---
dataset_info:
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  - name: id
    dtype: string
  - name: task
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  - name: prompt
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  - name: image_rubrics
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  - name: text_rubrics
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  - name: image_ref
    sequence: image
  - name: text_ref
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  splits:
  - name: test
    num_bytes: 301995195.0
    num_examples: 1000
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configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---
# UEval: A Benchmark for Unified Multimodal Generation

> [**UEval: A Benchmark for Unified Multimodal Generation**](https://arxiv.org/abs/2502.12150) </br>
> *[Bo Li](https://primerl.github.io/), [Yida Yin](https://davidyyd.github.io), [Wenhao Chai](https://wenhaochai.com/), [Xingyu Fu](https://zeyofu.github.io/)\*, [Zhuang Liu](https://liuzhuang13.github.io)\* (* indicates co-advising) <br>
> Princeton University<br>
> [[Paper]](https://arxiv.org/abs/2601.22155) [[Project page]](https://zlab-princeton.github.io/UEval/) [[Code]](https://github.com/zlab-princeton/UEval)

---
<p align="center">
<img src="https://github.com/user-attachments/assets/f66379a8-c571-4cba-8e9b-78d158ecd26c" width=100% height=100%
class="center">
</p>

We introduce **UEval**, a benchmark to evaluate unified models, i.e., models capable of generating both images and text. UEval comprises 1,000 expert-curated prompts that require both images and text in the model outputs, sourced from 8 diverse real-world domains.

## Results

We evaluate recent unified models on all 8 tasks in our benchmark. Overall, frontier models consistently outperform open-source ones across all tasks: GPT-5-Thinking achieves the highest average score of 66.4, while the best open-source model obtains only 49.1. The gap between proprietary and open-source models is very large: the strongest frontier model (e.g., GPT-5-Thinking) outperforms the best open-source model (e.g., Emu 3.5) by over 17 points on average.

| Model                           | Space    | Textbook | Diagram  | Paper    | Art      | Life     | Tech     | Exercise | Avg      |
| ------------------------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| *Reference*                     | 96.2     | 94.4     | 93.1     | 96.2     | 90.6     | 87.7     | 90.6     | 89.2     | 92.2     |
| Janus-Pro                       | 21.0     | 31.0     | 37.4     | 15.2     | 26.4     | 23.0     | 17.6     | 11.5     | 22.9     |
| Show-o2                         | 25.4     | 33.1     | 33.2     | 17.4     | 25.6     | 15.6     | 17.4     | 13.1     | 22.6     |
| MMaDA                           | 10.8     | 20.0     | 14.2     | 13.3     | 15.7     | 15.8     | 12.4     | 12.6     | 14.4     |
| BAGEL                           | 29.8     | 42.5     | 37.2     | 20.0     | 39.0     | 33.6     | 24.8     | 21.4     | 31.0     |
| Emu3.5                      | **59.1** | **57.4** | **41.1** | **31.6** | **59.3** | **62.0** | **37.0** | **45.4** | **49.1** |
| Gemini-2.0-Flash                | 65.2     | 55.2     | 47.6     | 45.8     | **70.4** | 58.0     | 50.2     | 48.0     | 55.1     |
| Gemini-2.5-Flash                | 78.0     | 74.0     | 66.4     | **71.6** | 66.6     | 63.0     | **58.2** | 50.0     | 66.0     |
| GPT-5-Instant                   | 77.3     | 77.9     | 62.3     | 55.1     | 71.2     | **69.7** | 50.7     | 57.6     | 65.2     |
| GPT-5-Thinking              | **84.0** | **78.0** | **67.8** | 51.9     | 67.8     | 63.8     | 57.0     | **61.4** | **66.4** |



<img width="1368" height="1417" alt="Image" src="https://github.com/user-attachments/assets/ede55cbf-fffd-4278-8fed-34d8c95596a8" />

<img width="1398" height="1542" alt="Image" src="https://github.com/user-attachments/assets/add410df-b267-4693-8a3d-b9c8e45108fb" />

## Citation
If you find this repository helpful, please consider citing:
```bibtex
@article{li2026ueval,
    title     = {UEval: A Benchmark for Unified Multimodal Generation},
    author    = {Li, Bo and Yin, Yida and Chai, Wenhao and Fu, Xingyu and Liu, Zhuang},
    journal   = {arXiv preprint arXiv:2601.22155},
    year      = {2026}
}
```