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

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 brother, at a way, WAY cheaper price.

There has been really no training on Opus models going on, really, none i tell you! /sarcasm

> It seems to really be a nice step-up and is getting quite close to the frontier.

IMHO it's already surpassed them. I vastly prefer my personal GLM and OpenCode setup to the Claude Code and Opus one that I have to use at work. The former makes way fewer StackOverflow brogrammer-tier mistakes and is considerably better at following instructions. The harness UX is also vastly superior as it doesn't ignore, randomly change, or incorrectly report settings.

Maybe it's the harness and I'd have even greater success with OpenCode and Anthropic, but I think it safe to say that Anthropic's moat is evaporating.

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

Hopefully the recent work Moonshot did with Kimi K2.7 Code trickles in to the other open-model labs.

Per AA, while K2.7 Code is roughly on par w/ K2.6 in terms of intelligence, it uses half the output tokens to get there.

This is GLM 5.2 Max. GLM 5.2 High which use less than half[1] the tokens.

[1] https://z.ai/blog/glm-5.2

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.

As per stats in other comments, it is frontier, not close to frontier.

> 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.

I agree. I've noticed that it is quite smart but it has a tendency to doubt itself and overthink. I monitor its internal dialogue and prod it when it does this. They need to optimize the chain of thought early stopping.

Note that AA's coding index is only made up of two benchmarks: Terminal-Bench Hard and SciCode. I'm skeptical that it makes a good coding index. It ranks Gemma 4 31B above Deepseek V4 Flash. Having used both of those models for a broad variety of coding tasks I would choose Deepseek every day.

  score  age  size   name
  62.0   8    -      Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback)
  59.1   55   -      GPT-5.5 (xhigh)
  58.5   55   -      GPT-5.5 (high)
  57.2   104  -      GPT-5.4 (xhigh)
  56.7   20   -      Claude Opus 4.8 (Adaptive Reasoning, Max Effort)
  56.2   55   -      GPT-5.5 (medium)
  55.5   118  -      Gemini 3.1 Pro Preview
  53.1   132  -      GPT-5.3 Codex (xhigh)
  53.1   62   -      Claude Opus 4.7 (Non-reasoning, High Effort)
  52.5   62   -      Claude Opus 4.7 (Adaptive Reasoning, Max Effort)
  52.1   55   -      GPT-5.5 (low)
  51.5   92   -      GPT-5.4 mini (xhigh)
  50.9   120  -      Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
  50.7   1    large  GLM-5.2 (max)
  50.1   29   -      Qwen3.7 Max
  48.7   188  -      GPT-5.2 (xhigh)
  48.6   55   -      GPT-5.5 (Non-reasoning)
  48.1   132  -      Claude Opus 4.6 (Adaptive Reasoning, Max Effort)
  47.8   205  -      Claude Opus 4.5 (Reasoning)

Consider using decrementing score order (best on top)

Cool project! Side note: Kind of a bad practice imo to ask people to blindly execute bash from an unknown source.

Thanks for sharing. I'm curious: why didn't you sort with the score descending?

Maybe your script could sort based on score.

Would be interesting to see where gpt 5.5 pro extended is.

It got 46.2 on DeepSWE in Z.ai's own run[1]. That would put it between Opus 4.7 xhigh and Opus 4.8 medium.

[1] https://z.ai/blog/glm-5.2

with open models you can get a subscription with privacy, at the same cost as codex.

openai, google and anthropic subscriptions are not available with privacy.

looking at the link there it's interesting that going from cursor cli to codex cli take gpt 5.5 from 7th to 3rd. but they didn't do open model in codex.

so, hard to say it's for sure a model benchmark. maybe open models are just shit at swe agent harness...it's not the most parsimonious explanation though.

DeepSWE “feels” like the right benchmark in comparison to Artificial Analysis indices and other coding benchmarks. And by their metrics, GPT-5.5 is still king in token efficiency, speed, and overall intelligence per dollar.

https://deepswe.datacurve.ai/

Fable 5 is cool and all, but we have not yet seen GPT-5.6.

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.

