those models will follow your request when they already agreed to do this

deepseek tends to quickly assume this is benchmark and even when have web access thinks things like "simulated internet" etc (but waste tokens on thinking if there could be way to cheat)

This is the only AI-safe approach - see the OpenAI agent swarm incident where the swarm engaged in offensive cyber because guardrails were not active for their task and the model had clearly been post-trained on that narrow domain.

For the models, it's always day one. They don't learn from experience. Whatever they know after training is all they'll ever know. So if you don't teach them penetration testing in training, they won't know it. Maybe they could theoretically derive everything from first principles, and so could a top 1% software engineer, but that's a very slow and inefficient way to do things.

You'd be surprised. Cyber offense is a very narrow domain, especially given the extensive mitigations that are being used in modern systems. Leaving it out of post-training tasks unless you're specifically interested in it is the no-brainer approach.

The prompt says that the agent shouldn’t look up existing implementations on the internet, so I’m guessing that the implementation is baked in?

[1] https://strategywiki.org/wiki/Pac-Man/Tips

That would have been interesting to watch, which model gets the highest score.

K3 and GLM 5.3 flash are pretty mid by comparison when you consider how much more it will take to run them

It's a really smart model but way too expensive. The same $20 goes 100x further with Claude. I used it for the initial task planning while the plan didn't expire, then handed off to DeepSeek or Minimax.

You can see in this discussions the goalposts being actively moved to desperately create a notion that the lead is still there.

You even see people in this thread referring to imaginary wonder models that they expect US companies to have under development that are supposedly leaps and bounds more impressive, albeit the only reference there is to their existence is the commenters hopes and dreams.

I think people need to realize that there is no longer a cutting edge company that is expected to dominate, and among the multitude of AI companies in the field competition is so tight that no one would even be surprised if tomorrow some other company far away from the US was able to crank out the top performing model, or something like a small local model that outperforms mid/high tier frontier models. The local model alone would be a death sentence to US companies, who are still burning through 10x their revenue.

> Open-weight principles. These evaluations frame our open-weight release decision. First, we aim not to release open weights that would extend the absolute frontier of cyber capability. K3.1 Pro's offensive cyber capability is, by design, below what our pipeline can produce, and remains behind that of frontier proprietary models. Second, we intend to progressively raise the cyber capability available in open weights. We believe making K3.1 Pro available in a non-discriminatory way benefits people who need dual-use capabilities for defensive purposes. When a capable model is open and runs on infrastructure its users control, many more teams gain the means to audit the code they maintain — including legacy systems that rarely receive dedicated security reviews. We believe that letting this capability diffuse gradually — rather than concentrating it in a few proprietary systems — ultimately makes all software more secure.

I don't think Chinese labs are behind at all. I think the tell is that they keep releasing models 2 weeks after new American proprietary models that match or beat them. Suggesting they're sitting on much higher capabilities and releasing just enough to undercut

This is 100M fewer than Opus 5.5, 40M fewer than Fable 5.1, and about a third of Sonnet 5.5. In the top-6, it's 3rd, beaten by Muse Wonder at 140M and Astra at, impressively, just 60M.

> reasonably priced when comparing to other models of similar price

???

Very similar to my adversarial programming language (KillSwitch) benchmark[1], at least on pass rate, it is much slower though.

1 - https://bench.killswitch-lang.org

From my own use of the models I find their ranking to be totally inaccurate.

So their base price is 1/5th of Opus 5.5 per token, but if you want "highspeed" it's 2x that and then I guess it generates 2x the number of tokens as well?

That might suggest that Opus is competitively priced after all.

I'm glad I waited a day to post my analysis of LLM price cuts because it needs...an addition.

Paying API prices for Deepseek 4.1 Flash feels like a much better deal at the moment.

Are you using k3 with thinking set to the absolute highest and only using k3 for everything?

I think Kimi just doesn't have the means to subsidize plans.

From my POV it looks like you guys tried "They're distilling us! Very immoral!", saw that and nobody cares, and now you're trying to run the more emotionally impactful "They're distilling us... with your grandma's stolen credit card! They're stealing from your grandma!!"

I doubt that an AI could do anything with these that legions of scammers haven't already figured out how to do - after all, it's not like AI set up this distillation setup in the first place :)

The way I see it, automated systems should basicially have access to the financial system anyways. This is some of the appeal of cryptocurrency.

Sybil resistance built into the traditional financial system (for example firewalling based on residential IP, requiring banking apps, etc.) is a massive annoyance that can obviously by circumvented at scale.

I hope we chose the latter. Biometrics are creepy.

Spitballing random pr pieces from companies about to IPO is not helpful to anyone, fyi.

MiniMax -> Qwen -> DeepSeek (pro) -> Gemini (Carbon) -> Fable (to beat Carbon) -> Astra (to be slightly worse than Fable but cheaper).

I doubt we'll see any major US release besides Grok in the next 3 weeks.

Would be nice if we got a Terra that's competitive with Gemini Flash and DeepSeek Flash.

