- 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).