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

In this paper they nerf an LLMs ability to emit waffling thinking tokens like "wait", "but", "alternatively", and the models (they're old, small models in the paper) terminate reasoning faster and perform better. I bet Anthropic is tuning this on their backend.

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.

Another thing I tell Claude to do is to not guess, but look at documentation, it messes up a lot less, might use some tokens reading docs, but at least it has a higher success rate code wise.

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.

Are the scores here normalized such that each point difference is equidistant?

  rank  score  age  size   name
  1     62.0   8    -      Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback)
  2     59.1   55   -      GPT-5.5 (xhigh)
  3     58.5   55   -      GPT-5.5 (high)
  4     57.2   104  -      GPT-5.4 (xhigh)
  5     56.7   20   -      Claude Opus 4.8 (Adaptive Reasoning, Max Effort)
  6     55.5   118  -      Gemini 3.1 Pro Preview
  7     53.1   62   -      Claude Opus 4.7 (Non-reasoning, High Effort)
  8     53.1   132  -      GPT-5.3 Codex (xhigh)
  9     52.5   62   -      Claude Opus 4.7 (Adaptive Reasoning, Max Effort)
  10    51.5   92   -      GPT-5.4 mini (xhigh)
  11    50.9   120  -      Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
  12    50.7   1    large  GLM-5.2 (max)
  13    50.1   29   -      Qwen3.7 Max
  14    48.7   188  -      GPT-5.2 (xhigh)
  15    48.1   132  -      Claude Opus 4.6 (Adaptive Reasoning, Max Effort)
  16    47.8   205  -      Claude Opus 4.5 (Reasoning)
  17    47.6   132  -      Claude Opus 4.6 (Non-reasoning, High Effort)
  18    47.5   70   -      Muse Spark
  19    47.5   54   large  DeepSeek V4 Pro (Reasoning, Max Effort)
  20    47.1   58   large  Kimi K2.6
  21    47.1   29   -      Gemini 3.5 Flash (minimal)
  22    46.7   449  -      Gemini 2.5 Pro Preview (Mar' 25)
  23    46.5   211  -      Gemini 3 Pro Preview (high)
  24    46.5   16   -      Qwen3.7 Plus
  25    46.4   120  -      Claude Sonnet 4.6 (Non-reasoning, High Effort)
  26    45.6   5    large  Kimi K2.7 Code
  27    45.6   104  -      GPT-5.4 (low)
  28    45.5   56   large  MiMo-V2.5-Pro
  29    45.1   43   -      GPT-5.5 Instant (May 2026)
  30    45.0   29   -      Gemini 3.5 Flash (high)
  31    44.9   58   -      Qwen3.6 Max Preview
  32    44.7   216  -      GPT-5.1 (high)
  33    44.2   188  -      GPT-5.2 (medium)
  34    44.2   126  large  GLM-5 (Reasoning)
  35    43.9   92   -      GPT-5.4 nano (xhigh)
  36    43.4   71   large  GLM-5.1 (Reasoning)
  37    43.4   16   large  MiniMax-M3
  38    43.2   54   large  DeepSeek V4 Pro (Reasoning, High Effort)
  39    43.0   188  -      GPT-5.2 Codex (xhigh)
  40    42.9   76   -      Qwen3.6 Plus
  41    42.9   205  -      Claude Opus 4.5 (Non-reasoning)
  42    42.6   182  -      Gemini 3 Flash Preview (Reasoning)
  43    42.2   99   -      Grok 4.20 0309 (Reasoning)
  44    42.1   56   large  MiMo-V2.5
  45    41.9   91   large  MiniMax-M2.7
  46    41.4   91   -      MiMo-V2-Pro
  47    41.3   121  large  Qwen3.5 397B A17B (Reasoning)
  48    41.0   48   -      Grok 4.3 (high)
  49    40.5   71   -      Grok 4.20 0309 v2 (Reasoning)
  50    40.5   342  -      Grok 4
  51    39.8   54   large  DeepSeek V4 Flash (Reasoning, High Effort)

- GPT 5.5 consistently the best, an opinion who gets me constant downvotes here by the Anthropic Marketeer strike force...

- China is going to eat the US lunch on AI

- What have European universities and companies been doing? Its like if, on a parallel past/future, Nikola Tesla and Edison would have created flying Cyberpunk machines, while Europeans researchers, would be getting together to request EU funds, for investigation on how to breed faster horses.

- If Zuckerberg could be fired, after spending a total of $235 billion on AI and having NOTHING to show for...should he be fired?

