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Kimi K3 is Moonshot AI's new 2.8-trillion-parameter open-source model. As of August 2026 it's the largest open-weight model released, with 104B active parameters per token across 896 experts. It supports a 1,048,576-token context window (exactly 4x Kimi K2.7 Code's). It handles text, images and video natively. Kimi K3 goes toe-to-toe with frontier proprietary models like Claude Fable 5 and GPT-5.6 Sol in benchmark testing. You can access it through Hugging Face, OpenRouter, Fireworks AI, Baseten, Together AI, or Moonshot's own platform. Most first-party providers charge the same $3/$15 per 1M tokens, and OpenRouter's cheapest route undercuts them. Self-hosting is technically possible, but "possible" means a minimum of 8 enterprise-grade accelerators and a hardware bill in the high six figures. Crypto miners don't even have enough hardware to run K3. We got Kimi K3 running in OpenCode and gave it live web access using the Firecrawl MCP. The Firecrawl MCP extends Kimi K3's knowledge base and gives it access to live web data. Kimi K3 is Moonshot AI's newest open-weight model, and as of August 2026 the largest one anyone has released at 2.8 trillion parameters. It succeeds Kimi K2.7 Code, reads text, images and video natively, and holds up to 1,048,576 tokens in a single context window. Moonshot opened API access on July 16, 2026, then published the full weights on July 27. Kimi K3 has some really impressive specs, especially for an open source model. Kimi K3 comes in with 2.8 trillion parameters. Models like GLM-5.2 and Kimi K2.7 Code range from roughly 700 billion to just over 1 trillion. Its architecture provides Kimi K3 with 896 different experts when performing different tasks. It also boasts a context window of over 1,000,000 tokens. Kimi K2.7 Code had a context window of just 262,144, so K3 quadruples it.
Kimi K3 supports text, images and video natively. Total Parameters: 2.8 trillion Activated Parameters per Token: 104B Architecture: Mixture-of-Experts (MoE), Stable LatentMoE Number of Experts: 896 total, 16 selected per token, 2 shared Layers: 93 (69 KDA + 24 Gated MLA, 1 dense layer) Context Window: 1,048,576 tokens Quantization: MXFP4 MoE expert weights / MXFP8 activations, quantization-aware trained from SFT onward (non-expert components stay in higher precision) Modality: Text, image and video (native); vision encoder MoonViT-V2 (401M params) License: Custom "Kimi K3 License" (not plain MIT) These stats were from Kimi K3's Hugging Face page.
Moonshot's Kimi K3 technical report credits its architecture and training changes, including Kimi Delta Attention, Attention Residuals and Stable LatentMoE, with roughly a 2.5x gain in overall scaling efficiency over Kimi K2.
Kimi's weights were released on July 27, 2026, as you can see in Kimi.ai's X post below. On Moonshot AI's technical blog, they go through Kimi K3's benchmarks, which are on par with frontier proprietary models as well.
Every number in this section comes from that blog. A few of them shifted slightly in the arXiv technical report, which was revised after launch, so it's worth checking both if a specific score matters to you.
Source: https://www.kimi.com/blog/kimi-k3 DeepSWE: Nearly tied GPT-5.5, just barely edging it out 67.5 to 67.0. Kimi K3 was behind only GPT-5.6 Sol (73.0) and Claude Fable 5 (70.0). FrontierSWE: Beat GPT-5.6 Sol, GLM-5.2, Opus 4.8 and GPT-5.5 with a score of 81.2, coming in second only to Claude Fable 5 (86.6). Kimi Code Bench 2.0 (internal): Finished second behind Claude Fable 5 (76.9) with a score of 72.9. Terminal Bench 2.1: Nearly tied GPT-5.6 Sol (88.8) with a score of 88.3, taking second place. Program Bench: Beat all other models with a score of 77.8, including GPT-5.6 Sol (77.6). SWE Marathon: Scored 42.0 and beat all other models tested. Worth noting that Claude Fable 5 hit harness fallbacks on 35% of these tasks, which drags its 35.0 down. Source: https://www.kimi.com/blog/kimi-k3 GDPval-AA V2 Elo: Finished behind Fable 5 and GPT-5.6 Sol, beating all other models. AA-Briefcase Elo: Finished second (1548) only to Claude Fable 5 (1583). Automation Bench: Beat all other models (30.8). The closest runner up was GPT-5.6 Sol (29.7). JobBench: Finished second (52.9) to Claude Fable 5 (57.4).