KVBench (Brief Caption) - Physics (English): leaderboard
Metric: Checklist score (%) on the physics prompts in English, each given as a brief caption, so the model must supply the textbook knowledge itself: KVBench knowledge-intensive text-to-image prompts from over 30 senior high-school textbooks (150 per subject and language, each with a textbook reference image); Qwen2.5-VL-32B-Instruct answers a checklist of four to six binary questions (key objects, attributes, spatial relations, reasoning outcomes) about each generated image, and the score is the mean share of satisfied items, in percent; higher is better. Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 14 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | FLUX.1-dev | 50.4 |
| 2 | GPT-Image (KVBench checkpoint unspecified) | 42.2 |
| 3 | Seedream-4.0 | 39.53 |
| 4 | FLUX.2-dev | 39.13 |
| 5 | Nano Banana Pro (Gemini 3 Pro Image) | 37.2 |
| 6 | FLUX.2-max | 35.07 |
| 7 | BAGEL-7B-MoT | 29.67 |
| 8 | SD3.5-Large (KVBench GGUF build, quantization unspecified) | 28.73 |
| 9 | Qwen-Image | 28 |
| 10 | Janus-Pro | 26.33 |
Interactive version: theaggregate.ai/benchmark?slug=kvbench-brief-caption-physics-english · How It Works · Data refreshed daily, snapshot 2026-10-07.