Just in case you were thinking of signing up directly with Moonshot to use the service, they appear to train even on API use:
> 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.
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.
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ekojs
> 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.
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.
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swimwiththebeat
Did anyone see on the blog post[0] that it was able to code up an entire GPU compiler from scratch? It looks like it even outperformed triton on some GPU kernels. That just seems insane to me.
Wonder if they’ll open-source this and show how many tokens it cost.
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.
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sebmellen
My testing prompt for these models is by no means objective or repeatable (like the pelican) but it's a nice test of curiosity:
Came out looking pretty cool! By contrast, Fable produced a moderately more interesting "live observatory" of the solar system.
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h2aichat
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)
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buildbot
Amazing to see an open source model already nearing the benchmarks of Fable and GPT 5.6 Sol!
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.
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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esher
Half kidding feature request for HN: Mark all AI related posts so I can filter them out, when I need a pause.
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seizethecheese
Kimi doesn't do well on my "ask a trivia question that other AIs get wrong" test.
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
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.
msdz
> 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?
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Gecko4072
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.
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pr337h4m
It does seem to have retained the K2 series's creative writing abilities, at least with the prompts I've tested so far.
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smalltorch
Account creation with only a phone number or google account is lame.
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himata4113
It's important we now have a recap to the opus 4.8 release where we were threatened with ID verification as "these models become more powerful" and had to pass "verification" to gain full access to the capabilities without having random "cyber" refusals.
d3Xt3r
Does anyone know how to connect this (web version) to Microsoft Learn MCP?
modeless
Anthropic's "durable advantage" theory of US AI dominance is looking pretty silly. There's zero indication that it will be hard for China to keep pace as models improve and start contributing to their own training. Which pretty much invalidates their policy recommendations.
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.
at this rate the next model release will just be a git commit hash and a shrug emoji
GodelNumbering
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.
schmorptron
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
wxw
Open source Fable/Sol challenger! Interesting to do a release product-first.
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.
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grommz
Imagine you're a mid sized company and you can host this model locally. Suddenly there are zero reasons to pay a single red cent to the bloodsucking American AI cartel.
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oybng
>Too many people are chatting with Kimi right now. Subscribe to enter a dedicated priority queue!
ncruces
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).
anthonypasq
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?
anentropic
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
nullbio
This is far too expensive. Why would I use this over a frontier model at these prices.
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npn
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.
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XCSme
I am trying to benchmark it, but it only supports (max) reasoning, and even for simple questions, it takes forever to answer/times out :(
taf2
I'm not finding this on huggingface yet is and open model or is kimi now a closed model ?
Seems to only use ≈60% as many reasoning tokens as 2.6. So the price hike is not as bad as it looks.
XCSme
No blog post? Benchmarks?
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root-parent
Wants a phone number...no thank you.
tw1984
> 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.
Crap, the first open weight model that really feels out of reach when it comes to running it locally at home. :-(
freestanding
it doesnt work though, text area brings up pop up window
tskj
I'm curious if they're keeping up mostly due to distillation or how that works. Does anyone outside China know?
cute_boi
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.
khalic
I really need to finish my automated model evaluation harness, I can't keep up with this pace
wellthisisgreat
how much would it cost to host it on AWS for example?
minraws
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...
loolhahalmao
do they not have an API? only sub?
satvikpendem
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.
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lvl155
Say what you want about these Chinese models but they sure create competition and urgency in the space.
Just in case you were thinking of signing up directly with Moonshot to use the service, they appear to train even on API use:
> 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
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.
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.
> 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:
That's one mighty large model! Moonshot is going to need the USD 500 million reportedly raised earlier this year to run this model.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.
Did anyone see on the blog post[0] that it was able to code up an entire GPU compiler from scratch? It looks like it even outperformed triton on some GPU kernels. That just seems insane to me.
Wonder if they’ll open-source this and show how many tokens it cost.
[0] 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.
@dang, since the English blog post is now live:
https://www.kimi.com/blog/kimi-k3
Maybe we should update the link to it instead?
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.
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.
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)
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)
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...
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.
Half kidding feature request for HN: Mark all AI related posts so I can filter them out, when I need a pause.
Kimi doesn't do well on my "ask a trivia question that other AIs get wrong" test.
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
Any updated Pareto frontier graphs? https://paraplouis.github.io/llm-pareto-frontier/ is quite out of date now.
Only supporting "max" reasoning is weird, their parameters are quite inflexible atm:
> 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?
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.
It does seem to have retained the K2 series's creative writing abilities, at least with the prompts I've tested so far.
Account creation with only a phone number or google account is lame.
It's important we now have a recap to the opus 4.8 release where we were threatened with ID verification as "these models become more powerful" and had to pass "verification" to gain full access to the capabilities without having random "cyber" refusals.
Does anyone know how to connect this (web version) to Microsoft Learn MCP?
Anthropic's "durable advantage" theory of US AI dominance is looking pretty silly. There's zero indication that it will be hard for China to keep pace as models improve and start contributing to their own training. Which pretty much invalidates their policy recommendations.
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.
The technical blog post is out now, and it's a better top-level link than what we have currently: https://www.kimi.com/blog/kimi-k3
at this rate the next model release will just be a git commit hash and a shrug emoji
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.
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.
https://platform.kimi.ai/docs/guide/kimi-k3-quickstart
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.
Imagine you're a mid sized company and you can host this model locally. Suddenly there are zero reasons to pay a single red cent to the bloodsucking American AI cartel.
>Too many people are chatting with Kimi right now. Subscribe to enter a dedicated priority queue!
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?
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
This is far too expensive. Why would I use this over a frontier model at these prices.
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.
I am trying to benchmark it, but it only supports (max) reasoning, and even for simple questions, it takes forever to answer/times out :(
I'm not finding this on huggingface yet is and open model or is kimi now a closed model ?
https://www.kimi.com/blog/kimi-k3
"The full model weights will be released by July 27, 2026."
at this rate we'll have a new state-of-the-art model before i finish typing this comment
Full benchmarks in Mandarin:
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) ...
Kimi 3's Artificial Analysis benchmark scores between GPT Sol and Opus 4.8.
https://artificialanalysis.ai/models
Seems to only use ≈60% as many reasoning tokens as 2.6. So the price hike is not as bad as it looks.
No blog post? Benchmarks?
Wants a phone number...no thank you.
> 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
Crap, the first open weight model that really feels out of reach when it comes to running it locally at home. :-(
it doesnt work though, text area brings up pop up window
I'm curious if they're keeping up mostly due to distillation or how that works. Does anyone outside China know?
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.
I really need to finish my automated model evaluation harness, I can't keep up with this pace
how much would it cost to host it on AWS for example?
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...
do they not have an API? only sub?
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.
Say what you want about these Chinese models but they sure create competition and urgency in the space.
Curious why the thinking mention chatgpt for a moment https://ibb.co/JFdhMN95