model now sits within a few points of the closed frontier on a widely cited third-party index, and that anyone can run it on their own hardware.
Veracity: Corroborated
83/100- If true, who benefits
Chinese labs and cost- or data-residency-sensitive buyers who can now standardize on self-hosted weights, plus anyone shorting the pricing power of closed API vendors.
- The nuance
The score 57 and open-weight lead are corroborated by third-party Artificial Analysis, but how the capability was reached is disputed: United States officials allege large-scale distillation of American models, which Moonshot denies, and the false-statement rate is unmeasured.
Part of a tracked trend
Open-Weight Models Close the Gap With Closed Frontier Labs
Over the next 3-9 months, open-weight releases with downloadable weights, long context, and strong agentic/coding performance increasingly match closed frontier models on practical work, eroding the closed-lab moat.
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