Art · Text-to-Image
Elo rankings from blind votes across 2 challenges in this category.
Highlights
10 imagesBest models, by
Best Text-to-Image Models by Price
1 model without pricing omitted.
Best Text-to-Image Models by Speed
37 models waiting for enough speed data.
Best AI Models for Art
| # | Model | Elo |
|---|---|---|
| 1 | 1301 | |
| 2 | 1284 | |
| 3 | 1279 | |
| 4 | 1243 | |
| 5 | 1229 | |
| 6 | 1227 | |
| 7 | 1226 | |
| 8 | 1223 | |
| 9 | 1221 | |
| 10 | 1220 |
| 11 | 1213 | |
| 12 | 1208 | |
| 13 | 1208 | |
| 14 | 1206 | |
| 15 | 1206 | |
| 16 | 1205 | |
| 17 | 1202 | |
| 18 | 1201 | |
| 19 | 1195 | |
| 20 | 1195 | |
| 21 | 1193 | |
| 22 | 1191 | |
| 23 | 1190 | |
| 24 | 1189 | |
| 25 | 1179 | |
| 26 | 1178 | |
| 27 | 1177 | |
| 28 | 1175 | |
| 29 | 1175 | |
| 30 | 1173 |
GPT Image 2 leads the Art category with a dominant 91.2% win rate and 1301 Elo, maintaining its position as the overall leaderboard champion. Qwen Image 2512 follows in second place at 1284 Elo, offering a 48% lower price point and significantly faster generation speeds than the top-ranked OpenAI model.
Highlighted challenges
The Reversed Rodeo
This competition tests how well AI image models truly understand language versus how much they rely on visual habits from their training data. The prompt is deliberately simple on the surface but devilishly hard in practice. Most models default to the familiar trope of an astronaut riding a horse. By forcing the reversal, we measure three critical capabilities that separate good models from great ones: Strict instruction following (including negations) Accurate subject-object relationships and spatial hierarchy Resistance to strong dataset biases
Geometric Composition
A spatial-reasoning test. Each object has a precise relationship (inside, on top, behind, seen through the glass), so it measures whether a model follows explicit placement instructions and handles transparency and refraction rather than just approximating the scene.
Keep the arena honest
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