IMHO, cheaper inference means higher costs overall :) because everyone will use more thus driving up the investment required to stay current or to compete.

Switching models is also kind of easy but not plug-and-play. Most harnesses out there do very poor job with the open weight models. Unlike Opus, GLM 5.2 ends up in loops and hallucinates a lot more. If your harness is built on the expectation that the LLM will perform well, then switching to GLM 5.2 will be an uphill struggle. We had to refactor our harness and introduce more defences because of GLM.

The cost savings are substantial. Obviously it really depends on your workloads but it is noticeable cheaper for agentic work. Coding - I don't know. We do have some coding agents on GLM 5.2 and what I noticed with some landing page experiments that the results between GLM and Opus are identical - they might be using the same training data? Obviously Opus is still substantially better model. I don't think there is an argument to be made here but GLM 5.2 is cost effective and really good too.

Overall, we switched all of our internal agents to GLM 5.2 and because it is Open Weight we are in talks to get the model from certain geo locations giving us more freedom as well as extra protection.

Overall I think this industry will be in much better place because of GLM 5.2 and whatever open-weight models come next.

Agent systems only increase the gap between frontier and open models. Open models still experience more tool call failures, run longer loops, and get stuck more often. Until that's resolved (and it's obviously technically possible) people will be forking out for a better agent experience.

GLM5.2 is not the concern, it's that there's many good Chinese models and labs, glm, qwen, deepseek, kimik, mimo, longcat, minimax, hy3, Ernie, etc

They have figured out how to train, plenty of them and are consistently doing so

The blog author complains of "lack of/poor web search capabilities" in GLM, but you can always use it against an MCP of which there are many. For applications where I am not concerned about my queries being passed through a US provider, I have had success with exa[1]

There are also other ways to give it context without web-search. For example the various MCPs that make `man` pages available.

I've also found GLM to be quite strong for coding tasks without the need for web search. So it also depends what you're doing.

[1] https://exa.ai/

Agree with the web search point. (I would like for Perplexity to start to offer more models out of the box integrated, like they do now with OpenAI/Gemini models.)

Somehow the blog post seems naive. Yes GLM 5.2 is good and cheaper per token, but margins are a result of supply and demand. Now demand for quality and quantity of tokens is increasing at least quadratic or cubic (more users * more tasks * more tokens per task). On the other side you have real infrastructure constraints on the supply side. Openai and Anthropic have large commitments and contracts that enable them to get access at a scale of compute that is not obviously going to be available for open source model hosts. And you see it, glm 5.2 inference is less stable and higher variance than any of the bigs labs.

Why is SpaceX not hosting glm 5.2? because they make more money with renting out to Anthropic and Google.

> I'd be very surprised if it wasn't more than 50% cheaper for nearly all workflows, for a very similar level of quality.

If your using pure API ... providers like neuralwatt cut that cost down even more by using energy as the actual cost. So GLM 5.2 is more expensive then GLM 5.1 on their service (those thinking tokens), compared to API costs, its dirt cheap. And way more tokens then the zai subscription delivers.

We are seeing a move towards more realistic pricing on actual consumption based usage. Be it DeepSeek, Xiaomi (MiMo), or zai's GLM via neuralwatt.

The main issue facing subscriptions a-la-carte usage, is that a lot of the heavy hitters really drain the resources. And that as a business model can not survive without ...

a) increasing the prices. b) everything goes to actual token/energy usage based billing but with more realistic pricing, and not the bloated API prices that are focused on companies.

We shall see what the future holds but things will change.

People have been making claims about the commoditization of llms since chatGPT, and they've been wrong every time as quality and prices and differentiation have increased.

> It turns out that nearly every agentic session does a lot of web searching for looking up items

This is why Google will win the race over most of its competitors. They own search.

I run a 2 Claude setup (one architect, one coder) and have been using it extensively. But I don't like the fact that I have to pay $200/month to use it. I'm going to try GLM with my current setup.