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
It produced worse UI mockups than GPT and GPT models are already the bottom of the barrel here. The only model that performed well was Kimi K3 - insanely good, but expensive.
It's hard to trust benchmarks these days.
If you want to generate a UI based on specific user input of some kind, then they are.
I'd suggest using one model for UI and another model for tacking onto that UI. LLMs are great at pattern matching, and benchmarks don't really capture one-shotting desirable UI.
That said, benchmaxxing is a thing and your experience with models is a thing. Benchmarks are fuzzy and should be taken with a grain of salt.
(Edit: TLDR; It gets on with it, makes the same mistakes you would, without overthinking and overengineering, most of the time)
Model Score Cost / Task Output Tokens / Task
-------------------------------------------------------------------------
Muse Spark 1.2 (xhigh) 56.8 $0.40 30,430
Gemini 3.7 Flash (high) 56.0 $0.40 36,847
GPT-5.6 Terra (max) 56.6 $0.51 20,838
GPT-5.6 Sol (high) 57.3 $0.52 7,545
GLM-5.2 (max) 53.0 $0.56 32,200
GLM-5.3 (max) 59.5 $0.68 41,107
GPT-5.5 (xhigh) 56.3 $0.69 16,893
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
Qwen3.8 2.4T A95B 57.7 $0.95 32,472
Claude Opus 5 (medium) 58.6 $0.98 12,459
Qwen3.8 Max 58.1 $1.13 38,287
GPT-5.6 Sol (max) 60.9 $1.23 16,879
Claude Opus 5 (high) 61.5 $1.52 21,353
Claude Opus 4.8 (max) 57.3 $1.65 33,557
Model Score Cost / Task Output Tokens / Task
-------------------------------------------------------------------------
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
GLM-5.3 (max) 59.5 $0.68 41,107
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
GLM-5.2 (max) 53.0 $0.56 32,200for over 1 billion real world users living in China, they don't have the option of paying $1.52 per task to use Opus 5, they are banned doing that due to US politics.
generally I choose models by their intelligence and then personal preference from direct experience.
> writing scripts
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.
The only thing that's keeping me is the value $200 subscription provides. If that value disappears, I see no reason why not to switch to something that isn't a black box.
[0] https://github.com/rynfar/meridian
[1] https://github.com/router-for-me/CLIProxyAPI
edit: reworded for clarity
At some point I will switch, $200 buys a lot of tokens on OpenRouter.
But regardless, you definitely should use a harness where switching models on the fly is easy. There's a reason why Anthropic uses their own proprietary formats/conventions anywhere they can - to lock you in when inference eventually commoditizes.
They're only a better value if you're paying API rates
GLM sets effort to max by default historically.
For web dev is just a must to have, and offloading that part to a secondary model doesn't work really well in my experience.