Curious what people's experience is with these models. Anecdotally I tried these out earlier in the year and found it struggled with pretty basic full-stack coding I was doing, when Sonnet 4.6 and Haiku 4.5 didn't break a sweat. Was hoping to use it while my Claude usage was resetting but was disappointed.
I've been using GLM-5/5.1 for about 6 months and it has been a fairly capable model. I've seen a lot of mixed opinions that tend to align with harness usage so it is worth trying out a couple with a model before writing it off. For example, I'm using crush and have had a good experience while others using CC have had a much more mixed experience. For task complexity, I treat it as I would sonnet with the same care in building out plans/prompts before firing it off and letting it go.
I use intelliJ for much of my development and also set the built in AI tools to use my GLM sub (BYOK) and it has worked out well albeit a bit slow.
Overarll, it's my main model and has been getting better with each release.
Yeah, the harness makes a big difference in my experience. Some of the models don't even work with some harnesses, including some very big ones. And some are clearly distilled to work with specific harnesses.
I'd love to see some numbers though, on models/harness combinations.
https://www.tbench.ai/leaderboard/terminal-bench/2.0
I've got a GLM subscription (mostly because I like supporting open model makers, pretty sure my monthly usage is so low that pay-per-token would be more cost effective), so I generally use GLM-5.1 for any personal projects and I use Opus at work.
To be entirely honest I haven't noticed much of a capability gap between the two for the sorts of things I ask of an AI agent. Maybe Opus is _slightly_ smarter or slightly better at long-running tasks but the difference is slim enough it could just be a placebo from the Claude branding / hype.
I'm looking forward to giving GLM-5.2 a spin sometime soon and seeing how it stacks up. If nothing else 1M context is a great improvement, feels like between DeepSeek v4, then MiniMax M3, and now GLM-5.2 adding it 1M is rapidly becoming "table stakes" for agentic models.
Which specific models were you using?
In March I switched to Opencode + Kimi K2.5 and found it was a step behind. I switched to GLM 5.1 and has felt like a step above. Its probably some combination of me forgetting the baseline, model improvements, and OpenCode improvements.
$20 a month has been good enough for my coding use cases. I wouldn't call myself a vibe coder. Stuff I do is create graphs/visualizations, review, polish code, generate toy examples for learning.
They're pretty good for casual use. I mostly use GLM and occasionally sprinkle some opus via api in when I think it'll help
In my experience these models (glm 5.1) struggle after 100K tokens.
GLM-5.1 had a coherency bug at launch, it might be worth retrying it if you haven't in a while. It can now use the full 256k context as intended.
Interesting, will give it a try again, thanks.