MultiBind - Face Binding: leaderboard
Metric: Subject-level success rate (%) in the face identity dimension on MultiBind (multi-subject image generation from per-subject reference images, a background reference and a long entity-indexed prompt, reconstructing a real target photo): share of generated subjects that stay consistent with their own subject in the target photo (InsightFace embeddings, thresholds calibrated to human labels) without being confused with another subject, over the subject slots matched in every model's output; higher is better. Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 6 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Nano Banana Pro | 84 | |
| 2 | GPT-Image-1.5 | 82.3 | |
| 3 | Seedream 4.5 | 58.6 | |
| 4 | Qwen-Image-Edit-2511 | 43.7 | |
| 5 | OmniGen2 | 41.9 | |
| 6 | HunyuanImage-3.0-Instruct | 38.7 |
Interactive version: theaggregate.ai/benchmark?slug=multibind-face-binding · How It Works · Data refreshed daily, snapshot 2026-10-11.