They must have hit really hard scaling limits if the prices were hiked so much so quickly.
And you can bet GLM is still ridiculously subsidized, just not as ridiculously as Anthropic and OpenAI.
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.
Can I ask where are you using all those tokens?
Also why Meta gets a +1, just charge less money on the training path.
If you factor that in, then there are clearly different tiers: one you can trust, and one that may well just be saying that to increase market share with little reputational or legal consequences if they are found to be lying.
These are not equal.
To be fair, none of us are sure of anything and I think that’s the part that’s most irritating
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.
The max plan will provide ~1,100 USD of GLM-5.3 or ~260 USD of GLM-5.3-flash per month for 168 USD. I can personally attest to these numbers through omp (~97% cache hit rate).
Unless you are able to highly parallelize (your work, you won't be able to hit your hourly or weekly quota using the flash model simply because it's so slow.
They give you ~3x more flash tokens, which maybe comes out to ~2x more actual work after accounting for the extra thinking it does to achieve the same result. The mental model, for not getting angry, is 5.3 is fast mode by default, and you can disable fast mode for 2x the work output at 1/3-1/10th the speed.
They're serving me 5.3 at ~40 tok/s and 5.3-flash at 30 tok/s (according to omp).
That is shocking. Is it per-token I wonder?
In the PRC, they[1] leaked tons of national secrets on the PRC's latest AI campaigns, the inner workings of their "opinion monitoring" (read: performative panopticon) and "stability" (read: violent oppression) departments, Chengdu's whole CCTV network, direct-energy weapons plans, espionage activities in Syria to hunt down Uyghur refugees, and god knows what else that Anthropic didn't divulge to us common folk.
In the US, it's very clearly an attempt to rip off a competitor. I'm not sure how else you could possibly see it. Even if you're a distillation fan in general (which A. why and B. plz don't), they did this through a network of Japanese and Signaporean shell accounts, presumably at least some of which were abusing Anthropic's subscription service in a ToS double-whammy, as it would be exorbitantly expensive otherwise. They also had to hack around Anthropic's API to get CoT traces, which seems impossible to explain away as anything innocent.
I've been beating the "China isn't necessarily an enemy, it's gonna take us all to handle AI" drum for literally years, but this attack was just... gross. Gross in scale and gross in arrogance. Not a good sign for the dawning alignment crisis, to say the least :(
TL;DR: 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. So... buyer beware, I guess.
[1]: For clarity, Z.ai was not alone in this, nor were they most egregious attack -- Moonshot.ai (kimi) took that coveted prize. DeepSeek was involved, too.
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.
No real reason to respect any terms they might want to impose. Besides, if you want to break TOS, just have an agent do it; "everyone" running these things agrees there's no corporate or moral liability for what your AI does.
Honestly, I have no idea what z.ai is either (I'm aware of an AI-enabled editor called Zed, but that's under zed.dev), so it's a bit presumptuous from them to assume that everyone 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
Come on now
Also, why would they introduce themselves on their own blog?
Where GLM-5.3-Flash is the newest "small / fast" model.
Why would you be reading their corporate blog posts if you don't even know who they are?!
One drive gives about 2 tok/s; striped across four drives it reaches 3.5 tok/s with byte-identical output, and our best internal build with a not-yet-published patch does 4.2.
Method and numbers: https://github.com/argonautlabsai/argodrive (built on antirez/ds4).
GLM has in the past been more technical rather than speculation about future development on RSI etc.
Also curious whether those 100k accelerators are entirely locally made. If that's genuinely end to end on all components including lithography, memory, design etc then that is quite a feat.
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.
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.
In addition to Huawei who make the Ascend series that Ziphu are using, there are also at least a half dozen or so other Chinese companies also making their own AI accelerators.
> 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.
> Why would I pick GLM over Claude?
"We implemented a series of aggressive memory optimizations, including..."
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.