It wasn't a secret either. They blogged about it last month: https://z.ai/blog/glm-5.3-flash#:~:text=Serving%20at%20Scale...

This introduces other incentives to cut corners and over-quantize.

They didn't pay for training

What provider are you using currently?

It's hard to know, since no one advertises the actual token limits (partially cause they're prolly complex / adaptive). So it seems much more likely that they just offer different pricing tiers than you're used to. Like, the $80 plan is still ~$80 of subscription quota, regardless of what else is offered.

For [API usage](https://openrouter.ai/z-ai/glm-5.3-flash#providers) they charge a bit more than the very cheapest providers of GLM-5.3-Flash, but not so much that a big price difference would make sense.

I also have a legacy pro plan and the only limitation is if you are trying to work in the morning from Europe because you are in the 3x usage overlapping China time but after 12 or so you basically can run it at least for me at least 3 parallel sessions all the time.

This is such a good video. Instant sub. Next to tech bros, we should also put AI-cringe bros.

Yeah that's what I already do. Fable writes the plan and checks things at certain milestones. Otherwise it does get lost indeed.

Kind of feels like this applies to every single model, from Astra to Qwen, they all eventually lose track of the plot unless you feed it some human's input that can steer them right every now and then. The only difference is how often you need to do so, and also how often you want to do so heavily influences how good quality the results will be.

There's also a third way that can spend the most tokens: if the AI is used as part of the product, and not just a tool to build the product.

That's easy to do with many agents independently told to find bugs in a large codebase.

Most of them are not public, but a fun thing I did was a mario cli game - https://github.com/Daviey/mario/ (or `ssh mario.baby`).

I now exclusively use https://omp.sh/ as my harness:

I set it up so it never works in the main branch so subagents etc don't step on each others toes, and only merges back when complete: https://github.com/Daviey/mario/blob/main/.omp/hooks/pre/wor...

A good AGENTS.md is essential: https://github.com/Daviey/mario/blob/main/AGENTS.md

I then provide specifications for what I want, making sure it is unit tested.

I too am sceptical but I’ll take my chances. At least it’s helping the open weights.

Yes I sometimes think the "don't train on my data" is actually a good signal for "this data/person is probably better to train on because they want to keep something private". The whole copyright system should have stopped these guys from training on everyone's data and it did not, if you think they care about the privacy checkbox I think you're dreaming personally, based on their past behavior.

I was gonna ask how people found their coding plans

Very good - but I'm on a legacy plan. And coming up on a renewal that would put me on the watered down current plan. But with 50% legacy discount think it may be worthwhile. If I go to a competitor I'd be paying market rate.

They must have hit really hard scaling limits if the prices were hiked so much so quickly.

Not really scaling - their plans were initially comically subsidized even more so than what the western providers are doing. More advert for an upstart than commercially priced.

Way to restrictive in terms of tokens provided. I am on their largest plan, and quickly run into their limits. And that is using it selectively in addition to codex.

Every tool call is essentially entire prompt so far sent again with the response and that's why cache rates are so high for agentic workloads. This really bites when using expensive models since most models are 1/10 for cached input.

If you are using their coding plan for coding, then yes you can easily hit such cache rates, with a good harness.

I’m getting 97%.

Breaking TOS isn't illegal per se. It just allows for denial of services, and may define terms by which the provider can reclaim costs.

Nulla poena sine lege?

What does any of this have to do with the legality of distilling Claude?

use these services if you want, but know that you're supporting aggressive escalations and companies that very clearly don't give a flying fuck about violating the law, much less your ToS

From my European point of view the same risk/concerns apply when using US providers

Yes, wont somebody please think of the shareholders whose IP had been stolen...

Source for 1? Are we sure those aren't hallucinations?

alignment crisis

Alignment is meaningless; as you've noticed, humans aren't all that "morally aligned".

If the tool needs safety measures it should be kept in a safe enclosure like we do with CNC machines, furnaces, and so on.

You didn't explain why it's illegal or why distillation is bad.

Like Anthropic and OpenAI are? After all, didn't they distill all the information in the world into their model(s)?

I mean, if they get to distill other's IP, why can't others distill their IP?

That is such a canard, IMO. FWIW, Anthropic and OpenAI encrypt "thinking" token outputs in their models, while Chinese labs don't. If anything, it's more likely that everyone is using open-weight models in their synthetic training data generation pipelines. It's way easier to distill from logits than it is to distill from hard tokens.

https://x.com/EricSimons/status/2099252922098061714

We weep for Dario, that he had to suffer such a devastating attack against his Terms of Service.

Eh, even if this was true, then they're merely stealing from thieves. Anthropic did break a ToS or two to get training data themselves.

I'm not defending their actions, but we should be clear about where the law currently stands: Anthropic was found to infringe because of the torrenting, not because of the training.

I have very little sympathy for thieves who get robbed of the goods they have stolen.

If you understand what they have achieved here, then the notion that they are bottle-necked on training data is absurd.

I wonder how you imagine that China built their own space station? Reliant on using American made duct tape, perhaps?

Do you realize how reasoning models are being trained nowadays? You design/build simulation environments to run agents in, with the environment providing the RLVR "verification" scoring. So why won't Ziphu use GLM to build their own RL training environments? Do you think they are not doing this?

