GLM-5.3 Artificial Analysis Benchmarks

I like to compare models with a similar score on cost per task and output tokens per task since those measure two things I'm interested in: cost efficiency and token efficiency. Here's how GLM-5.3 compares to other models in a similar score and against GLM-5.2 to save a few clicks for others who care about these metrics:

    Model                        Score    Cost / Task    Output Tokens / Task
    -------------------------------------------------------------------------
    GLM-5.3 (max)                 59.5          $0.68                  41,107
    GLM-5.2 (max)                 53.0          $0.56                  32,200
    Claude Opus 5 (high)          61.5          $1.52                  21,353
    GPT-5.6 Sol (max)             60.9          $1.23                  16,879
    Grok 4.6 (high)               60.9          $0.84                  21,735
    Kimi K3 (max)                 59.7          $0.84                  25,474
    GPT-5.6 Sol (xhigh)           59.0          $0.87                  11,098
    Claude Opus 5 (medium)        58.6          $0.98                  12,459
    Qwen3.8 Max                   58.1          $1.13                  38,287
    Qwen3.8 2.4T A95B             57.7          $0.95                  32,472
    Claude Opus 4.8 (max)         57.3          $1.65                  33,557
    GPT-5.6 Sol (high)            57.3          $0.52                   7,545
    Muse Spark 1.2 (xhigh)        56.8          $0.40                  30,430
    GPT-5.6 Terra (max)           56.6          $0.51                  20,838
    GPT-5.5 (xhigh)               56.3          $0.69                  16,893
    Gemini 3.7 Flash (high)       56.0          $0.40                  36,847

I've tested GLM 5.3 on the release day and Artificial Analysis is spot on. It's a really good model.

But my main takeaway was something else. I've used closed weight models for long enough that I've forgotten how good it feels to see reasoning tokens.

With GPT/Claude, you kind of hope that intent was captured well, that agent had all the information, all the tools it needed, because you won't see "hmmm it seems like nix flake isn't available here and I shouldn't install something globally" until it slopped out millions of tokens and wasted hundreds of dollars for 8 hours. With GLM and the likes, you just stop the disease right where it begins.

I understand that running these benchmarks can get expensive, but it would be really nice to see AA include more benchmarks of models at reasoning settings other than the maximum, at least for the biggest releases. They have that nice graph of cost vs. composite benchmark score with the Pareto frontier line, but who knows if those are actually the optimal choices? There are already a few non-max-reasoning models on the Pareto line, among the few that were tested.

Beware of the benchmarks listed. SciCode and EnterpriseOps for instance: https://shukla.io/blog/2026-08/gym.html

Sol is an underappreciated model. Dropped Claude today and went to codex. None of that god awful prose Claude used for me any longer.

Does Artificial Analysis use OpenRouter for model access to do their benchmarks?

Very impressive score for the size, though token use is higher than k3 and far higher than proprietary models, and its price to performance isn't all that far ahead of k3 as a result

Is it worth using these models if I have a claude code subscription already? The appeal of lower cost is nice but I haven't gotten over the switching cost yet.

Tied for #1 by agentic index (with Opus 5).

And reminder: it's less than a quarter the size of Kimi K3!

...do I take out a double mortgage to buy a 4 Spark cluster?

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