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Muse Spark has a nice balance. not to mentions the Contribs version is old deepseek flash prices.

Tested muse spark 1.2 because it was rated so high on design arena, and I've missed a model that can do nice UI in the hands of an operator with no UI skills.

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 just want it to generate UI out of nothing, the benchmarks aren't really for that.

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.

I found the sweetspot here: GPT-5.6 Sol (high) 57.3 $0.52 7,545

(Edit: TLDR; It gets on with it, makes the same mistakes you would, without overthinking and overengineering, most of the time)

It would make reading and comparing a bit easier if the data was sorted by a dimension.

Cost per task:

  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

Benchmark score:

  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                  32,200

This matches my experience with Sol. Read and thought for a while, and edited files, tested, edited again, then ran out of budget in a relatively short time. But its solution was very good and was done quickly, so all things equal I prefer that over something much more verbose like Deepseek.

this is not very useful.

for 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.

What’s the reseller situation?

these $/task figures aren't very useful in my experience. it doesn't tell you how well it did the task.

generally I choose models by their intelligence and then personal preference from direct experience.

they make a ton of sense for tasks that are achievable with no prob by all models - e.g. writing scripts that do a specific thing etc.

    > writing scripts

you can use a Gemini model completely for free doing that. no agent. aistudio.google.com