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README.md

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 # ImageBench — 50 Text-to-Image Models Judged by VLMs on 192 Prompts
 
-Reproducibility dataset for [imagebench.ai](https://imagebench.ai): the 192-prompt V1.1 benchmark, per-(model, prompt) VLM verdicts, and per-model aggregate scores for 50 text-to-image models.
+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>
@@ -36,7 +36,7 @@ Reproducibility dataset for [imagebench.ai](https://imagebench.ai): the 192-prom
 
 | File | Rows | Description |
 |---|---|---|
-| `prompts.csv` | 64 tests × 3 variants | The V1.1 benchmark: prompt variants, category/subcategory/difficulty, VLM judge questions, evaluation criteria, VLM-routing assignment. |
+| `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. |