GLM 5.2 vs. Opus • Hacker News Board

Re: GLM 5.2 vs. Opus

#271

Post by InsideOutSanta » Mon, Jun 22, 2026, 4:43 PM UTC

mellosouls wrote: post #264

GLM-5.2 cost a fraction as much. Opus finished in half the time and shipped a cleaner game This implies Opus was potentially much (?) better value. GLM cost a quarter but Opus was twice as fast. So we are already at GLM actually costing half when you compare on time, without even considering the extra effort and time it would take to get Opus-par results. It's good to have cheaper options and very impressive to see t…

For me, time doesn't matter for LLMs. I can start a bunch of tasks, and I'll review the PRs when they're done. Faster is nicer, but if the task gets done correctly, I'm good.

Re: GLM 5.2 vs. Opus

#272

Post by InsideOutSanta » Mon, Jun 22, 2026, 4:46 PM UTC

One nice thing about GLM is that it has never refused a task. I'm working on a website that renders countries right now, and Anthropic's models regularly give me the old "This request triggered safety guardrails."

I'm not sure what exactly triggers it, but it seems to happen when it has to look at lists of countries. I suspect there must be at least one country name that triggers the safety guardrail.

You'd expect GLM to balk at something like Taiwan, but so far, it hasn't.

Re: GLM 5.2 vs. Opus

#273

Post by digitaltrees » Mon, Jun 22, 2026, 4:51 PM UTC

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…

Exactly this. I recently tried Claude code again to get the subsidy on fable rather than paying api prices and was so frustrated by how much it pushed autonomous behavior. It would start ignoring my planning documents, ignoring my coding conventions, reimplementing features and code already in the project (not sure it ever makes sense to have two auth systems in parallel or two websocket implementations for the same ui) and then in the most shocking interaction just refused to stop working and listen to my instructions. I think maybe it was because there was a subagent doing the work but it was a complete waste of time and effort.

I was using cursor, in large part because I could at least stop it when I need to.

I ended up building my own IDE from scratch so I can be more in the loop while also having the full agent experience.

Re: GLM 5.2 vs. Opus

#274

Post by faxmeyourcode » Mon, Jun 22, 2026, 4:53 PM UTC

I feel like another comparison worth looking at is purely cost.

Capability per dollar is something I care about:

    Opus API    $5/$25
    Sonnet API  $5/$15
    Haiku API   $1/$5

    GLM 5.2 API $1.4/$4.4

So you're really getting near opus level capability for the price of haiku.

Re: GLM 5.2 vs. Opus

#275

Post by somesortofthing » Mon, Jun 22, 2026, 4:55 PM UTC

this comparison seems kind of pointless if one model has vision and the other doesn't. obviously a model that can see is going to beat a blind model at making a video game.

Re: GLM 5.2 vs. Opus

#276

Post by digitaltrees » Mon, Jun 22, 2026, 4:55 PM UTC

echelon wrote: post #212

post-it wrote: post #180

Earlier quoted context omitted.

The streetlight effect: > A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "this is…

The minute an open model breaks through and beats Claude Opus/Fable, it's over. There are far more opportunities that can be served when the world's intellectuals have the raw weights and can fine tune, splice, distill, and reapply. Imagine having raw unfettered access to Fable. It can be refit to structural biology. It can be fine tuned on the repo for smaller context requirements. It can be run cheaper and air gapp…

I don’t think we need them. I think the models we have are good enough. It’s the orchestration layer that makes the biggest difference at this point. The open source models we have are capable of calling tools and the work is getting them to be capable enough to know which tools to call and what to do in response.

I think we are leaving the main frame era of AI and entering the PC era already. If there wasn’t a RAM shortage and we all had 2TB of ram and GPUs we would all have large local models or personal APIs serving our teams.

That’s why all the labs are moving to the App layer and moving away from being the API for intelligence like they were originally.

Re: GLM 5.2 vs. Opus

#277

Post by gertlabs » Mon, Jun 22, 2026, 5:01 PM UTC

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 intelligence on the first response, but fail to call tools or explore correctly which limits their usefulness for agentic coding.

Neither will be great for coding in a computational chemistry repo for different reasons, but the model with strong one-shot performance will be less likely to make subtle errors indicative of poor understanding, so we weight both capabilities into their final score.

The latest Anthropic and OpenAI models excel in both domains.

Data at https://gertlabs.com/rankings

Re: GLM 5.2 vs. Opus

#278

Post by [deleted] » Mon, Jun 22, 2026, 5:05 PM UTC

sanderjd wrote: post #197

post-it wrote: post #180

Earlier quoted context omitted.

The streetlight effect: > A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "this is…

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…

[deleted]

Re: GLM 5.2 vs. Opus

#279

Post by jeremyjh » Mon, Jun 22, 2026, 5:17 PM UTC

Sanzig wrote: post #217

cmrdporcupine wrote: post #168

Earlier quoted context omitted.

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.

Take a look at Ollama Cloud: https://ollama.com/pricing You get access to a whole bunch of bleeding edge open models including GLM-5.2, Kimi K2.7, DeepSeek 4 Pro, etc. Inference is run on US/SG/EU cloud providers with zero data retention policies. The $20/mo tier is very generous, in my experience.

They don’t have a statement about where it is run or data retention on the GLM5.2 model. They do state that for others, like MiniMax.

Re: GLM 5.2 vs. Opus

#280

Post by wahnfrieden » Mon, Jun 22, 2026, 5:21 PM UTC

digitaltrees wrote: post #276

echelon wrote: post #212

Earlier quoted context omitted.

The minute an open model breaks through and beats Claude Opus/Fable, it's over. There are far more opportunities that can be served when the world's intellectuals have the raw weights and can fine tune, splice, distill, and reapply. Imagine having raw unfettered access to Fable. It can be refit to structural biology. It can be fine tuned on the repo for smaller context requirements. It can be run cheaper and air gapp…

I don’t think we need them. I think the models we have are good enough. It’s the orchestration layer that makes the biggest difference at this point. The open source models we have are capable of calling tools and the work is getting them to be capable enough to know which tools to call and what to do in response. I think we are leaving the main frame era of AI and entering the PC era already. If there wasn’t a RAM s…

They are absolutely not good enough