These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.

I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.

Not sure about Sol as I haven't used it, but, at least for security work -- does it matter? It's not like you will be allowed to use Fable (or access Mythos) for anything cybersecurity-related unless your name is "Dario Amodei" or you are one of his rich friends. So regardless of how good Fable/Mythos is here it's a completely moot point for normal people, because they can't use it for that anyway.

Isn't post-training turning out to be the most important part?

4x DGX sparks should let you run this at 4 bit at least and there are some folks who ran GLM 5.2 on this configuration in r/LocalLlama

in some cases (mainly reverse engineering) I have observed GLM 5.2 jailbreaking itself with no effort on my part, the thinking trace revealed that it did some mental gymnastics to pretend it was a crackme or capture the flag competition.

Even if there was a small/medium gap, the fact that this is a free model beats both of the above on pure economics.

Hopping for an AgentWorld variant from Qwen but I guess, I have too high expectations.

yet but it is still contain a lot of trash. you need better models to process those trash and create a curate dataset. this will happen again and again until there is no more juice to squeeze. and I'm sure we are still not done with it.

> post training

yeah this will be crucial. the big models are already too capable, they are just not that aligned with current agent tasks.

> parameter count doesn’t seem to be a direct correlation anymore

I don't think so, remember that chinese labs do not have as much compute power compare to US frontier labs. that's why deepseek v4 flash had that huge jump and deepseek v4 pro is kinda a disappointment, they just do not have the compute power to proper posttrain the pro model like they wanted. glm is also a relative small model so you also can see the huge jump with just post training. so it does not mean the size does not matter, it is just mean that the chinese labs currently only capable of training smaller models effectively.

LLMs are still surprisingly “easy”. You need maybe a couple dozens of right people, a lot of good quality data and a lot of GPU that you know how to operate. There’s relatively little “secret sauce” needed.

Despite this being the topic du jour of 2025, it was never true. Most of the "we've hit a wall with data" came from communicators / media and not researchers. It got popular because negativity sells. It's a false premise for a number of reasons:

a) Data curation is as important, if not more important than bulk data. Models becoming better at classification leads to better curation leads to cleaner data. Throwing common crawl and pray is so 2023. We've known this since llama3 days, it worked then, there's no reason to think this will not continue to work as the models imrpove.

b) Models are today good enough that you can augment / multiply your data easily with enough compute. You can now have a model take "authoritative content" and create more data from that + scenarios. Say you take a book on computer architecture. You ask models to break it down. Then you ask models to find examples for each topic. Then you ask models to ask questions and offer answers from several viewpoints. Then you take each of those and ask other models to flag inconsistencies. And so on. But you can whateverX your data from one authoritative source + bulk data into 5x - 10x "scenarios".

c) RL is really really really powerful. It's hard to do right (reward hacking, instabilities, etc) but once it works it "keeps" on working. Again, we knew this to be true a few years ago, ever since models really started to do well on math (highly verifiable). It only follows they're getting better on cybersec and other verifiable tasks. But now, with models improving, you get the same data augmentation pipelines as above, just better because they're also verifiable. For example, the way cursor augments their data: take a repo, ask an agent to identify a feature (it can be a large multi-file feature). Remove all code relating to that feature, but keep the original tests in the repo. While training, that becomes a RL scenario: implement this feature in this repo. Verify it with the original (hidden for training) tests. Reward appropriately. Now you can get 1 repo -> 20-50-100 scenarios. Instead of "feed everything into the pretraining", you're now creating scenarios, verify them w/ existing tools, and get your scoring function for the rewards. And, importantly, as the models become better in general, they also become better at this pipeline building exercise. So the next iteration gets trained on more scenarios, better scenarios, and so on.

> how will models continue to get better?

Probably the same. No one can know for sure, but at the moment, despite all the "walls this, slowdown that, plateauing" and so on, there are no signs of slowing down. And, as you noted, this works across the field of model sizes. There are, of course, theoretical information-based limits on size, but smaller models also improve, once "bigger" models can be used as training data generators, oracles for verification, rubric verifiers for open ended questions, and so on.

And smaller models (i.e. cheaper to serve) get to generate more traces during RL, and more rollouts give you better training, and so on. Next up - hardware optimised inferencing (ASICs basically). Once you have that, we can expect another wave of improvements. And so on.

Let those companies with thousands of GPU making millions pay. They should.

"Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete."

/s