I have noticed that Opus and GPT 5.5 are very good at adjusting their thinking / reasoning intensity depending on the task at hand, something the open weights models are still not as good at.

In addition to that, some of the open weights models like GLM 5.2 or DeepSeek v4 Pro tend to be MUCH slower when generating tokens, which contributes to the perceived slowness. Although I wouldn't call models like GLM 5.2 slow by any means, e.g. it is currently one of the fastest models inside Notion today.

Probably the data center where the model is running more than anything. Another option is if Opus is using anything like a Mixture of Experts approach, in which case the amount of the model loaded in memory at one time could be smaller than GLM.

Could just be infra. I'm betting Anthropic is much better prepared.

Right, just give the text llm access to a vision specific agent and that problem can be solved. Or if you really want let it even call Opus with an image - seems like you’d still save money

The amount of times I have had to spend tokens to attempt (in futility) to convince a proprietary model that the request I asked it to perform on code that I wrote is safe/legal/moral is insane.

Part of me wants to believe they really do care about protecting the world from... something... I don't know quite what exactly tbh... but it must be costing them a small fortune to scan each input and output against N guardrails and they are a for-profit corporation who could easily turn a blind eye to all of this and simply say "what you do with this model is on you" like I would expect most corporations to.

Strange times.

For me, time doesn't matter for LLMs. I can start a bunch of tasks, and I'll review the PRs when they're done. Faster is nicer, but if the task gets done correctly, I'm good.

I cannot unsee it.

There was this dude here not long ago who bought like $70k worth of gpus to research, and if I'm not mistaken his research was something related to make llms sound less llm-y. I wonder how it goes for him.

I think a bunch of real humans started to adopt the LLMs writing style.

This is excellent feedback thank you! These LLMisms in writing are a challenge I am living with currently and trying to improve on. The technical writing industry is taking a huge knock right now with companies demanding more work in less time with a big drop in quality, day to day I get less and less time to work on the quality in the prose of my work. We are working at the frontier of this right now, so we are the most heavily effected, but also get to experiment with the changes first which can be both stimulating and very frustrating.

Yes, and it's really grating. It's like half of all new writing is done in the same "voice" now.

My impression is that individual subscriptions are the loss leading hook. The money is made on Enterprise token contracts.

Employees and students used to coding with thousands of dollars worth of tokens (on a 20/100 dollar plan) will push enterprise to spend.

Having a Chinese model that is competitive won't displace this enterprise spend. But an open model hosted in the US/EU might.

The existence of GLM 5.2 puts a ceiling on how much OpenAI/Anthropic can charge for API Access.

Taking a view from outside the USA, European companies just had Fable taken away due to US export controls, and before that Anthropic announced it is holding their data for 30 days. There is immediate value to these firms to build their infrastructure around an AI that won’t be pulled away from them. And outside of Europe, other countries are more price sensitive and don’t have the same fear of building relationships with Chinese companies.

This is an important point. I suspect API pricing will eventually disappear just like how paying for an MMS disappeared. It's an antiquated model. The bulk of the work is being done on "coding plans" is my wild guess.

It's annoying that the plans are so restrictive beyond usage limits. Understandable maybe, but annoying. In practice, only Anthropic (and maybe Google) are really restrictive though. They really scared me away with their policy of charging API rates after the fact if they consider your usage not TOS-aligned. This might be an ungrounded fear that I have, but I feel this is something they'd do so they scared me away.

> But the only people that use API billing for their coding work are large corporations

As well as people using 3rd party harnesses like OpenCode.

> At the same time, none of these companies will use a Chinese API for their employees

So who are Amazon Bedrock (who serve GLM) targetting?

Individuals are presumably going with one of the cheaper US providers such as DeepInfra ($0.18/M cached input for GLM vs $0.50 for Opus) or Fireworks AI.

The value of these models is that you can run them on your own hardware.

A company can buy a NVIDIA B300 and serve it's developers in house with unlimited tokens.

> At the same time, none of these companies will use a Chinese API for their employees.

nice try but you intentionally ignored the entire Chinese market & Chinese big corporates. there are 130 Chinese companies in the fortune 500 list, with an average revenue of 80 billion USD each. do you think they are going to sign up for Claude, Codex or GLM? now consider South East Asia, Africa, Middle East, Middle Asia and South America, tell me why their large corporates won't be using GLM API billings?

your western centric view of the world is totally out of date, like it or not, 2026 is vastly different from 1996, the US no longer controls high tech whatsoever.

Also, I was testing out the GLM 5.2 using Openrouter because that's where I've got an account with some money and then when I wanted to perhaps subscribe for a better deal at z.ai, their infra was clearly overloaded to the point the 5.2 was timing out on 100% of chat requests, so perhaps I will try later when the infrastructure catches up with the model capability. Only then I can make sure their subscription is worth it.

I'm on glm pro subscription and I get so so so much more usage than Claude or Codex! I hammer on glm all day. It's a more expensive plan, but I would need a much much much bigger plan for codex or Claude to do what I do.

Calling GLM-5.2 locally hostable is a bit of a stretch. It's 1.5Ti of weights at bf16. FP8 requires >800Gi of VRAM which is well into data center multi-GPU systems