Hacker News Reader

Comment by natrys | Hacker News Reader

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

View on HN

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

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'.

It's much more a rallying cry for open weights funding than it is for regulatory capture.

The argument on our side wins - if America or the West don't do open source, China will. And that means -- with certainty -- that China wins the market.

Every politician and VC should hear that loud and clear.

I call them “Chinese models” without vitriol. I think they’re great. Although it’s good to remember that all models have ideological biases inherited from their creators.

I just want to see dario cry for some reason . i cannot explain it but i want him in particular to lose.

> If the numbers hold up scrutiny, this is scary good.

After using it for a few hours, I believe these benchmarks.

It's like reading Anthropic's obituary.

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.

Fable won’t even generate a jwt to test endpoints because it is security related. It is crazy capable but useless for real work

Unless your real work is outside the scope of one tiny niche of work.

Eh, it doesn't hit you until it hits you.

I finally bumped into a task that Codex would refuse to work on.

Was I attempting to reverse-engineer a GPU driver? Yes. Was I trying to hack into the DoD? No.

I wasn't doing anything wrong, but that's not what OpenAI's safety mechanisms thought.

GLM has issues with tool calls and nested JSON and it wastes tokens pretty often. I see it being a bit above half the price of Opus in a bit more complex eval tasks. With some RL you could probably get the tool calls sorted and the price down.

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.

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.

Oracle is fine, it's just that they can't really expect political decisions that hindered it to accquire TikTok which will be slated to be the biggest customer if the deal went through.

Now they are betting with Project Stargate but it also seems to be crumbling down.

But don't forget that they literally hold the biggest databases, both in commercial and open source, that is, Oracle Database and MySQL. Plus Oracle Java they literally controls at least 30% of the internet's software infrastructure.

And also with a good team of attorneies enforcing the licenses, they can squeeze so much money at the cost of morality.

Also recently they downgraded the always free OCI ARM instance from 4C24G to 2C12G without telling anyone.

> Oracle is fine

They're drowning in debt and risk is increasing. If these US models don't keep holding up their valuation will tank further and some will recall the loans or ask for different terms.

New enterprise java licenses are going to milk enterprise just like broadcom is doing. New license deals makes you pay for employee total number (including contractors) instead of for users of oracle java.

Even boring, slow moving companies are well on their way to eradicate the last traces of oracle java. We kicked the last of that 2 years ago, despite having >3000 different systems across the globe. (We started circa 5 years ago).

Models need datacenters to run. It also need other services to do anything useful

The point: Fable isn't worth what Anthropic says it is, so Anthropic isn't as valuable as they make themselves out to be.

The DeepSeek incident has already shown it, this is a reminder.

I would assume the opposite is true — with an open-weight Fable-class model, doesn't demand for GPUs go up? Plenty of companies can now look at what Anthropic is offering — high per token costs for a very intelligent model — and do the math, and at some point it makes sense to just rent the GPU yourself and run Kimi on it if you get similar intelligence without paying Anthropic's margins (albeit with high upfront capital cost).

This would drive down Anthropic's margins, but drive up demand for datacenter and GPU capacity. It's not that people would be using fewer GPUs, they'd just shift demand from high priced token vendors to direct GPU rental, which benefits datacenter companies while hurting Anthropic.

Its a margins game. If its too cheap to run, its not worth the investment.

That makes no sense, Terry.

The cost to run Kimi is the cost of the GPUs (+ overhead of hiring humans for now to manage it). Kimi K3 does not change the demand curve for LLMs, it only changes the possible suppliers — and they're all competing for the same supply-constrained resource, which is GPUs in datacenters. Regardless of who is serving the model, or how, they're going to need to rent or buy GPUs in datacenters. Hence: this is great for datacenters.

You can run open weight models anywhere.

If it ends up being open weights, companies will use it running in US data centers.

> 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.

Both things, and both reasons, can be true at the same time.

Not everyone's going to care about Anthropic requiring data collection (a similar debate plays out with regards to "pay or consent" on website tracking), just as not everyone cares about China with regards to security/IP issues (if they did, a lot more would be banned besides occasionally-Huawei).

> 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.

it's well documented that models can be adversarially trained with essentially backdoors in response to special inputs

while I am skeptical that this is happening atm, there are probably many industries where the risk does not seem worthwhile

I feel like that's a threat that isn't super difficult to block. Unplug it from the internet, require it to go through an API intermediary to access web pages.

