alt Hacker NewsKimi K3 is now live
Comments
> 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.
Pelican: https://tools.simonwillison.net/markdown-svg-renderer#url=ht... - rendered via the OpenRouter API: https://openrouter.ai/moonshotai/kimi-k3
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.
I finished benchmarking[0] it, but it was not fun, it only supports (max) reasoning and the model is quite slow. Apart from a few requests timing out, it also has some issues with tool calling/response format schemas (Moonshot rejected tools.function.parameters with anyOf schema).
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...
> 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
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.
I'm a bit nervous this one isn't going to be open-weights. Any mention of "open" has been struck from the literature for this model (it was present an hour ago). We don't even know active params?
At this pricing, I'll be surprised if it's open.
Working with chinese models is giving me a fullfilment sensation. I think that I have enough quality for the work that I need to do and lots of extra tokens to work with. With Claude and ChatGPT I reach the limits fairly easy, but not with OpenCode Go. So I will use Claude once in a while for difficult tasks to see how much better it still is (but use Chinese on a daily basis)
Half kidding feature request for HN: Mark all AI related posts so I can filter them out, when I need a pause.
Any updated Pareto frontier graphs? https://paraplouis.github.io/llm-pareto-frontier/ is quite out of date now.
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)
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.
Only supporting "max" reasoning is weird, their parameters are quite inflexible atm:
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.Quite impressed by the result to my first prompt...
How feasible is it to hook Kimi up to do GitHub code reviews? the Copilot quotas got really stingy recently
Very interesting to see how Gemini 3.5 Pro stacks up against this new wave of models. Hope they have something similar to a Gemini 3.1 moment soon. Their speciality has always been math and multi modal intelligence and the new models are recently all very coding focused.
Account creation with only a phone number or google account is lame.
> 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?
It does seem to have retained the K2 series's creative writing abilities, at least with the prompts I've tested so far.
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.
>Too many people are chatting with Kimi right now. Subscribe to enter a dedicated priority queue!
Why do most LLMs insist on a login, even for a free trial?
I entered a question to try it, but as soon as I hit enter it wants my phone number for a login. No thanks.
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
Open source Fable/Sol challenger! Interesting to do a release product-first.
I'm not finding this on huggingface yet is and open model or is kimi now a closed model ?
it doesnt work though, text area brings up pop up window
I get a quota of GitHub Copilot for free.
From all the models available to me I'm most happy with Kimi K2.7 (given the cost/performance).
Does anyone have any heuristics on how scaling parameter count actually scales cost to serve? Also im assuming we dont really know the sparsity here?
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?
Wants a phone number...no thank you.
I am trying to benchmark it, but it only supports (max) reasoning, and even for simple questions, it takes forever to answer/times out :(
> 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.
This is far too expensive. Why would I use this over a frontier model at these prices.
I'm curious if they're keeping up mostly due to distillation or how that works. Does anyone outside China know?
Seems to only use ≈60% as many reasoning tokens as 2.6. So the price hike is not as bad as it looks.
at this rate we'll have a new state-of-the-art model before i finish typing this comment
The question remains is it open or not, if it's open I will use it if it's not well I was happily being fucked over by an American tech giant...
Say what you want about these Chinese models but they sure create competition and urgency in the space.
Not worth it. I have just tried a single prompt in the web interface and it is still not finish reasoning. It thinks too much and often repeats the same stuff over and over.
Combine with the price it will surely more costly than gpt 5.6.
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.
do they not have an API? only sub?
I really need to finish my automated model evaluation harness, I can't keep up with this pace
Curious why the thinking mention chatgpt for a moment https://ibb.co/JFdhMN95
Thank you Kimi. We no longer need to rely that much on Dario and his supreme lackeys to decide what is safe or not for simple tasks.
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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.