> Some are even offering API rates at 3x lower than the official ZAI api rates

Looking at openrouter [1], some of the cheaper offerings are for quantized models. Not sure how much intelligence is lost in quantization. And they are not 3 times cheaper. Where did you find 3x lower prices for APIs? I am considering skipping open router and using them directly for that price.

edit:

I see, croft [2] 8bit for $0.50/$0.08/$2.20

[1]: https://openrouter.ai/z-ai/glm-5.2

[2]: https://ai.nahcrof.com/pricing

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%.

To answer the question in your first sentence - because it's VERY computationally (ha) expensive as a human being to keep up with all the options. It's also very hard to figure out how to run a model like this. There's no installer. If you really really care, which 99% of people do not, you have to google a guide, and then find out it's out of date...

I've tried a number of these, and the learning curve is very steep compared to "install Claude Code and pay $100/mo". There is no way saving me $50/month matters compared to figuring that out.

In my org everyone is extremely Claude-pilled to the point you’d think it’s the only LLM that exists, purely because it caters to non-engineers within enterprises.

> Why aren't more people talking about this?

Wasn't this released like 2 days ago? Everyone is still evaluating and playing around with it, things like the submission is just starting to come out. Give it some days at least before jumping to conclusions, ideally weeks.

I cancelled my claude sub after realizing I can burn 300m tokens a day of this quality, for $50 a month.

Isn't it closer to sonnet?

I’m not that interested in models that I can’t run on my desktop for ~0€, which is my AI budget.

> unlimited tokens for $50 a month

link?

> Why

imho everything but opus produces unusable code (fable was even better...), eg gpt5.5 seems to write the absolute worst code that still technically solves the problem; tbh I'd be totally willing to trade "raw intelligence" for "code taste"

more labs need to figure out whatever anthropic did to destroy everybody else on frontiercode bench

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"

I've been using Google ai studio as a free vision bridge. Gemma 31B is dummy capable at vision and at 1500 rpd its basically unlimited.

I don't see this being such a big gap. There are some use-cases for sure but apart from UX/UI work it is not really needed. Besides, none of the frontier models can replicate actual images - the can approximate at least in my own experience.

I had the same reaction with Deepseek V4 ! It would be more useful as a vision model

> while being a little bit verbose

Discovered today that they set reasoning effort to max by default. So that’s probably why

> GLM writing

This is honestly what I care bout the most now, which is how well they can write. I think we have reached a point now, if you know how to program, you can provide enough information for the models to pretty much do what you need.

What they still struggle immensely with is the writing which has too many nuances but they are truly getting better.

This is my workflow. And then once a day I copy paste the code into the free Claude Sonnet so it comes out actually readable.

After having got a taste of Fable 5 for me Opus 4.8 doesn't cut it any more -- and I don't know how to put this, I don't know if it's just me, but it's rhetorical flourishes are starting to really grate on me, never mind that it is at times deliberately weasel-wordy and economical with the truth until pressed. Opus 4.8 is definitely a stronger coding agent than DeepSeek 4.0 or Kimi 2.7 succeeding where they flounder and fail but its way of expressing itself conversationally is making me reconsider my subscription …

I know of multiple businesses in Europe that have been doing that for a while with 70B models, and are upgrading hardware to run the new crop of 700B-1T models (really started around Kimi K2, but buying and hosting that kind of hardware takes time)

Not everyone is willing (or even legally able) to send their trade secrets to OpenAI or Anthropic

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

It’s a ~750B model so still a hell of a lot of vram

Would need to be a pretty determined medium biz

So far there seems to be one major use-case for complete privacy, and that is legal work. You don't need top of the line models to search vast amounts of text in discovery and it needs to be completely confidential. There's quite a few lawyers over on r/localllama showing off their multi-GPU builds. Coincidentally they also have the vast funding required for it.

Unless you have genuine national security concerns, you’d be better off just negotiating a commercial agreement with privacy protections with a couple of existing vendors.

> how close are we to medium or larger businesses starting to buy hardware to run models like this to keep the models local?

Years.

Even Microsoft said they don't have enough for Github and need to call Amazon.

Getting a few even at decent prices is hard. Unless the shortages goes down...

I think they’ve just picked poor peer examples. Instead of choosing other models near 5.2 on the intelligence scale, they’ve picked some open models from further down the scale.

Some models are heavily subsidized. Total params & active params are better measurement of inference cost.

The only benchmarks that matters is your actual task.

I've had models that benched poorly but performed great. And I constantly see models at near the top of AA, which are terrible.

