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.
> 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.
> its overall intelligence ranks second only to Claude Fable 5 and GPT-5.6 Sol
Pretty sure ranking “second” to two others means ranking third.
Yeah, bad wording it seems. Though a charitable interpretation is that Fable 5 and GPT 5.6 Sol are joint 1st place in the measurement.
If there are two folks standing at gold, nobody gets the silver medal.
Doesn’t matter, the next one is still third.
Which is still great because it means neither of the two best financed labs in the world manage to produce even two models themselves that would beat Kimi K3.
Where are you seeing this write up?
I copied that from https://platform.kimi.ai/docs/guide/kimi-k3-quickstart but it seems they updated the page to remove the benchmark score now.
Half kidding feature request for HN: Mark all AI related posts so I can filter them out, when I need a pause.
Lol, this post is number one on the leaderboard on the “filtered” list list. Trusting ai slop to filter out ai is as ironic as it gets.
This post is at the top when filtered against AI :) Maybe it should use llm based filters to understand if the post is about AI and filter it out?
Us the AI to build the bubble against the AI, because everyone knows AI is the AI of the AI.
I'll see your simonw tool and raise you one that actually works: https://hcker.news/?view=frontpage&ai=exclude
I's not just matching against titles. Ironically, I have an agent running daily scans, reading the contents of the top 200 stories of the day. It auto screens high-confidence ones and I make judgement calls on like 10-20 of them per day.
Right now, that site doesn't show this post, regardless of whether the filter is active or not ...
So, it's impossible to know whether your filter is working on this story yet, either.
Except it literally shows this post as the first result
definitely take the breaks when you need them. I've already had a few friends just get lost in the AI train of stuff and suffer mentally a bit.
https://hn.algolia.com/?dateRange=last24h&page=0&prefix=fals...
or
https://lobste.rs will probably have less AI
I see a future HN post about how someone vibe coded HN to filter the AI stories. HNAI (Heck No AI)
Same but 100% serious
Why only a half measure
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)
Any updated Pareto frontier graphs? https://paraplouis.github.io/llm-pareto-frontier/ is quite out of date now.
It does seem to have retained the K2 series's creative writing abilities, at least with the prompts I've tested so far.
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
Account creation with only a phone number or google account is lame.
Especially if you don't have a phone and don't want to use your google account for anything but gmail, for privacy reasons. Both of these point apply to me, for instance.
Open source Fable/Sol challenger! Interesting to do a release product-first.
> 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?
No, you can't divide the entire size by the expert count. A lot of weights are constant for all tokens, so total active count is ((2800-(shared)/896)*16 + (shared))
2.5x the scaling efficiency, so 4 times the price? What is happening here? Did the subsidies dry up with the discrepancy between chinese and US models?
It's also 2.8x parameter count (1T -> 2.8T), likely higher activation per token (50B?).
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.
Where did you hear about the deepseek release? Would love to follow the same source.
That is exciting!
I don't understand how DeepSeek can be so cheap with their cache pricing - ~0.003 usd / 1Mtok. 100x less than Kimi K3, or similar numbers against pretty much any other decently sized model to my knowledge. I've been using it whenever possible as even longer agent sessions cost few cents.
What provider are you using?
DeepSeek's own API
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.
Seems to only use ≈60% as many reasoning tokens as 2.6. So the price hike is not as bad as it looks.
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.
I really need to finish my automated model evaluation harness, I can't keep up with this pace
Say what you want about these Chinese models but they sure create competition and urgency in the space.
> 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.
> > ...ranks second only to Claude Fable 5 and GPT-5.6 Sol.
So... it ranks THIRD?
The literal interpretation of that sentence is "when it is second or third, it is only behind Fable 5 or 5.6 Sol". And indeed they give benchmarks where it is ahead of one but not both models.
USSR is proud to announce that they won 2nd place in an Olympic contest. The filthy USA regime? Next to last!
(There were only two countries competing in said event)
Apple proudly announced they won 2nd place in a competition among smartphone OSes.
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Tokenizers also matter. Anthropics tokenizers will encode the same piece of text at a way higher token count than OpenAi, for example.
That said, Kimi is competing against GLM in my mind, and GLM 5.2 is less than 1/3 the price.
Tokenizers define the alphabet on which the language model is trained. I don't want people to get the impression it's a module which can be swapped out or modified on its own. Alphabet size is a design consideration related to correctly encoding the training data.
With that kind of pricing, I don't think they're competing with GLM with this new launch.
I feel like the quickstart is missing something. It's referring to its tech blog for actual benchmarks, but K3 isn't mentioned on there, the last thing on that blog was K2.6, 2 releases ago.
Agreed re reasoning. I’ve seen this play out with 5x reasoning negating cost savings.
Will be interesting to see how it stacks up pricing wise on the various inference providers.
I eat 1M context in a local model in about 3-4 hours.
It'd need to be exceptionally smart and error free to ever make sense.
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And as a gift, you can hand over your data to the Chinese regime.
Right at this moment, there are more people in the world on the side of China than on the side of the USA. Which can translate into raw market numbers at some point. So these comments are kinda moot.
Better than handing it over to the US regime.
Or just host it yourself or on your country's cloud provider once they release the weights.
Or the American one :)
Sadly these days this seems like the least worse of the three major regimes.
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I have absolutely zero sympathy for Western model providers.
Bring on the Chinese token-dumping onslaught.