VMetaphor-Bench - Element Mapping: leaderboard
Element mapping in VMetaphor-Bench (Chen et al., 2026): multiple-choice questions on whether each source element in the generated image corresponds to its intended target element, answered by a Qwen3.5-27B judge. Accuracy in percent; higher is better. Conceptual prompts, which state only the metaphorical idea.
Metric: MCQ accuracy (%; the element-level mapping questions between the source and target domains; the generated image is judged by Qwen3.5-27B on multiple-choice questions (four options including cannot be determined) built from each metaphor's structured annotation; 1,500 visual metaphors curated from real creative imagery; conceptual prompts that state only the metaphorical idea). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 11 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT Image 1.5 | 76.5 | |
| 2 | Nano Banana 2 (Gemini 3.1 Flash Image Preview) | 75.9 | |
| 3 | Seedream 5.0 Lite | 66.5 | |
| 4 | FLUX.2-dev | 63.9 | |
| 5 | Janus-Pro-7B | 63.2 | #7341 |
| 6 | Qwen-Image-2512 | 61.7 | |
| 7 | Z-Image-Turbo | 60.8 | |
| 8 | Stable Diffusion 3.5 Large | 58.9 | |
| 9 | FLUX.1-dev | 55.2 | |
| 10 | BAGEL-7B-MoT | 54.2 | #5515 |
Interactive version: theaggregate.ai/benchmark?slug=vmetaphor-bench-element-mapping · How It Works · Data refreshed daily, snapshot 2026-11-12.