AeroGround - Multi-Target Matching: leaderboard
AeroGround (2026) tests vision-language models on paired UAV and ground-level views of simulated open environments. Exact-match accuracy in percent on closed-form questions; higher is better. The model must give the complete one-to-one correspondence of several targets across the two views.
Metric: Accuracy (%; Object Alignment: give the full one-to-one correspondence of several targets across views; aerial-ground collaborative questions from paired simulated UAV and ground views, exact-match scoring of the parsed option or mapping, invalid output scored wrong; one deterministic run). Source: arxiv.org. Saturation forecast: Around October 2027. 16 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Seed 2.0 Pro | 30.6 | #209 |
| 2 | Gemini 3.1 Pro (Preview) | 25.9 | #91 |
| 3 | Kimi K2.5 | 17.6 | #252 |
| 4 | GPT-5.4 | 14.7 | #121 |
| 5 | Qwen 3 VL 32B (Thinking) | 14.1 | #557 |
| 6 | Qwen 3 VL 8B Instruct | 10 | #982 |
| 7 | Qwen 3 VL 8B (Thinking) | 10 | #887 |
| 8 | Qwen 3 VL 32B Instruct | 8.8 | #582 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=aeroground-multi-target-matching · How It Works · Data refreshed daily, snapshot 2026-11-12.