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GLM-5.2 is the new leading open weights model on Artificial Analysis

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Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#301

Post by SubiculumCode »

mikeocool wrote: post #87
vorticalbox wrote: post #29

Earlier 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

Seriously. Whenever I read the thinking output I get mad and turn down effort to medium or low. Just output the code and we’ll work through it! I feel similarly about having codex review claude’s plans. I don’t think I’ve ever seen it catch a major issue. It just points out things that would have inevitably been addressed during implementation anyway.

A lot of times this is how humans work. Just start 'putting words on paper', 'think by doing', etc. sometimes it's more efficient to see why something won't work after writing a bit of it, and sometimes you get lucky and it works right off the bat

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#302

Post by WASDx »

0xbadcafebee wrote: post #203
simonw wrote: post #110

I was surprised that GLM 5.1/5.2 are not vision models - they are text input only. That's actually pretty uncommon these days. All of the OpenAI/Anthropic/Gemini models accept images, and so do the other leading open weight families - Gemma 4, Qwen 3.6, Kimi 2.x. In GLM's case image input would be useful because it's a model that scores very highly for tasks like web design, but without image input it can't take a sc…

Configure a subagent in your coding harness to spin up a new sub-session with any vision model for those tasks and feed the result back to the main model. No need for "one model that does everything"

Are you suggesting it should summarize the image in text or generate it in HTML or something else?

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#303

Post by FooBarWidget »

benjiro29 wrote: post #59
Tiberium wrote: post #2

It 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…

GLM 5.2 Max = Opus 4.8 Max in thinking behavior. The thinking chain is so similar, and so is the amount of token usage on the output. If you want reasonable token usage, you need to run it GLM 5.2 at High. There is little drop in quality from Max to High (for most tasks). And it cuts token usage by 2 a 2.5x. GLM 5.2, Max is really something you only need for complex tasks. In essence, GLM 5.2 is Opus 4.8 its little b…

With such ridiculously long thinking traces I'm surprised max outperforms high. After all, performance falls off a hill after a certain amount of context, and long thinking traces can fill that up really quickly.

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#304

Post by drob518 »

happyPersonR wrote: post #217
RyanHamilton wrote: post #96

Earlier quoted context omitted.

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!

more thinking == more tokens === more money LOLL

I think they are optimizing for one-shot performance because that will drive usage. They can’t afford to look bad in the benchmarks. And if that means consuming an order of magnitude more tokens, well, that’s good for business, too.

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#305

Post by cmrdporcupine »

JSR_FDED wrote: post #288
JKCalhoun wrote: post #229

Earlier quoted context omitted.

"…Anthropic Marketeer strike force…" Might also just be the result of "good will" (that the company has deftly fostered). Other companies might learn from Anthropic in that regard.

“Good will” is easier if OpenAI is your yardstick

As evil as Google is as a company these days [cough disclaimer, used to work here, so biased] I can't help but think that if Gemini didn't... suck, and if they had a coding model at the same quality as GPT 5.5 or Opus 4.8 they'd be completely cleaning up purely on the basis of relative reputations of the companies.

That Google is dropping the ball so badly, or just disinterested in the coding side of things... is either a sign of incompetence, or a lack of interest in losing money in that space. I wish I knew which.

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#306

Post by hereme888 »

bityard wrote: post #289
hereme888 wrote: post #266

Hmmm... GLM insists it's Gemini. https://github.com/zai-org/GLM-5/issues/79

It's a surprisingly common misconception that models contain any metadata at all about themselves in their weights. If you ask them, "What model are you?" they either retrieve the answer from the system prompt, or they hallucinate an answer. Same goes for questions about knowledge cut-off, how many parameters they have, the source of their training data, etc.

Huh. That kinda makes sense. So you think it's hallucinating it's model name?

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#307

Post by drob518 »

gbingles wrote: post #265
robmccoll wrote: post #126

Earlier quoted context omitted.

That's interesting. I gave nearly the same task to Gemma4 31b as a test yesterday. Write a symbolic math engine in Typescript that can perform evaluation and simple expression reductions over +-/*(). It performed the task correctly with minimal reasoning - much fewer reasoning tokens than output tokens.

Tbh, so what? I googled "symbolic math engine in Typescript that can perform evaluation and simple expression reductions over +-/*()" and got what looks to be viable answers without using any AI model at all. Reciting well established things from memory isn't terribly interesting. Show it a novel codebase and have it implement something within it.

So, a natural question would be why a model would ever get it wrong?

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#308

Post by drob518 »

kristopolous wrote: post #104

I have a script that ranks these based on codingindex from Artificial Analysis. All it does is pull a json from their main table page and parses it with the fields I care about (coding). There used to be a mailing list associated with it but eh ... there wasn't much interest. I use the script every day though. Current partial output score age size name 47.1 58 large Kimi K2.6 47.5 54 large DeepSeek V4 Pro (Reasoning,…

Maybe your script could sort based on score.

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#309

Post by simianwords »

stymaar wrote: post #296
simianwords wrote: post #200

Earlier quoted context omitted.

> How can you be sure You are free to believe that they are doing all this. Or you can simply believe the intuition that models are getting cheaper by the day. I can run Gemma 4 31B from my laptop today.

> Or you can simply believe the intuition Sure, you can believe you intuition as much as you want, but telling strangers over the internet that they are wrong because “I trust my intuition” is… awkward.

At some point it does come to intuition. Even if the companies IPO and share their financials, you can always argue that they might be lying.

Re: GLM-5.2 is the new leading open weights model on Artificial Analysis

#310

Post by SubiculumCode »

gbingles wrote: post #265
robmccoll wrote: post #126

Earlier quoted context omitted.

That's interesting. I gave nearly the same task to Gemma4 31b as a test yesterday. Write a symbolic math engine in Typescript that can perform evaluation and simple expression reductions over +-/*(). It performed the task correctly with minimal reasoning - much fewer reasoning tokens than output tokens.

Tbh, so what? I googled "symbolic math engine in Typescript that can perform evaluation and simple expression reductions over +-/*()" and got what looks to be viable answers without using any AI model at all. Reciting well established things from memory isn't terribly interesting. Show it a novel codebase and have it implement something within it.

TBH, while your point is a fair one, your attitude is off-putting and needlessly condescending.
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