There doesn't necessarily seem to be a lot of overlap between benchmarks and real world usage. (Let alone common sense!)

As far as they go, though, these harder benchmarks match my experience more closely:

https://deepswe.datacurve.ai/

and https://cognition.ai/blog/frontier-code

Where we see "top" models drop way down in score when given longer tasks.

That being said, I've had a reasonably pleasant time with GLM-5.2 so far. (And have had an OK time with DeepSeek as well.)

By the time I'm done testing all the Chinese models, they'll be obsolete :)

According to reports in this thread it is somewhere between Opus 4.7 and 4.8. This is effectively frontier.

I think the problem is, as can also be seen on other benchmarks, is that most models nowadays are focused more and more purely on tool calling and coding.

This means, that models are losing more and more general and domain-specific knowledge.

Look at those graphs on ARtificialAnalysis, GLM-5.1 still performs similarly or better:

AA-Omnisicence Accuracy: https://i.snipboard.io/5DYmpx.jpg

IFBench: https://i.snipboard.io/74kg0R.jpg

I still feel like models are not getting any smarter for a few months already, they just changed their training to be focused more on some areas than others, so shifting the intelligence from one place to another, not necessarily increasing the overall intelligence or "AGI" score.

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..

Are you using it for long context windows? I burn through my 5hr quota with GLM almost instantly on 200k+ contexts, but if I reset every ~100k or so it's much more manageable.

pi.dev and ask ai to add features you miss from claude or codex. i configure keyboard shortcuts and swap models easily

Which Opus?

GLM-5.2 is already close to Opus-4.7 level:

https://aibenchy.com/compare/anthropic-claude-opus-4-7-mediu...

Why wait for the next few months? There are plenty of better models that you can run today locally. Qwen3.5-397B beats Qwen3.6-27B. MiniMax2.7 is a longrun horizon monster. (I haven't given 3 much of a try yet). KimiK2.6/2.7, MiMoV2.5/MiMoV2.5-Pro and GLM5.1 will wreck Qwen3.6-27B any day on any task.

DeepSeekv4+ will have image capability, they said so in their paper. GLM whenever they decide to. Both companies have they tech and for whatever reason haven't decide to prioritize it. Both of their OCR are SOTA among all OCR models closed or open. GLM demonstrated they know how to do this, with GLM-4.6V.

Yes, you are right (as far as I'm aware). For things where you need the LLM to look at screenshots, photos or other images you can use Kimi-K2.6/K2.7 - comparable pricing, somewhat comparable performance and quality. You can even probably combine two models (e.g Kimi and GLM) in one agent, using Kimi for multimodal inputs and GLM for everything else, although 1) I'm not sure if this will not cause some kind of context poisoning with low-quality patterns for better performing model (e.g. in some cases Kimi may be worse than GLM, but GLM, when following up, may adopt the same reasoning patterns as Kimi, undermining it's own performance), and 2) I'm not quite sure if it's possible with the tools currently available (I'm not really into agentic or chatbots stuff to be honest).

They do not and it sucks for certain tasks.

It also means that if they actually trained with vision, they'd be on par with Anthropic models as vision seems to improve model performance across the board even for non-vision tasks.

That's right, but there are other recent open weights and relatively big LLMs that are multimodal, e.g. MiniMax-M3.

With open weights LLMs, it is affordable to use many different models, each for whatever it is better.

Moreover, for analyzing "UIs, photos, screenshots, etc." there are small models that can be run locally on smartphones or laptops, e.g. IBM granite-vision-4.1-4B, certain Google Gemma 4 variants and certain Qwen variants, whose output you can use as input for a big LLM, in order to accomplish some more complex task.

Configure a subagent in your coding harness for vision, add a prompt about the vision use, configure a vision model for it, modify your main agent's prompt to use the vision subagent for vision tasks. Now your non-vision model has vision support.

They have a separate VL model but never tried it

Very interested in this! Can you share more about the modelling method (eg, three js?), the task list, and outputs here?

I think there's probably some good juice to squeeze in terms of spacial awareness by doing a benchmark something like

- give 3d modelling task

- render and snapshot from a variety of angles

- feed to third-party vision model for a "what is this" type query

- grade on end-to-end accuracy

Bonus points for asking the vision model something like "how beautiful is this 1-10".

Would you be able to run it against Gemini Flash (not Lite) 3.0, high thinking?

