Overview
The original SWE-bench Multimodal release augmented the benchmark with 517 issues containing visual elements such as:
- Screenshots of bugs or interface issues
- Design mockups or wireframes
- Diagrams explaining desired functionality
- Error messages with visual context
This extension evaluates models' ability to interpret and act on information presented in both textual and visual formats.
Multimodal v2
V2 retains 480 tasks selected for reproducible evaluation, with known flaky or ungradeable tests removed.
- The complete test split and evaluation tooling are open source.
- Docker environments were rebuilt to address dependency and browser drift.
- JavaScript grading and visual test-asset handling were made more robust.
Correspondence
For questions about SWE-bench Multimodal, please contact:
Citation
If you use SWE-bench Multimodal in your research, please cite our paper:
@inproceedings{yang2025swebench,
title={SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?},
author={John Yang and Carlos E Jimenez and Alex L Zhang and Kilian Lieret and Joyce Yang and Xindi Wu and Ori Press and Niklas Muennighoff and Gabriel Synnaeve and Karthik R Narasimhan and Diyi Yang and Sida Wang and Ofir Press},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=riTiq3i21b}
}
Yang, J., Jimenez, C. E., Zhang, A. L., Lieret, K., Yang, J., Wu, X., Press, O., Muennighoff, N., Synnaeve, G., Narasimhan, K. R., Yang, D., Wang, S. I., & Press, O. (2024). SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? arXiv preprint arXiv:2410.03859.
Yang, John, et al. "SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?" arXiv preprint arXiv:2410.03859 (2024).