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# Kimi K3 could be the largest open-weight model ever, and it drops tonight

**[Trends](https://daily.dev/sources/trends)** · 2 min read · 10 upvotes · 0 comments

## Summary

Moonshot AI is set to release Kimi K3, a model with 2-3 trillion parameters that would make it the largest open-weight model from China. Anonymous sources suggest it could match or surpass Anthropic's Claude Opus 4.8. The release comes as Moonshot raises at a $31.5 billion valuation, up 57% from $20 billion just two months prior. The model continues a trend of competitive open-weight releases from Chinese labs — following DeepSeek and Z.ai — that is pressuring closed-source providers like Anthropic and OpenAI.

## Content

Moonshot AI dropped Kimi K3 on July 16 and the benchmarks landed hard. The 2.8 trillion-parameter mixture-of-experts model hit #1 on Arena's Frontend Code leaderboard (1,679 Elo vs Claude Fable 5's 1,631 and GPT-5.6 Sol's 1,618), topped DesignArena's Frontend Web App benchmark, ranked #3 on DeepSWE, and #4 on Agent Arena — matching Opus 4.8 and GPT-5.6 Sol. On SWE tasks, Fireworks AI found K3 at 92.4% vs Fable's 92.6%. Essentially tied at the top.

The cost gap is where it gets interesting. Kimi K3 runs at $3 per million input tokens vs Fable 5's $10. In a 10-task UI head-to-head, K3 cost $4.06 total vs $14.15 for Fable — 71% cheaper. For every $100 spent, K3 completes 14.7 software engineering tasks vs Fable's 5.3. One developer put it bluntly: "switched my Claude API to Kimi yesterday — it's faster, 10x cheaper, and writes better code."

The catch is speed. K3 took 28 minutes on three Rust tasks vs under 7 for Fable. Agentic knowledge work runs average 56 minutes per task at $10.57 each — 10x pricier than K2.6 — because the model revisits tasks repeatedly and generates heavy output. Moonshot's own blog admits K3 "exhibits a noticeable gap in user experience compared with Claude Fable 5 and GPT-5.6 Sol." Capacity issues have already forced signup pauses.

The benchmark picture isn't uniformly rosy either. K3 scores only 39% on FrontierMath Tier 4 (7 points behind top US models from seven months ago), 23% on CritPt, and trails GPT-5.4-xhigh on LiveBench. @scaling01 notes Moonshot "still has a lot of post-training ahead" and that K3's strongest domain — coding — isn't fully reflected in current ECI scores yet.

Politically, the reaction was swift. Trump advisor David Sacks called it "concerning." Gavin Baker called it an inflection point that's bad news for closed labs like OpenAI and Anthropic but good for infrastructure — the DeepSeek commoditization playbook, again. Weights drop publicly July 27 under a modified MIT license, with vLLM day-0 support already confirmed. Once they're out, the containment conversation is over.

The routing angle is underrated: Fireworks found that sending 72-96% of agentic tasks to K3 and the rest to Fable achieves state-of-the-art accuracy above either model alone, at up to 50x lower cost than Fable-only. Single-model strategies are looking increasingly expensive.

---

Tags: [#ai](https://daily.dev/tags/ai), [#kimi-k3](https://daily.dev/tags/kimi-k3), [#llm](https://daily.dev/tags/llm)

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