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Object-focused prompts that test whether a generator gets counts, positions, colors, and attributes right.
GenEval is a sharp test of prompt fidelity. Every prompt is engineered to require getting a specific compositional fact right: "two cats next to a red apple", "a blue car to the left of a yellow truck". An object detector verifies whether the image contains the requested objects, in the requested counts, colors, and positions.
For each generated image, an off-the-shelf detector scores whether the objects, counts, positions, colors, and attributes match the prompt. The composite score is the percent of prompts where every criterion is satisfied.
Benchmark data sourced from: Hugging Face, Arena.ai, Artificial Analysis.
| # | Model | Lab | Source | Score |
|---|---|---|---|---|
| 01 | Intern Lumina U2 | Shanghai AI Lab | Open | 89.3 |
| 02 | BAGEL-7B-MoT | Bytedance | Open | 88.0 |
| 03 | Z-Image-Turbo | Alibaba | Open | 83.0 |
| 04 | MF-1 | Horizon Robotics | Open | 82.8 |
| 05 | Qwen-Image | Alibaba | Open | 81.4 |
| 06 | GenFirst | ByteDance Seed | Open | 0.9 |
No models in this category.
Not enough scored models yet.
Most current text-to-image models score between 40% and 75%. Top diffusion and autoregressive models in 2026 push above 80% on the overall split. Anything under 35% is unlikely to handle multi-object compositional prompts reliably.
No. It only asks whether the requested objects are present with the correct properties. A cartoonish image that nails the composition can score higher than a photorealistic one that flubs an attribute.
Based on score correlations across our database.
