TravelEval - Daily Time Utilization: leaderboard
TravelEval (KDD 2026) asks an LLM for a complete multi-day travel plan and simulates it in a sandbox of 10 Chinese cities with real transport, hotel and attraction data. Scores are from Direct prompting over 1,150 queries. The board is the percentage of each day, from leaving the hotel to returning, spent actually visiting attractions; higher is better.
Metric: Daily Time Utilization (DTU): effective attraction time over the whole day from departure to return (%); Direct prompting (one pass, no tools), plans simulated in the TravelEval sandbox of 10 Chinese cities (real rail, flight, hotel and attraction data, queuing-time model, road-network distances) over 1,150 queries; higher is better. Source: arxiv.org. Saturation forecast: Around 2031. 7 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 2.0 Flash | 27.15 | #704 |
| 2 | Claude Sonnet 4.5 | 25.97 | #250 |
| 3 | DeepSeek V3.1 (Non-reasoning) | 25.51 | #497 (DeepSeek V3.1) |
| 4 | GPT-4o Mini (2024-07-18) | 23.63 | #1510 |
| 5 | Qwen 3 8B | 23.46 | #1830 |
| 6 | GPT-5 Chat | 22.17 | #444 |
| 7 | GPT-4o (2024-11-20) | 21.76 | #866 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=traveleval-daily-time-utilization · How It Works · Data refreshed daily, snapshot 2026-11-13.