README.md

---
license: cc-by-4.0
task_categories:
  - text-to-image
language:
  - en
tags:
  - benchmark
  - evaluation
  - text-to-image
  - vlm-eval
  - image-generation
  - diffusion-models
  - leaderboard
pretty_name: ImageBench
size_categories:
  - 1K<n<10K
configs:
  - config_name: verdicts
    data_files: verdicts.csv
  - config_name: scores
    data_files: scores.csv
  - config_name: prompts
    data_files: prompts.csv
---

# ImageBench — 50 Text-to-Image Models Judged by VLMs on 192 Prompts

Reproducibility dataset for [imagebench.ai](https://imagebench.ai): the 192-prompt V1.2 benchmark, per-(model, prompt) VLM verdicts, and per-model aggregate scores for 50 text-to-image models.

**Live leaderboard + every generated image**: <https://imagebench.ai>
**Methodology**: <https://imagebench.ai/methodology-v1>
**Reproducibility repo**: <https://github.com/dh7/image-bench-ai>

## What's in this dataset

| File | Rows | Description |
|---|---|---|
| `prompts.csv` | 64 tests × 3 variants | The V1.2 benchmark: prompt variants, category/subcategory/difficulty, VLM judge questions, evaluation criteria, VLM-routing assignment. |
| `verdicts.csv` | **9,600** (50 models × 192 prompts) | Per-image pass/fail verdict, VLM judge reasoning, HPS aesthetic score, VLM model used. |
| `scores.csv` | 50 | Per-model aggregate: Overall (0-100), pass rate, aesthetic Estimated Preference Score, per-category pass rates. |

## Column reference

### `verdicts.csv`
- `model_slug` — canonical model identifier (matches URLs on imagebench.ai)
- `model_name` — human-readable name
- `category` / `subcategory` / `difficulty` — benchmark taxonomy
- `prompt_id` — matches image filename on imagebench.ai/gallery
- `prompt` — the actual prompt sent to the model
- `verdict` — `PASS` or `FAIL` from the VLM judge
- `vlm` — which VLM produced the verdict (Qwen 3.5 122B / Gemini 3.1 Pro / etc.)
- `judge_response` — the VLM's textual reasoning for the verdict
- `hps_mu` — HPSv3 aesthetic score (higher = judged more aesthetically pleasing by human-preference model)
- `hps_pref` — pairwise HPS preference metadata

### `scores.csv`
- `model_slug`, `model_name`
- `overall` — 0-100 blended score (capability + aesthetic preference)
- `pass_rate` — % of prompts passing the VLM verdict
- `eps` — Estimated Preference Score (aesthetic, based on HPSv3)
- `pass_<category>` — pass rate per category (text rendering, spatial reasoning, human realism, truthfulness, studio quality, graphical design)

## How this was produced

1. **Prompts**: 64 tests × 3 variants, hand-curated across 6 capability categories with binary judge questions per test.
2. **Generation**: each model produces 192 images (one per prompt) via its native API — no cherry-picking, no re-runs.
3. **Judging**: VLM-routed — Qwen 3.5 122B handles most categories, with per-category routing to specialists (Gemini 3.1 Pro for hands, etc.) based on calibration.
4. **Aesthetic score**: HPSv3 (Human Preference Score v3) applied to every generated image.
5. **Overall**: 0.5 × normalized pass rate + 0.5 × normalized EPS.

Full methodology + calibration study: <https://imagebench.ai/methodology-v1>

## Known limitations

- **Text-to-image only** — no editing, inpainting, video, long-form coherence.
- **Prompt bias** — the 192-prompt set is weighted toward tests that *separate* models. "Every model passes" mundane prompts are under-represented, so pass rates are lower than typical real-world use.
- **VLM judge bias** — VLMs have their own systematic errors. We validate on a hands-specific calibration blog: <https://imagebench.ai/blog/hands-benchmark-qwen35-122b>
- **Category weighting** — Overall gives each category equal weight, but "text rendering" is inherently harder to pass than "professional studio quality". Interpret cross-category comparisons carefully.

## Citation

If you use this dataset in research or writing, please cite:

```
ImageBench: 50 Text-to-Image Models Judged by VLMs on 192 Prompts
https://imagebench.ai
```

## License

**CC-BY 4.0** — free to use with attribution.

## Feedback

Issues, corrections, or methodology critique: open an issue at <https://github.com/dh7/image-bench-ai> or leave a comment on this dataset.