DefectBench (Building Facades): leaderboard

Metric: Defect identification F1 (0-1, times 100) of the primary defect types (crack, material loss, stain, external fixings) the model names per image, on DefectBench's 487 building-facade evaluation images (crack 117, material loss 102, surface stain 134, external fixings 122), zero-shot, JSON answers in one multi-turn dialogue per image; higher is better. Source: arxiv.org. Saturation forecast: Around January 2027. 18 models tracked.

Top models

#ModelScoreOverall rank
1Qwen 3 VL 32B (Thinking)84.42#287 (Qwen 3 VL 32B)
2Claude Opus 4.176.94#132
3GPT-4o75.65#333
4GLM-4.6V75.04#309
5GPT-5.2 Pro74.79#57
6Gemini 3 Pro (Preview)74.08#64
7GLM-4.5V73.99#339
8Seed 1.873.73#136
9Qwen 2.5 VL 72B Instruct72.94#364
10Qwen 3 VL 8B Instruct72.62#401
11Qwen 3 VL 32B Instruct71.66#276
12Gemini 3 Flash (Preview)71.56#78
13Qwen 2.5 VL 32B Instruct66.84#443
14Qwen 2.5 VL 7B Instruct47.96#643

No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.

Interactive version: theaggregate.ai/benchmark?slug=defectbench-building-facades · How It Works · Data refreshed daily, snapshot 2026-10-11.