Moonshot AI's Kimi K3 Drops 2.8 Trillion Parameters to Beat OpenAI and Anthropic
Moonshot AI's 2.8T parameter Kimi K3 launches with frontier-level agentic performance, a 1M token context window, and open weights on the way
- Kimi K3 launches: Moonshot AI releases its 2.8T parameter flagship model, the largest open-source model to date, on July 16, 2026.
- Frontier-tier agentic performance: Scores 1668 Elo on GDPval-AA v2, surpassing Claude Opus 4.8 (1600) but trailing Claude Fable 5 (1760).
- Open weights incoming: Weights not yet released but confirmed coming; would lead all open-weight models including DeepSeek V4 Pro (1.6T) and GLM-5.2 (753B).
- Pricing: $3/$15 per 1M input/output tokens; ~$0.94 cost per task, roughly half the price of Claude Opus 4.8.
- Token efficiency up, hallucinations up: 21% fewer output tokens than K2.6 while scoring 13 points higher, but hallucination rate rose from 39% to 51%.
- New architecture: Built on Attention Residuals and native multimodal vision; accessible now via Kimi's API with 1M token context window.
Moonshot AI just dropped Kimi K3, and it's making a loud entrance. The model clocks in at 2.8 trillion total parameters, making it the largest open-source model released to date, and it's already posting benchmark numbers that put it in the same breath as Anthropic's Claude Opus 4.8 and OpenAI's GPT-5.5. The weights aren't out yet, but Moonshot has confirmed they're coming, which would make K3 the dominant open-weight model by a wide margin.
A generation jump, not an iteration
Kimi K3 is the third-generation model in Moonshot AI's Kimi series, succeeding the K2 family that shipped between July 2025 and mid-2026. Moonshot AI is a Beijing-based startup best known for the Kimi chatbot and for releasing K2 under a Modified MIT license, which made it one of the strongest open-weight models of 2025. But K3 isn't just another incremental update. At 2.8 trillion total parameters, it sits well ahead of DeepSeek V4-Pro at 1.6 trillion and every other open-weight release from a Chinese lab this year. Moonshot's own chart shows just how sharply the line jumps with K3, after months of the field hovering in the 500 billion to 1 trillion parameter range.
K3 follows the K2 family, which had roughly 1 trillion total parameters with a 256K context window. K3 more than doubles the parameter count and quadruples the context. The architecture also changes. K3 is described as built on a "new architectural innovation" rather than a straight scale-up of the K2-series MoE. Specifically, the model introduces Attention Residuals, a technique Moonshot open-sourced earlier this year as a drop-in replacement for standard residual connections, alongside native visual understanding baked directly into the model rather than added as a separate module.
Where it lands on the leaderboard
Kimi K3 scores 57 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models, which average 30. When evaluating the Intelligence Index, it generated 130M tokens, which is very verbose compared to the average of 63M.