Comment by natrys
3 hours ago
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
The link has 6 well-known benchmarks where this beats Fable (out of 14 I counted). If the numbers hold up scrutiny, this is scary good.
Forget about their pricing but the companies that do have means to host such models fully on-prem are also the same companies that are paying tens of millions of $ in inference cost every month, and are by extension the biggest customers of OAI and Anthropic
> If the numbers hold up scrutiny, this is scary good.
After using it for a few hours, I believe these benchmarks.
Open Source >>> Closed Source [1]
I don't want to cheer against my country, but we've given up on open source. The way Anthropic and OpenAI treat their customers as adversaries is embarrassing.
I will cheer for China, for Kimi, and for z.ai until we have something in the same category.
[1] I'd even be fine with open weights, fair source, or anything that let us have direct access to the weights. Even if that came with stipulations. Don't hide the weights from us.
I am with you in the spirit of openweights but I am trying to hard-avoid bringing countries into this. The narrative of US vs China only benefits those who want regulatory capture in the US since attacking China is politically much easier than attacking open-weights, so certain groups like to repeatedly call them 'Chinese models'.
I think given how much benchmaxxing we're seeing - the anecdotal evidence of how competent this model is (and efficient) will depend on user's actual real-world use cases.
Given the pricing, it suggests that this model is much more efficient/competent than previous-gen OS/distilled models.
It's like reading Anthropic's obituary.
Nah:
https://www.youtube.com/watch?v=LSlV206xPqM
These real world examples show it's one tier away.
These "real world" examples are nothing like the way I use LLMs from within a harness. GPT 5.6 Sol and Fable are clearly more impressive, but how does this translate to interactive agent use, or use under an agent orchestration framework?
This is weird and reactionary. Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns. Anthropic/american models aren't going anywhere anytime soon.
> Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns
This is such a common omission: the Chinese models are open, you can host them yourself on your premises. So privacy and independence.
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> Lots of organizations are continuing to refuse to use chinese models
Correction: Lots of organizations are refusing to use Anthropic Fable because they have forced opt-in data collection as part of their privacy policy, even for Enterprise.
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Nope, but I think this is maybe the critical mass needed to finally crash the AI hype/datacenter cost problem everyones is talking about.
With Oracle being junk before this, more will follow.
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If it ends up being open weights, companies will use it running in US data centers.
You can run open weight models anywhere.
Cursor will rebrand it as Composer 3.0 to assuage any such concerns, as they did with the previous Kimi models.
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Fable is by Anthropic, and this is too expensive, GLM 5.2 is roughly the same quality at a much cheaper price.
(I mantain a client with llama.cpp and 101 models across 14 companies by http)
As much as I like GLM 5.2 it's clearly a step below Opus (or even Fable) for more complicated tasks. I would place it at Opus 4.6/4.7 level.
Having said that, the safety system on Fable makes it an extremely unattractive model. It feels that half of the time you're paying double for Opus level performance.
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Meh, not fable/sol tier:
https://www.youtube.com/watch?v=LSlV206xPqM
If anecdote is data, then here's another point:
https://nitter.net/synthwavedd/status/2077537805715005724#m
(As an aside, I don't know how it was professional of Arena to unmask an unreleased cloaked model on their platform. Also practically, upstream could have been A/B testing multiple variants under same endpoint, casting validity of such pre-announcement tests into question)
Crazy how their models always come out after the US labs and just lag the performance of top models. Almost like they are performing distillation attacks... how strange.
distillation attack? why the violent word choice? When OpenAI crawled Github was that an attack?
Distillation is not an attack. It simply a way to train a model. Not doing it when you are behind is akin to snatching defeat from the jaws of victory.
Do you have moat if your advanced model can be distilled in a month or two ?