Kimi K3: Open Frontier Intelligence (kimi.com)

More details:

- https://platform.kimi.ai/docs/guide/kimi-k3-quickstart

- https://platform.kimi.ai/docs/pricing/chat-k3

1M context, pricing is $3/$15 for 1M tokens (cache $0.3), which is extremely high for a Chinese open-weight model, but if it's truly competitive with most of the current frontier and is only behind Fable/Sol, the pricing is justified.

This is 1:1 pricing of Anthropic's Sonnet series (except Sonnet 5 which is currently on discount), and very close to 5.6 Terra pricing (Terra's input is $2.5).

One thing to consider, though: reasoning efficiency matters directly for how expensive a model actually is in real use. GPT's models are extremely reasoning efficient, and some Claude models like Fable at lower effort are as well. So if Sol spends 10K reasoning tokens to do something (at $30/1M) vs Kimi K3 that spends 50K reasoning tokens, Sol would win on cost effectiveness.

Half kidding feature request for HN: Mark all AI related posts so I can filter them out, when I need a pause.

> Among the models tested, its overall intelligence ranks second only to Claude Fable 5 and GPT-5.6 Sol.

> The full model weights of Kimi K3 will be released in the coming days. More details on the architecture, training, and evaluation will be published together with the Kimi K3 technical report.

https://platform.kimi.ai/docs/guide/kimi-k3-quickstart

Excited for the deepseek release this week (or at least they announced they'd release this week). Hopefully they also push even closer to SOTA.

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I really need to finish my automated model evaluation harness, I can't keep up with this pace

I've playing around in between with Arc-AGI-3 lately. Based on my very quick test prompt, I do not think it will achieve any meaningful score in Arc AGI 3. Not that it was expected to.

> We also further increased the sparsity of the Mixture of Experts (MoE): with the Stable LatentMoE framework, the model efficiently activates 16 out of 896 experts. Together with improvements in training methodology and data recipes, these structural advances give K3 roughly 2.5x the overall scaling efficiency of K2, converting compute into capability more effectively.

Assuming experts are uniformly distributed (I’m really not that familiar with the deep details there), that’s 2800/896*16 = 50 billion active parameters just for the active/expert part. Wild stuff, and I’m glad there’s at least some companies still publishing (and pushing, for open-weight models) total parameter count.

And: It sounds very believable that this would result in efficiency gains wrt. to compute necessary for “good”-quality inference. Does anyone know whether there currently even are any SOTA or near-SOTA models that are dense still?

Amazing to see an open source model already nearing the benchmarks of Fable and GPT 5.6 Sol!

Also very cool to see LatentMoE being picked up by more models (https://arxiv.org/abs/2601.18089)

Curious why the thinking mention chatgpt for a moment https://ibb.co/JFdhMN95

Open source Fable/Sol challenger! Interesting to do a release product-first.

https://platform.kimi.ai/docs/guide/kimi-k3-quickstart

> In our evaluations, Kimi K3 delivers frontier-level performance. Among the models tested, its overall intelligence ranks second only to Claude Fable 5 and GPT-5.6 Sol. For the complete benchmark results, see our tech blog. The full model weights of Kimi K3 will be released in the coming days. More details on the architecture, training, and evaluation will be published together with the Kimi K3 technical report.

> K3 pushes the boundary of end-to-end knowledge work. On the GDPval-AA v2 leaderboard, Kimi K3 scores 1687. The benchmark evaluates AI models on real-world tasks across 44 occupations and 9 major industries; Kimi K3 ranks behind only Claude Fable 5 Max and GPT-5.6 Sol Max, and ahead of Claude Opus 4.8 Max at 1600.

> On AA-Briefcase, Kimi K3 scores 1527, ranking second among all models — behind only Claude Fable 5 Max and ahead of GPT-5.6 Sol Max (1495). AA-Briefcase is a private agentic knowledge-work benchmark developed by Artificial Analysis to evaluate frontier agentic capability in long-horizon knowledge work.

Really good benchmark score it seems. Maybe another DeepSeek moment right here.

Account creation with only a phone number or google account is lame.

Say what you want about these Chinese models but they sure create competition and urgency in the space.

Seems to only use ≈60% as many reasoning tokens as 2.6. So the price hike is not as bad as it looks.

That's a more than 2x jump in parameter count. I know it's not a measure of quality by itself, but it will be interesting how it "scales". Bust it looks like they're gonna be competing with the big boys now, pricing also approaches Gpt 5.6 Terra

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It does seem to have retained the K2 series's creative writing abilities, at least with the prompts I've tested so far.

Now, will they actually release the weights? Seems like Chinese model providers are slowly closing up, like Alibaba's Qwen 3.6 which did release weights (but not the biggest parameter count ones) and none for 3.7.

Any updated Pareto frontier graphs? https://paraplouis.github.io/llm-pareto-frontier/ is quite out of date now.

I'm curious if they're keeping up mostly due to distillation or how that works. Does anyone outside China know?

No blog post? Benchmarks?