Still on a z.ai legacy plan and their 50% discount for switching to standard plans tips the balance for me. So I guess I’ll reevaluate round about beginning 2028…
GLM 5.2 vs. Opus
81–90 of 367 posts
Re: GLM 5.2 vs. Opus
#82I 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…
The argument is flawed, there is no logical reason to assume a single prompt won’t be sufficient to constitute the complexity of a software project. It may not be practical in many cases but there is too much variability in what is considered a complex software project and in the sufficiency of instruction in a single prompt to make that claim and say it’s “by definition.”
And that prompt will basically be 2000 page spec Bible à la IBM circa 1960, see waterfall. Unless AI develops mindreading (and advanced mindreading at that), single prompt creation of actual complex software products will never happen. You'll one shot a simple non scientific calculator, but not Excel or Vim or Nginx.
Re: GLM 5.2 vs. Opus
#83I wonder how much tokens and time where used for the verifying part. Maybe GLM 5.2 instantly found the "solution" to read the screen pixel by pixel, but it could also have been a major token and time consumer.
Hi, author here, I cannot give an exact number for how many token the verification step took, but the verification GLM 5.2 ran was very stupid and definitely a waste of time. It read the pixel color data to try and verify the scene rendered properly. Which is really bad. Opus opened the game in a Playwright browser and took screenshots to verify the actual image. Which helped a lot.
Pro tip: You could use a multi-modal model to verify images as a subagent spawned by GLM 5.2, to get around this issue.
Re: GLM 5.2 vs. Opus
#84> GLM-5.2 cost a fraction as much. Opus finished in half the time and shipped a cleaner game. Off topic, but does anyone else instantly pick up on LLMisms like this? It seems like all the models have converged on this style of writing, and improvements aren't really changing it.
Yes, and it's really grating. It's like half of all new writing is done in the same "voice" now.
Re: GLM 5.2 vs. Opus
#85I wonder how much tokens and time where used for the verifying part. Maybe GLM 5.2 instantly found the "solution" to read the screen pixel by pixel, but it could also have been a major token and time consumer.
I could be wrong but I believe this is a non-vision model. Please weigh in to correct me bc I would love to be wrong
GLM 5.2 is text only, not multi modal. And Opus is multi modal.
Re: GLM 5.2 vs. Opus
#86GLM 5.2 has one big issue that will limit its meaningful success and that's the value of their coding subscription. Yes, in terms of API pricing, GLM 5.2 outperforms the competition. But the only people that use API billing for their coding work are large corporations, where these highly subsidized subscriptions are being fazed out. At the same time, none of these companies will use a Chinese API for their employees.…
My impression is that individual subscriptions are the loss leading hook. The money is made on Enterprise token contracts.
Employees and students used to coding with thousands of dollars worth of tokens (on a 20/100 dollar plan) will push enterprise to spend.
Having a Chinese model that is competitive won't displace this enterprise spend. But an open model hosted in the US/EU might.
The existence of GLM 5.2 puts a ceiling on how much OpenAI/Anthropic can charge for API Access.
Re: GLM 5.2 vs. Opus
#87While this is interesting, one single sample with different coding harness is not very scientific.
Yes I agree 100%. My next guide would do better to use identical harnesses.
Re: GLM 5.2 vs. Opus
#88Chinese models optimize for benchmarks and do poorly in real-world tasks
Not my experience at all, I have written about comparing DS4 vs Opus 4.8 on 16 real life work tasks on multiple posts. Also, every single lab does RL on benchmarks, which is why Opus 4.6 was the last truly great assistant, after it, all models tend to drift into implementation asap.
Hi, author here, can you link? I would love to read about this.
Re: GLM 5.2 vs. Opus
#89Earlier quoted context omitted.
Not my experience at all, I have written about comparing DS4 vs Opus 4.8 on 16 real life work tasks on multiple posts. Also, every single lab does RL on benchmarks, which is why Opus 4.6 was the last truly great assistant, after it, all models tend to drift into implementation asap.
Hi, author here, can you link? I would love to read about this.
Re: GLM 5.2 vs. Opus
#90> GLM-5.2 cost a fraction as much. Opus finished in half the time and shipped a cleaner game. Off topic, but does anyone else instantly pick up on LLMisms like this? It seems like all the models have converged on this style of writing, and improvements aren't really changing it.
I think a bunch of real humans started to adopt the LLMs writing style.
Indeed. I'm trying to develop a similar style. The phrasing in the quoted passage is really tight.