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

  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)

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

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

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.

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

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.

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

https://deepswe.datacurve.ai/

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

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.

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

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'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.

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

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

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

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

Would need to be a pretty determined medium biz

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...

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.

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.

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.

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 :)

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

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

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

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.