But if that's your thing, here you go: https://github.com/day50-dev/aa-eval-email/commit/1853be6461...

add an argument (any argument) and it will be sorted as your specified. It just works as a toggle flipping the order ... so literally any string will do.

The original link has been updated accordingly with the new code.

Setup a fresh new large monitor. Open CLI. Run command. Watch output at the bottom of your screen. Keep watching the bottom of your screen for the rest of the day.

Sure you can tile windows and it helps but come on. Just have the command/input section in the bottom and the “output” on top. Keep the command bit on the bottom.

Unless you're running it locally, aren't you just trusting some other entity?

With a good harness, that's my favorite model for any personal project. I use Opus 4.8 at work because i don't have to pay for it and of course I love it, but DeepSeek is like 80% there for one tenth of the price.

GPT can find fault in everything and anything including its own work.

I do not have GLM 5.2 numbers because the whole default max setting is overkill. But GLM 5.1 numbers had it at 12x cheaper then API rates. And about 2.5x more tokens vs zai their own subscription service.

Yes, its FP8 but lets be honest, do we know for sure that even zai runs at FP16? I learned a long time ago with Claude and Codex how much cheating happens on model levels, even from the big boys.

(there's a table which shows comparison between vendors)

Also, it seems there's a general one as well (for all kimi models?): https://github.com/MoonshotAI/Kimi-Vendor-Verifier

https://docs.z.ai/devpack/tool/claude

Here's my setup. I add this to my .bashrc

export ZAI_API_KEY="your_key_here"

alias claudez='ANTHROPIC_AUTH_TOKEN="$ZAI_API_KEY" ANTHROPIC_BASE_URL="https://api.z.ai/api/anthropic" ANTHROPIC_DEFAULT_OPUS_MODEL="glm-5.2[1m]" ANTHROPIC_DEFAULT_SONNET_MODEL="glm-4.7" ANTHROPIC_DEFAULT_HAIKU_MODEL="glm-4.7" claude'

Then I just run claudez

pro tip the same thing works with deepseek https://api-docs.deepseek.com/guides/anthropic_api

Even more pro tip: Claude Code can set this up for you haha

There's ZCode (https://zcode.z.ai). Which is like the Codex App.

That's as "easy" as it is for non-devs that you're complaining about.

I'd pay for an out of the box solution. i.e. an Installer with updates

https://github.com/QuantiusBenignus/Zshelf/discussions/2

Not accounting for hardware, of course :)

Even the local models I run on my Mac are getting surprisingly good at that now.

There they can deploy these models while using the existing legal frameworks.

I was benchmarking using a soon to be released new version of my AI CAD modeling software[0]. It's basically an agent that has access to tools that can execute build123d scripts, get sculpted models, blender to combine sculpts + parametric models, tools to inspect the model (visually and with code), search datasheets, ...

I tried what you recommend a while ago (asking an AI to evaluate using different angles) and the AI evaluations were extremely bad - barely any correlation to what I scored. Things have gotten better, but I don't trust it enough yet.

Here is how I score adherence (and how AI did as well, but I tried methods where it would just give back a boolean "pass" or not):

    <0.2 → Poor – Misses core intent; largely irrelevant or incorrect.
    <0.4 → Weak – Partially relevant; significant omissions or errors.
    <0.6 → Fair – Covers main points but lacks completeness or precision.
    <0.8 → Good – Mostly accurate; minor gaps or deviations.
    <=1.0 → Excellent – Fully aligned; precise, comprehensive, and faithful to intent.

    dragon-bottle-stopper
    editing-param-mid-conv
    editing-parametric-enclosure
    editing-swap-material-param
    editing-text-edit-cube
    multi-turn-bird-house
    multi-turn-dice-tower
    multi-turn-modular-planter
    multi-turn-phone-stand
    multi-turn-shelf
    one-shot-bookend
    one-shot-cable-clip
    one-shot-chess-queen
    one-shot-coaster
    one-shot-coffee-cup
    one-shot-dog-tag
    one-shot-dragon-figurine
    one-shot-hex-bracket
    one-shot-keychain-fob
    one-shot-low-poly-tree
    one-shot-pegboard-hook
    one-shot-pi4-case
    one-shot-threaded-jar

Edit: Surprisingly very good results with 3.0 flash with high thinking.

Cost: $0.06

Duration: 3.22 min

Code Errors: 1.3 per attempts (meaning on average it had to retry 1.3 times)

Adherence was on par with 3.5 flash Low thinking

Isn't that a million?

You probably refer to GLM-4.7

You'll see that it quickly gives up. Thing is, they seem to count cached hits as if they were the non-cached tokens.

I wont be subscribing again thats for sure. I am not paying iPhone money for a Xiaomi.