Sure, but whatever you do, don't buy their (Z.ai) lite plan.
I feel like i threw 15 dollars in the sea. I'm getting rate limited after 3-4 prompts. You get way less value than just paying 25 dollars for Claude or OpenAI models.
GLM-5.2 is the new leading open weights model on Artificial Analysis
91–100 of 476 posts
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#92Artificial Analysis coding benchmark shows GLM5.1 on high pretty close to GPT5.5 xhigh in cost to run, with GPT5.5 on medium significantly less expensive. Compared to GPT5.5 medium GLM5.1xhigh is twice the cost and half the intelligence. They don't have GLM5.2 on there yet, but that'd a big gap to bridge. https://artificialanalysis.ai/agents/coding-agents?coding-ag... I thought I was "holding it wrong" until DeepSWE…
I gave GLM 5.2 a spin on openrouter yesterday and it was mostly fine but it racked up $5 in token use in 30 minutes of (relatively slow) work.
It's easily 4x the cost of DeepSeek V4 but I didn't actually feel the results were that much better. I had GPT 5.5 in Codex review it after it was done and there was plenty of slop to go around.
Having better luck with MiniMax M3, from a cost/benefit ratio.
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#93In my tests[0] GLM-5.2 is not much better than GLM-5, and overall DeepSeek V4 Flash seems to be the better/more cost-effective choice: [0]: https://aibenchy.com/compare/deepseek-deepseek-v4-flash-high...
man, i love dsv4-flash but i found its weaknesses in complex projects with multiple moving parts. tried kimi 2.6 and it understood and could work on the task. bigger is better..
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#94Earlier quoted context omitted.
Be careful about unofficial providers, a lot of them misconfigure models or stealth quantize them. For a while the difference between Kimi on the official API and most third party providers was 20-40%.
OpenRouter should be penalising or banning for this.
This is my biggest complaint about OpenRouter and I'm a fan. Might be pretty tough at scale?
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#95I have been trying out GLM 5.2 and I am really impressed by it for the most part.
To all people on Hackernews, I am curious as to what agent harness are you using it with.
Previously I was using opencode and then I switched to using Opencode + obra/superpowers and creating custom skill.md themselves for it. I found things to take more time and intervene more but the result of it has been that I have found it to work better.
Now I have also started using oh-my-pi as well and I found it to be faster compared to Opencode.
I am unsure how much of there is a difference to it and how much of things are placebo but what is your opinion regarding the best Agent harness for GLM 5.2?
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#96Earlier quoted context omitted.
This is a problem I find with opus is will spend so long thinking then going “but wait what if” To point where I stop it and simple tell it to “start writing code you can work it out as you go along” Seems writers block also effects LLM
Fable was 20 times worse on that. It's clear it was the vibe coding model, as like no other model before, fully turned you into his assistant instead of the other way around.
Could it be possible, these firms are optimizing for two things: a) Better performance. b) Gathering data from you to further improve performance later. I've also found the huge amount of planning rather than iteration frustrating. I've felt like I'm teaching a junior!
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#97It seems to really be a nice step-up and is getting quite close to the frontier. I wish they'd start focusing on the reasoning efficiency now, though. I have a simple (relatively) test task to evaluate LLMs: writing a simple math evaluator library in Nim (it's about 400-600 lines total max), and GLM 5.2 (xhigh which maps to max effort) spent over 15 minutes (!) reasoning, spending about 45k tokens, before it finally…
This is a problem I find with opus is will spend so long thinking then going “but wait what if” To point where I stop it and simple tell it to “start writing code you can work it out as you go along” Seems writers block also effects LLM
I've been having success with Opus but you REALLY have to tame it. Long prompts that list what files to look at, relationships between entities, etc... I went from regularly hitting my daily limit to almost never hitting it. Oh, and also I was being lazy with small changes and stopping that helped a lot too. As you said, it gets in these loops where it's just churning and if you don't stop it it can go on for way too long.
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#98It seems to really be a nice step-up and is getting quite close to the frontier. I wish they'd start focusing on the reasoning efficiency now, though. I have a simple (relatively) test task to evaluate LLMs: writing a simple math evaluator library in Nim (it's about 400-600 lines total max), and GLM 5.2 (xhigh which maps to max effort) spent over 15 minutes (!) reasoning, spending about 45k tokens, before it finally…
> Of course if you convert those values to actual request cost, GLM 5.2 will probably beat GPT 5.5/Opus 4.8, but speed matters for a lot of people, I think.
GLM5.2 ends up being far more expensive than I thought it would be when I tried it on openrouter. I ground through $5 USD worth of tokens quite quickly.
And this was high, not max.
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#99Knowing very little about how to run these, how close are we to medium or larger businesses starting to buy hardware to run models like this to keep the models local? It’s expensive, and not as capable as the frontier models, but would have some pretty big benefits around privacy and agency.
This is not a new situation. This was happening also when good vision models like alexa net were coming through, especially for OCR. Companies had choice between cloud or self hosting with GPUs. But turns out, problem is usage patterns.
Your usage will peak during certain timezone work hours(even if you are a huge multinational company most of your engineers/users tend to be from only a few locations), so then you have a bunch of gpus doing nothing the rest of the day. especially with latency sensitive stuff, this is a decades old tradeoff problem, its not unique to llms
Re: GLM-5.2 is the new leading open weights model on Artificial Analysis
#100Earlier quoted context omitted.
Fable was 20 times worse on that. It's clear it was the vibe coding model, as like no other model before, fully turned you into his assistant instead of the other way around.
Could it be possible, these firms are optimizing for two things: a) Better performance. b) Gathering data from you to further improve performance later. I've also found the huge amount of planning rather than iteration frustrating. I've felt like I'm teaching a junior!
I think they simply optimize around E2E benchmarks, none of those benchmarks is designed as multi turn assistance to the user, but going from a prompt straight to the final solution.