# 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.

- Published: 2026-07-28T05:47:21.225Z
- Canonical: https://polylog.news/ai/2026-07-28/moonshot-releases-kimi-k3-weights-a-2-8-trillion-parameter-m
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [Polylog editors](https://polylog.news)

Moonshot AI published the weights of [Kimi K3](https://t.me/ai_machinelearning_big_data/10601) 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](https://t.me/aipost/7652) 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.

## What this means

The closed labs' lead is measured in months, not capability tiers. A downloadable 2.8-trillion-parameter MoE model that any enterprise or government can run on its own hardware narrows the gap between open weights and the closed frontier to a single index tier. That exposes closed API vendors on switching cost and data-residency arguments rather than on raw capability. The mechanism is distribution: buyers in cut-off regions and cost-sensitive customers standardize on infrastructure they control, and Chinese labs capture that demand.

## What to watch

- Whether independent evaluators reproduce the GDPval and Frontend Code Arena numbers and publish results on false-statement rates and long-context retrieval, which would confirm or refute the parity claim.
- Adoption signals such as inference providers hosting K3 and enterprises reporting production use, which would show the open-weight option moving from benchmark scores to real workloads.