Maybe I just don't have any imagination.

It could generate code that's plausible but has intentional flaws, kind of like the defunct underhanded C contest [0], except through a LLM.

[0] https://en.wikipedia.org/wiki/Underhanded_C_Contest

It could, but exposing that would doom the company entirely, and AI doesn't generate code with near the quality needed to get a model to mass adoption, insert malicious underhanded code, ensure that consistently looks innocuous enough to never be noticed, and- most importantly- actually exfiltrate data without being noticed. Once it is noticed, it's game over across the board.

When the model is open weights you can even pass every token (including the chain of thought) though a fourth-party lightweight model like gpt-oss-safeguard to check that it has not become adversarial.

I suppose this is like when Anthropic was using “prompt modification, steering vectors, or parameter-efficient fine-tuning” to poison the work of people working in the LLM field, including academic researchers.

No, that was totally different. They were just doing that for your safety.

So it's better to trust cloud solution where not only they can do the same, but also actively use your data?

Because in 2026 we still believe USA is more trustworthy than China?

That's right, but this strategy only works when there are no opponents of the same level to review the generated code.

You don't even need that, all models are susceptible to prompt injection. You already need to take extreme security precautions and assume all models can essentially behave like attacker-controlled rootkits.

Good luck hosting 2.8T params yourself. A box capable of this at a useful performance level is at least $100k.

More like $500k, but that's not an unreasonable price for a medium sized enterprise to pay.

I have an RF engineering background, a nice mmWave vector network analyzer can easily land in that ballpark.

If the business value is there, companies will pay for it.

Not to mention the bill you would be paying anthropic could be way higher at that scale

How much would it cost to run a 2.8T model with long context for 1000 developers, plus 30,000 non-dev employees with short session memory?

For several export controlled industries in the United States, even self-hosting a Chinese model is a non-starter.

Cursor will rebrand it as Composer 3.0 to assuage any such concerns, as they did with the previous Kimi models.

More likely for them to use Kimi 2.7 since Grok is now the flagship product.

Musk bought it. From now on, it will only be Grok.

This is apparently Open Weights, so no reason Amazon can't serve it alongside GLM which they already do.

> continuing to refuse to use chinese models due to security and IP concerns

One can run open weights in an exclusive TEE'd GPU too, which still comes out cheaper than closed weight LLMs. Ex: https://chutes.ai/pricing

> Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns

These customers exist (e.g. US military) but there's not enough of them to justify a trillion dollar valuation.

Anthropic's valuation is predicated on growth. If they start going backwards and losing customers to open models, it hurts their ability to gather investment and with it the ability to train new models, leading to a death spiral.

The best they can hope for is that the US gives them state aid to compete with China, however their relationship with the current administration is not great.

The inverse is also true: many companies are refusing to use American models due to security and IP concerns. And it's more concerning here: American companies straight up say they will train on your IP; local Chinese models structurally can't. The security concern goes this way too: for American companies, you're relying on their own security infrastructure and essentially blind trust. For locally hosted Chinese models, you 100% control the security story.

The reality this demonstrates: most US companies don't give even 2 shits about their IP, and are fine willingly handing it to Anthropic et al. Those that do care largely must care contractually. For group 2, they're either using Chinese today or aren't using AI at all. Those are the only two valid options, there is no secret "use US but self host it" third option.

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 a question I am going to get an answer tomorrow with evals. Extremely interesting...

If Chinese AI companies can train a model that's slightly worse than the frontier, then there's no reason why they can't train a model that is slightly better than the frontier.

Everybody can agree that K3 doesn't clearly surpass Fable. However, inevitably there will be a time in the future when a Chinese AI company releases a model that's better than any US model.

K3 isn't the knockout blow but it's the 2nd knockdown that makes everyone in the arena realize that the fighter is not winning the fight.

Anthropic is arguably still better in tooling and integrating model and tooling. Good habit beats raw intelligence.

For code editing Cursor editor tooling is even better.

Certainly for their IPO, anyway

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.

Any benchmark where Sol is better than Fable at coding is ridiculous.

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)

Why we should waste 46 minutes instead of briefly looking at charts for 20 seconds? To pay their ads? No, thanks.

Reply on news.ycombinator.com