This was a problem with older Qwen/MiMo/Kimi models mostly. GLM has always been on the more robust side, and newer iterations from all those labs have improved as well. The only lab I've seen regressing this way is DeepSeek, 3.2 was fairly robust but 4.0 feels more benchmaxxed.

I have used GLM since version 4.8 I think and do enjoy using them. More then other models like Kimi or Deepseek. Though only tested them on smaller private projects.

I beg to differ. I replaced a $40/mo GitHub Copilot subscription where I used Opus 4.6 and GPT 5.5 with a $10/mo opencode Go plan where I use mostly DeepSeek V4 Flash and testing MiMo 2.5.

I work on mid-sized projects currently (200k to 1kk lines of code).

You are obviously lying because it shows you have no experience with. GLM since 4.5 have been crushing it. all their models since then haven't skipped a beat. 4.5/4.5-air, 4.6, 4.7, 4.8, 5, 5.1. That aside, MiMoV2.5, MiniMax from 2.0, DeepSeek from V3, Kimi since V2, Qwen since 3, Hy3 have all been amazing models. All from China, we need to get over it. China is not losing yet as far as the AI race is concerned.

[flagged]

Your system prompt is showing.

Where can I read more about the coming 3mil GLM 5.2?

Claude Sonnet 4.6 identified itself as DeepSeek repeatedly: https://www.reddit.com/r/DeepSeek/comments/1rd5jw7/claude_so...

I tested this myself a few months ago, and confirmed that it was true.

LLMs don't know who they are unless the system prompt tells them, and as all of them are trained on model responses that exist on the web that end up being scraped, the weights may predict a certain incorrect response. LLMs have no ability to introspect, and do not know anything about themselves, so they will hallucinate in response to that question unless they are carefully trained on that exact, pointless question.

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.

Then why does it score better than any Gemini model?

Recent incident with the Rio 3.5 model clearly shows that many coding models are specifically trained/fine tuned for the benchmarks.

....so use DeepSeek v4 Pro for 95% of your coding tasks, and GLM 5.2 for the other 5%? You don't need to stick to one model.

People always say stuff like this, but it is misleading. The reason it's misleading is because that remaining 5% makes a huge difference, and is where most of the value of using AI agents lies.

I'm not interested in using AI to write code that would have taken me 5-10 minutes to write myself. I use AI to debug complex bugs and develop large features that span multiple domains - stuff that normally takes hours, if not days/weeks. A model that is "enough for 95%" does not cut it for that, because the failures compound during long-horizon tasks and the thing becomes a mess.

https://en.wikipedia.org/wiki/Artificial_scarcity

"open source" means that the code itself (for LLMs - this is training code) is available to the general public. "open weights" means that the weights (trained over time) are available publicly, rather than locked behind a paywalled chat. I do not know of an open source LLM that is not also open weights (unless they never bothered training it). Models like Claude and Gemini are neither open source, nor are they open weights.

they went public a few weeks ago

And apparently, actual support for 1M context window, not just theoretical.

Did you consider their peak hours and model usage multiplier? Read the green box https://docs.z.ai/devpack/overview#usage-instruction

I had the Lite plan, I NEVER maxed out the quota because I considered these things. If I, for example, switched over to GLM-5-Turbo, then I could've easily burned through quota.

How are you using it? I have the lite plan and I've only ever maxed my weekly usage a few hours before reset. I will concede that I'm not a super heavy LLM user but it's been really good for me.

My workflow is usually:

- read file. I want to achieve X, how do? Do not implement anything.

- I would do a, b and c

- sketch a brief implementation of your suggestion

- <code> (not writing files yet)

- instead of your approach x, wouldn't it make sense to instead do z? What would that look like?

- <code>

- nice, implement this

- starts writing files, run tests, etc.

I just used CC with GLM, I was satisfied.

The entire point of this post is that it's open weights, you can run it yourself and don't have to deal with the API issues. You really do have that choice.

That’s what happens when you offer something decent at a fraction of the price of opus - more demand than you can serve

Give it a few days and additional provider will be up and available on OpenRouter. Then the game of figuring out who’s not nuking the weights and neutering the quantization begins.

I indeed got a few timeouts yesterday using the official API, I imagine for the coding plan users it'll be even worse.

A yes, the stealth advertisement post ...