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GLM 5.2 vs. Opus

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Re: GLM 5.2 vs. Opus

#331

Post by mycall »

gertlabs wrote: post #277
cultofmetatron wrote: post #19

I seriously dont' know all this big hullabaloo about one shot prompting. by definition, a single prompt wont' constitute the complexity of a software project. ergo, what you'll get is a series of assumptions made by the model based on preexisting code in its training corpus. I'd rather see a coding agent that can follow steps in a plan file to a T while following guardrails and adhering to the proper coding conventio…

One-shot performance often translates to the most difficult problems a model will be able to understand. We run an evaluation that tests both agentic and one-shot performance, and we find that Chinese models are almost universally very good at using tools and a harness to iterate towards a better solution, whereas their initial response ranks relatively low. Compare that to Gemini models, which have impressive fluid…

> The latest Anthropic and OpenAI models excel in both domains.

Is that because OpenAI models are not a single model but a cluster of models which specialize different domains?

Re: GLM 5.2 vs. Opus

#332

Post by gertlabs »

mycall wrote: post #331
gertlabs wrote: post #277

Earlier quoted context omitted.

One-shot performance often translates to the most difficult problems a model will be able to understand. We run an evaluation that tests both agentic and one-shot performance, and we find that Chinese models are almost universally very good at using tools and a harness to iterate towards a better solution, whereas their initial response ranks relatively low. Compare that to Gemini models, which have impressive fluid…

> The latest Anthropic and OpenAI models excel in both domains. Is that because OpenAI models are not a single model but a cluster of models which specialize different domains?

By domain, I really meant "tool calling" and "one-shot fluid intelligence"

Anthropic models were the original leaders in tool calling and agentic work, even when other models felt significantly smarter in (Claude Sonnet 3.5 vs Gemini 2.5 Pro, for example). OpenAI models were the opposite, starting smart (more correct solutions on the first try) and got better at exploring and iterating with tools in 2026. The latest releases (Opus 4.5+ and GPT 5.4+) excel at both.

Re: GLM 5.2 vs. Opus

#333

Post by tezza »

I've just put GLM 5.2 through my __qualitative__ benchmark. I was quick enough to capture Claude Fable, so now you can compare GLM5.2 vs Claude Fable vs Opus 4.8 vs Chat GPT 5.5

https://generative-ai.review/2026/06/glm5-2-from-z-ai-vs-cla...

I've structured it side-by-side. You can clearly see where the private models excel, and where GLM 5.2 is still really good.

Re: GLM 5.2 vs. Opus

#335

Post by digitaltrees »

noonething wrote: post #320
digitaltrees wrote: post #299

Earlier quoted context omitted.

I disagree because I am getting real work done. But I have three Mac studios with 1.5 tb of ram and built my own harness around large models in my own ide (propelcode.app). It’s not perfect. And opus ChatGPT 5.5 are better but they’re good enough

if you had the information to do the task perfectly then those open models should be fine no? if so, your just paying for knowledge at that point.

Yah that's my point.

Re: GLM 5.2 vs. Opus

#336

Post by wahnfrieden »

digitaltrees wrote: post #335
noonething wrote: post #320

Earlier quoted context omitted.

if you had the information to do the task perfectly then those open models should be fine no? if so, your just paying for knowledge at that point.

Yah that's my point.

Maybe write “I don’t think I need them. The models we have are good enough for me.” instead then

Re: GLM 5.2 vs. Opus

#337

Post by cmrdporcupine »

w0m wrote: post #311
wiremine wrote: post #290

Earlier quoted context omitted.

I ran a fairly large experiment last week, and the token usage wasn't bad at all. What softs of use cases are you seeing large token usage by GLM 5.2?

> are you seeing large token usage by GLM 5.2 the statement isn't "GLM 5.2 has large token usage", it's "GLM 5.2 has large token usage vs modern Opus". I haven't used it, but this wouldn't surprise me. I see ~30% lower token usage for better results with Opus 4.8 vs 4.6 (and i had great results with 4.6)

I'm comparing with GPT5.5 on Codex and it's not even a competition. GLM takes way longer and eats a lot of tokens getting work done, it's easy to rack up a big bill on openrouter. I tried the $20 plan from ollama, too, and ate through half a month of budget in a few hours and blew my daily limit twice and still had to get codex to complete it -- which it did with only 10% of my monthly limit remaining.

GLM is promising but it's pretty costly, all things considered.

Re: GLM 5.2 vs. Opus

#338

Post by cromka »

habosa wrote: post #295

At work we use Anthropic models and have basically no limits. So I am very familiar with what Opus can do. I also see the bills, I know what it costs. At home I make a point of trying other models / tools on my side projects. So I've been using OpenCode and trying tons of models via OpenRouter. I tried Kimi, Deepseek, MiMo, etc. GLM 5.2 is a _major_ step up from every other non-GPT/Claude/Gemini model I've tried. It'…

And you can use it in complete privacy if you so need.

Re: GLM 5.2 vs. Opus

#339

Post by sanderjd »

jerf wrote: post #218
sanderjd wrote: post #197

Earlier quoted context omitted.

Sure, for casual evaluation, I agree. But are there serious analyses that are evaluating this kind of thing? I mean, these are the kinds of things I evaluate in my own work when a new model comes out, or when I'm evaluating a harness. But this is all very ad hoc and intuitional. I'd love to start bringing rigor to it, but I haven't found much prior art on this. In another thread someone said that's because it's proba…

It's hard to bring much rigor to it. I'm not saying impossible , but it's not like it's completely obvious how to do it and people are just too lazy. Intrinsically, if I'm going to test a back-and-forth with a model I have a human in the loop making frequent decisions. Did the model fail or succeed at whatever rate it did that because of the model or the human? Did the testing protocols capture the actual problem, e.…

Yes, well put.

Re: GLM 5.2 vs. Opus

#340

Post by [deleted] »

stevenhubertron wrote: post #208

No one has really talked about hybrid and using Opus to plan and orchestrate GLMs work both through initial build and code reviews. That’s a true best of both worlds and there doesn’t need to be a winner.

[deleted]
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