Kimi K3 Launches to Revolutionize Agentic Coding and Knowledge Work

This might be the most impressive website generator demo I've seen:

https://macos27.kimi.page

Context from the person who prompted it: https://x.com/mweinbach/status/2077827886149439547

So Chinese labs are driving essentially towards commodotized intelligence. Even if its a few months behind the US.

Is this a classic 'commoditize my compliment' situation? They want to sell the hardware and infrastructure behind AI and make the software part not the value driver / moat?

I can see it. But also even two Chinese labs sinking 100s of millions USD into training isn't exactly commoditization. It's still a ton of effort with dubious payoff.

> Kimi K3 is Kimi’s most capable model to date, with 2.8 trillion parameters.

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

That's one mighty large model! Moonshot is going to need the USD 500 million reportedly raised earlier this year to run this model.

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.

Some official benchmark numbers posted in Chinese social media (I am sure they will publish an English blogpost later too):

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

Generally looks like a Sol/Fable tier model, better across the board than Opus 4.8.

(Edit) English blogpost is up now: https://www.kimi.com/blog/kimi-k3

On the first try, Kimi K3 just found the source of a bug that Fable 5 hasn't been able to pinpoint in multiple attempts. It's just one anecdote, and I haven't used K3 much yet, but so far it's looking extremely promising.

My testing prompt for these models is by no means objective or repeatable (like the pelican) but it's a nice test of curiosity:

> 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.

Kimi K3 blog is up: https://www.kimi.com/blog/kimi-k3

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.

According to artificialanalysis, cost per task is $0.94, which is almost the same as $1.04 of gpt 5.6 sol max (fable is most expensive by far, at $2.75). Things like glm 5.2 max cost roughly half that. The model certainly sounds extremely impressive for something not from openai/antrophic, but the price makes it a mediocre product.

Instruction following seems lower than I’d like, too. OTOH scores on agentic stuff seem high, which… feels a bit contradictory? I thought decent instruction following is step 1 of solid agentic workflow.

The benchmarks look nothing short of incredible. Assuming it’s not benchmaxxed to hell and back it’s just a notch below gpt 5.6, which came out what, a week ago? If the performance claims hold up the delayed Gemini 3.5 pro will likely end up not only behind fable, but also behind 5.6 and a (supposed) open weights model. Google might have to do some real soul-searching.