[xlii]

I've been checking out GLM 5.2 on some projects and few thoughts on it:

- it takes it sweet time to get code rolling, not the fastest model by any means

- it strays a lot during discovery/planning but then corrects

- it's not steering friendly, as it hallucinates things that it doesn't follow later on

- its output is quite good

A sample use case: I was optimizing rendering on Swift+Zig codebase. It chocked on 5k data entries.

GLM 5.2 spent 20 minutes building the benchmarks and getting data out, which made me frustrated so I blocked non-editing tool access and went AFK, after approx. 30 minutes I found that it used already-made benchmarks and some "conclusions" to optimize 3 choke points. Output pointed that it couldn't validate suspicions and asked for more data.

Implementation worked well, it was idiomatic and non-intrusive. I would even say that it was more idiomatic than GPT 5.5 effects on same repo.

I would opt in in using it more BUT GPT usually completes same requests 5x faster.

GLM 5.2 was spark for preparing and running inside isolated containers with JJ workspaces (so that multiple can be ran in parallel).

[trollbridge]

I used it the other day for something of low importance that other models simply weren't figuring out and I didn't want to burn up Opus 4.8 on. (It had to do with overriding left-click on a macOS menu bar and then making Ctrl+click or right click bring up the menu like left-click normally does, and doing all this conditionally.)

Switched the model to GLM-5.2 halfway in the middle of a troubleshooting session (didn't even bother to reprompt, just changed it in the middle of its reasoning), gave it a few minutes, problem fixed. This is with the subscription based allocation on OpenCode Go, where a problem like this would completely burn up my Opus for the current 5 hours or even the current week.

[jeremyjh]

Its also nice that you can see its entire reasoning trace. I can see it going off the rails - or see something I forgot to tell it - and stop and correct it. Or I'll learn WHY it made the choice it did and not have to question it after.

[jauntywundrkind]

Strong agree! I deeply appreciate this aspect of GLM. Watching it think & being able to nudge early is incredibly useful. Being able to point at bad assumptions is incredibly useful. Watching what it's seeing is super informative.

It's always a shock to me how opaque most other models are!

It also is pretty resilience to letting you inject in while it's working without going off course or while getting back on track after, which I appreciate

[Imanari]

This mirrors my experience. I have been using it in Pi. It is smart and output is good but it is not efficient in getting there.

[ju-st]

which thinking level? max or high?

[Oras]

Also pricing, I wanted to give a try, but when pricing is only 30% cheaper than Opus, I wouldn't go for it with these issues.

[chpatrick]

It's pricing is a lot cheaper if you can run it yourself.

[jeremyjh]

What?

It is less than 20% of the cost of Opus at API rates. 1.40/4.40 vs 5/25.

[cmrdporcupine]

Not when you factory in token efficiency. It burns a lot more tokens to do the same job, so when I compared to GPT5.5 I was frankly not really much ahead, and with weaker thinking.

Maybe makes sense if you have z.AI's (not greatly priced) subscription plan, but it's not competitive against an OpenAI or Anthropic monthly coding subscription plan. I burned through almost $10 worth of tokens just doing an hour of work.