# Best AI for UI design evidence methodology

Retrieved: 2026-09-05T06:49:43.701Z

License: CC BY 4.0

Canonical analysis: https://llm-stats.com/best-ai-for-ui-design

## Scope

This dataset ranks underlying AI models for generating coded user interfaces. It does not rank design canvases, image generators, IDE extensions or autonomous product-building services.

## Ranking procedure

The primary signal is the text-to-website arena. Multiple models receive the same interface prompt and produce working React outputs. Voters compare results without seeing model identities. Models are ordered by conservative TrueSkill (mu minus three sigma), limiting the advantage of sparse evidence. The broader LLM Stats coding index is included as a separate implementation-quality cross-check and never silently substituted for the UI score.

The uncertainty-aware ranking is based on the academic method described by Herbrich, Minka and Graepel in [TrueSkill: A Bayesian Skill Rating System](https://www.microsoft.com/en-us/research/publication/trueskilltm-a-bayesian-skill-rating-system/). Ranking evidence refreshes hourly; editorial conclusions are updated only when the evidence materially changes.

At retrieval, the export contained 10 ranked models. No payment can change placement.

## Reproduction procedure

1. Open https://llm-stats.com/best-ai-for-ui-design and record the retrieval time.
2. Download https://llm-stats.com/research/best-ai-for-ui-design/evidence.csv or https://llm-stats.com/research/best-ai-for-ui-design/evidence.json during the same refresh window.
3. Confirm that the visible order matches the website_arena_score order in the export.
4. Open https://llm-stats.com/arenas/coding-arena/text-to-website and run the same brief across available models with identities hidden.
5. Preserve the prompt, model versions, tools, token limit, output code, rendered captures, votes, ties and failures.

## Recommended UI field test

Run five fixed briefs per finalist: a marketing page, a dense dashboard, a mobile product flow, a design-system extension and an accessibility repair. Score requirement coverage, visual hierarchy, responsive behavior, interaction states, accessibility, code quality, latency and total cost. Test generated code in an isolated environment and require human design and engineering review before production use.

## Limitations

- Human preference does not directly prove accessibility, browser compatibility, security or maintainability.
- A rendered first screen may conceal missing interaction, empty, loading and error states.
- The coding index covers broader software tasks and is only a cross-check.
- Prices can omit subscriptions, caching, tool calls and reviewer correction time.