It's presumptuous for them to assume that a reader of their blog is familiar with their product?

Also I feel like the obvious way to read the very first sentence is that GLM is a language model

As we develop GLM, the model sometimes exhibits capabilities that surprise us

z.ai is a fairly well known AI lab out of China and their GLM models are probably the most popular outside of Anthropic or OpenAI’s. I don’t think it’s presumptuous for them to not introduce themselves in a post on their own blog, I think you’re just a bit out of the loop here.

As we develop GLM, the model sometimes exhibits capabilities that surprise us, and even unsettle us.

Come on now

Also, why would they introduce themselves on their own blog?

A ai model family similar to Codex, Gemini or Claude.

Where GLM-5.3-Flash is the newest "small / fast" model.

It's only the top open-weights LLM in the world,

https://artificialanalysis.ai/#intelligence-category-tabs

It didn't read as harsh. Only unaware and you broadcasted that you don't have the decency to do basic searches.

Maybe my post sounded harsher than I intended

Appreciate the clarification. For me it was the "F" in "WTF" that tipped me. Other than that, it's more than fair for you to not know what GLM is. Things are moving so fast that I would be surprised if anyone can keep track of it all. Cheers, have a grand day!

Ziphu, aka Z.ai, is the company that makes GLM (a very competitive Chinese LLM).

Why would you be reading their corporate blog posts if you don't even know who they are?!

seems unusably slow, and is this for short context?

I am surprised at the lack of open-weights models in the >35B, but <200B range. I keep thinking about devices like the NVIDIA Spark and AMD Ryzen Halo, which have their 128GB of combined memory, but there are so few models made for that range. Nearly all the open weights distillations are for larger customer bases with <24GB VRAM.

Ziphu (who make GLM) use Huawei Ascend processors made by SMIC. Huawei use a combination of domestic memory from CXMT and leftover (pre-sanctions) memory from Samsung.

Just like the rest of the world, including the US (Intel, Micron), SMIC are currently using ASML lithography equipment (DUV, not EUV), but Shanghai Aishengna are now moving into early production with their own DUV machines, with SMIC and CXMT as early customers.

There is also a state sponsored Chinese EUV development underway.

Wouldn't it be refreshing if OpenAI and Anthropic were this open, and spelled out how they were using their own models during development and rollout?!

All I can recall reading from OpenAI about what they have actually done in the name of "RSI" is using one of their models to help automate the training process.

Look at how China does it. They'll happily sell us everything we want - more than enough of it, cheap enough, to put all of our own manufacturers out of business.

Seems to work for them.

US companies should now be more worried about Chinese companies flooding the market with their, hopefully, very affordable GPU's. The scale at which they can manufacture stuff is unmatched anywhere else. Nvidia can kiss goodbye to their 75%+ profit margins.

Almost everyone knew that these sanctions would backfire within a few years. You can't really put sanctions that have noticeable negative effects on bigger economies. They only work for small to medium economies. I believe sanctions on any economy in top 10 would fail.

Same thing with Trump not helping Ukraine and berating NATO. He thought he held all the cards, but now Ukraine has a thriving battle-tested drone industry, UK and France have stepped into replace the US with advanced missiles and anti-missile systems, stepping up their own production and transferring IP to Ukraine.

Now, the US is left out in the cold with little influence left, themselves now the ones with an anti-missile shortage.

It was evident that this will happen.

Compared with our initial baseline on the same hardware, we achieved a 3× improvement in end-to-end serving performance, reaching hardware efficiency and per-token cost comparable to mainstream NVIDIA GPUs. This demonstrates that Chinese chips can support frontier-model inference efficiently and economically at scale.

It created demand that would not have been there without restrictions

For one you would have to use Claude if you pick it. But seriously there is no way for you to determine if one is a better offer than the other, when the usage/tokens/credits are vague, detached, and won't tell you much without trying both.

    > Why would I pick GLM over Claude?

To support the company that makes their model weights available for download, while Anthropic lobbies to restrict access.

Anthropic: 17 USD (pro), 100 USD (max) GLM: 80 USD (pro), 168 USD (max) -> with "limited-time event" discount this is 56 USD and 117.6 USD

GLM's "Max" plan is (was?) equivalent to 3x Claude's 20x ($200) plan.

    "We implemented a series of aggressive memory optimizations, including..."

This whole thing sounds like industrial scale auto-research, but done by people who actually know what they are doing.

Python acts as an orchestrator of accelerator libraries and does none of the inference math directly

Have you seen how bloated and slow Python is?

Yes, but it's calling C code.

Time to tackle consumer GPUs next, since I’m not getting that Intel Arc B770.

If only this infrastructure could handle all the traffic. I've tried using glm via z.ai - and it's a snail kind of slow.

And at the same time you have pretty strict limits to your usage, so in many cases you can't even let it work all night, as you will reach your limit faster than that.

what prevents US AI labs from doing the same level of software optimization?

Because they don’t have to. Most of the time money would buy you newest and/or more hardwares so there’s low/minimal interest to optimize the code or approach.

They have already been doing it for months https://openai.com/index/openai-broadcom-jalapeno-inference-... . OpenAI on their custom chip brought up lightspeed deepseek as experiment by using AI in the exact same way as this zAI blogpost. And the kernel optimization contests/etc have all been havily done through AI based optimization loops for half a year+.