Portraits · Text-to-Image
Elo rankings from blind votes across 1 challenge in this category.
Highlights
5 imagesBest models, by
Best Text-to-Image Models by Price
Best Text-to-Image Models by Speed
16 models waiting for enough speed data.
Best AI Models for Portraits
| # | Model | Elo |
|---|---|---|
| 1 | 1279 | |
| 2 | 1267 | |
| 3 | 1252 | |
| 4 | 1233 | |
| 5 | 1232 | |
| 6 | 1232 | |
| 7 | 1232 | |
| 8 | 1230 | |
| 9 | 1229 | |
| 10 | 1223 |
| 11 | 1218 | |
| 12 | 1216 | |
| 13 | 1206 | |
| 14 | 1198 | |
| 15 | 1198 | |
| 16 | 1176 | |
| 17 | 1173 | |
| 18 | 1171 | |
| 19 | 1169 | |
| 20 | 1153 | |
| 21 | 1148 | |
| 22 | 1138 | |
| 23 | 1134 | |
| 24 | 1120 | |
| 25 | 1108 | |
| 26 | 1102 | |
| 27 | 1044 | |
| 28 | 1042 | |
| 29 | 1034 |
FLUX.2 [dev] Turbo leads the portrait category with a 1279 Elo and 78.9% win rate, narrowly outpacing Google’s Nano Banana 2 (1267 Elo) despite the latter's higher 81.8% individual win rate. This specialized ranking favors efficiency, as top-tier portrait performance is dominated by "Fast" and "Medium" speed models costing $0.030/img or less.
Rivalries
Aggregate head-to-head across the arena
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.
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