The fun part is, we're already at the point where we are seeing something frontier-ish almost every single week.

The really fun part will be when they all start adding System 1-3 natively (IMO) - which I think will be the next release cycle.

Yeah, there's another paragraph:

> Identifying and removing cyber-related data. During development, we observed vulnerability research performance improving steadily as post-training scaled. Tracing the source of this trend, we found that cyber-related content was embedded within the general mid-training corpus, and cyber-related tasks were present in the RL environment mix — a natural byproduct of training on broad data. In preliminary experiments on internal models, we found that without data removal at either the mid-training or the RL stage, downstream cyber capability would increase substantially. These findings gave us a concrete lever for control. For the open-weight release of K3.1 Pro, we identified and removed cyber-related data from both training stages, while retaining the general-purpose coding tasks needed for model usefulness. We will continue to apply appropriate controls at every training stage, of which cyber-related data identification and restriction is one example.

(emphasis mine)

It's clear that cyber is a touchy subject right now. But it's also clear that RL works on it, because it's highly verifiable. So smaller models are going to become very interesting, because they can be fine-tuned later to cover this field.

> "below what our pipeline can produce, and remains behind that of frontier proprietary models."

Like Ant and OAI, they've realized vaguely claiming to have nerfed their latest model to an unspecified degree not only looks 'good guy' prudent, it provides a built-in excuse when a model fails.

I don't want to give US labs the results of my DNA tests and my family tree going back to the 16th century just to secure my shitty app, but if the Chinese are bowing out as well it seems the only alternative is to admit that some script kiddie with a bunch of tokens will just pwn you now and then.

[0] https://x.com/crystalsssup/status/1944287779896328668

(note: extremely speculative and based on almost nothing, and especially near-complete ignorance to alternatives)

The way to stop the vulnerability apocalypse is to fix the vulnerabilities, not to shoot the damn messengers.

I hope someone abliterates and FTs this model on vulnerability discovery so we can find more bugs in our testing, insecure tech stacks.

There was law proposal (rumour?) last week about Chinese labs having to get approved for weights release now.

Not sure I'd put Google there in practice. And while Muse is an impressive jump from behind it's still not quite at the frontier either IMO.

I think the former definition is a bit more meaningful since it avoids issues with determining what actually constitutes "the best" model out there. In that case, Google has been a frontier lab for a long time, Meta arguably as well, and you're also ignoring xAI which has been competitive for a while now. And, obviously, there's a bunch of Chinese labs which would deserve the title as well.

But if one is being strict (and willing to accept generally accepted definitions/measures/etc.), then there has only ever been two frontier labs: OpenAI and Anthropic. They're the only labs to have had "the best" models at any point in time since LLMs became a thing.

everyone else mostly seems to have just decided to cheat on benchmarks to make their models look better than they actually are, and none seem to want to actually try to make better models.

As long as LLM's can be distilled, and all these "labs" (what a weird name) can distill one another, there are going to be as many as capital is available to be burnt on the topic.

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Unlike Claude models which always hide the thinking process, Chinese models always show it. So how the heck they can delegate the api call to anthropic and still get the full reasoning? Yes there are tricks to reveal the thinking output, but they are just tricks and do not works reliably. So what would happen when they failed to do it? Cancel the ongoing token generation and revert back to it own model? What about the glitches, the delays, the changes in behavior?

I would understand if the attempts are small in volume, because normally people hardly notice it, but anthropic claims that they happened for millions of calls. Such large numbers would surely get noticed by Chinese users and got reported in the tech forums like linux.do. yet we haven't seen any sight.

Unless you want to see more of what happened the past few days.

I think the argument that "cyber capabilities should be democratized, thus keep producing them now, fast, and for everybody" is a very naive one.

If enough people are flagging it, we may be at a point where the pelican benchmark is truly overdone.

When the frontier is pacing itself in its speed in terms of adding new capabilities (notice how fast opus 5.5 got released after Astra), then that also means that the open weights models which were just behind are accelerating in their speed just as much as well.

Good news for us because the models which used to feel 6 months behind now feel like they are lesser.

Double threat to Anthropic and OAI because if open weights models become good enough at the current level of Sota while being cheaper (which time will tell if this model fits the bill or not), then people will use those models instead of OAI/Anthropic provided it is "good enough"

Though it has to be said that only real world use case can be trusted but I remain optimistic and hope that this turns out to be good in real world use cases as well.

But in my opinion, all of this is really good for us end users!

Still get the model out there.

Mostly it's datacenter build out and circular financing magic when the same dollars being commited multiple times by multiple companies passing hot potato to each other. They basically commit capacities 2-3 years ahead.

a lot of those money is the commitment from US AI labs that's not yet spend until the data center is roll out.

you are conflating.

Everyone and their dog has been saying its a bubble, there is no moat, this is all known techniques being scaled (very inefficiently) onto increasingly expensive hardware clusters at exponential cost, etc.

Chinese models never agreed to the pacing anyway lol.