I’ve been using AI to generate some repetitive frontend (guilty), and while most outputs felt vibe-coded, some results were surprisingly good. So I cleaned it up and made a ranking game out of it with friends, and you can check it out here: https://www.designarena.ai/vote

/vote: Your prompt will be answered by four random, anonymous models. You pick the one you prefer and crown the winner, tournament-style.

/leaderboard: See the current winning models, as dictated by voter preferences.

/play: Iterate quickly by seeing four models respond to the same input and pressing space to regenerate the results you don’t lock-in.

We were especially impressed with the quality of DeepSeek and Grok, and variance between categories (To judge by the results so far, OpenAI is very good for game dev, but seems to suck everywhere else).

We’ve learned a lot, and are curious to hear your comments and questions. Excited to make this better!

This is really good! It would be really cool to somehow get human designs in the mix to see how the models compare. I bet there are curated design datasets with descriptions that you could pass to each of the models and then run voting as a "bonus" question (comparing the human and AI generated versions) after the normal genAI voting round.

This is a surprisingly good idea. The model vs model is fun, but not really that useful.

But this could be a legitimate way to design apps in general if you could tell the models what you liked and didn't like.

I tried the vote and both results always suck, there's no option to say neither are winners. Also it seems from the network tab you're sending 4 (or 5?) requests but only displaying the first two that respond, which biases it to the small models that respond more quickly which usually results in showing two bad results

How about adding "mobile"? A lot of the time models tend to default to designs that don't make sense on mobile, even when instructed to design it as such.

As a UX/UI designer in Korea, I love seeing related products being released. I hope they become even more advanced in the future.

nice! Training models using reward signals for code correctness is obviously very common; I'm very curious to see how good things can get using a reward signal obtained from visual feedback

interesting idea, this benchmark maps fairly closely to the types of output I typically ask LLMs to generate for me day-to-day

It would lend credibility to publish your system prompt.

Very cool! Can the code and design that is generated be used?

The problem is, what is being taught as UI-UX is 90% hogwash, balooney, pure bullshit. And these results reflect that.

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