UXBench: leaderboard
Large language models (LLMs) are increasingly deployed as UX judges that inspect interfaces, diagnose usability problems, and propose repairs. Yet no controlled benchmark measures whether the resulting critiques are reliable and actionable across heterogeneous product surfaces. We introduce UXBench, a benchmark for evaluating LLMs as interaction-grounded UX judges. UXBench comprises local-first ru
Metric: Automated Lift (self-reported). Source: benchmarklist.com. Saturation forecast: Not forecast. 8 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.4 | 21.6 | #79 |
| 2 | Kimi K2.5 | 20.5 | #144 |
| 3 | Claude Sonnet 4.6 | 17.4 | #90 |
| 4 | GPT-5.4 Mini | 17.2 | #212 |
| 5 | GLM-5.1 | 17.1 | #97 |
| 6 | GPT-5.4 Nano | 16.2 | #328 |
| 7 | Qwen 3.6 Plus | 14.8 | #120 |
| 8 | Gemini 3.1 Pro (Preview) | 13.6 | #58 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=uxbench · How It Works · Data refreshed daily, snapshot 2026-10-20.