> As an early proof of concept, Kimi K3 designed a chip to serve a nano model built on its own architecture. In a single 48-hour autonomous run, K3 built, optimized, and verified the chip using open-source EDA tools on the Nangate 45nm library. Within 4 mm², the chip closes timing at 100 MHz and sustains over 8,700 tokens/s decode throughput in simulation, packing 1.46M standard cells, 0.277 MB of SRAM, and an INT4 MAC array with fused dequantization. A chip built by a model, for a model, reflects K3's long-horizon agentic capabilities.

Absolutely wild.

> We may use Content to provide, maintain, develop, support, and improve the Services, comply with applicable law, enforce our terms and policies, and keep the Services safe and secure. Customer who requires restrictions on the use of Customer Content for training or improving Moonshot AI models may contact Moonshot AI to discuss available enterprise arrangements or separate written agreements. Unless otherwise expressly agreed in writing, Customer Content may be used for the foregoing purposes.

https://platform.kimi.ai/docs/agreement/modeluse#4-content

- 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.

> 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.

95 input, 16,658 output = 25 cents! https://www.llm-prices.com/#it=95&ot=16658&ic=3&oc=15 (13,241 of those were reasoning tokens.)

I think that's the most expensive pelican I've rendered through a Chinese model so far.

This puts them on the top of the largest open models list:

  Kimi K3            2.8T
  DeepSeek-V4-Pro    1.6T (49B active)
  Kimi K2.6          ~1T (32B active)
  GLM-5.2            754B (40B active)
  DeepSeek-V3.2      685B
  Mistral Large 3    675B

2.8T param open model, 1M context, native vision. Weights releasing by July 27 with technical report. Launching with max thinking effort by default; low/high effort modes coming in future updates.

https://www.kimi.com/blog/kimi-k3

Maybe we should update the link to it instead?

At this pricing, I'll be surprised if it's open.

> Impress me with a 1 page html file

Result: https://ydaurtg3fdwhq.kimi.page/

Came out looking pretty cool! By contrast, Fable produced a moderately more interesting "live observatory" of the solar system.

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

Wonder if they’ll open-source this and show how many tokens it cost.

[0] https://www.kimi.com/blog/kimi-k3

It also, for some reason failed to generate either of the 2 coding demos (hamster svg and solar system css animation).

Intelligence-wise, it's between GPT-5.6 Terra and GPT-5.6 Sol. It's ~30% better than Kimi K2.6, but a lot slower and more expensive.

[0]: https://aibenchy.com/compare/moonshotai-kimi-k3-max/moonshot...

https://www.kimi.com/blog/kimi-k3

- The blog post is explicitly saying that the model is open; that language was removed from the previously shared link

- It shows benchmarks

I've been playing around with it for the past few hours, and I think it's an amazing model. I'm not sure I could tell the difference between this and Fable in a blind test. The quota in the $100 Kimi Coding plan seems to roughly align with what I get from the $200 Anthropic plan when I primarily use Fable.

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?

    Important limits:

    reasoning_effort currently supports only max; K3 always has thinking mode enabled.

    max_completion_tokens defaults to 131072 and can be set up to 1048576.

    temperature=1.0, top_p=0.95, n=1, presence_penalty=0, and frequency_penalty=0 are fixed; omit them from requests.

    Return the complete assistant message unchanged in multi-turn conversations and tool calls.

    Vision input does not support public image URLs. Use base64 or ms://<file-id>, and make content an array of objects.

    Web search is being updated and is not recommended for production workflows in the near term.

The question it came up with, "which U.S. state is closest to Africa?" is a pretty standard trivia question without any reason to believe other AIs would get confused. https://pellmell.ai/s/dccdeca69f929f79bc89317035610049

Even GPT-OSS-120b gets this right: https://pellmell.ai/s/1a43dfc7a3baa214aa0fa1b95d2c536a

They can't even blame it on distillation this time, unless they want to claim that their own preferred security measures were ineffective in preventing Chinese access to Mythos.

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

I entered a question to try it, but as soon as I hit enter it wants my phone number for a login. No thanks.

From all the models available to me I'm most happy with Kimi K2.7 (given the cost/performance).

Is them pricing at Sonnet level actually give us any information at all at how big Sonnet is or is there too much opacity around inference margins?

How feasible is it to hook Kimi up to do GitHub code reviews? the Copilot quotas got really stingy recently

Combine with the price it will surely more costly than gpt 5.6.

https://mp.weixin.qq.com/s/V4xhEIy8xDXSMDPrPkmUAQ

Translation:

https://mp-weixin-qq-com.translate.goog/s/V4xhEIy8xDXSMDPrPk...

Cheaper then GPT 5.6 Sol (according to their results) ...

"The full model weights will be released by July 27, 2026."

> 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

https://artificialanalysis.ai/models