| Title: |
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces |
| Authors: |
Merrill, Mike A.; Shaw, Alexander G.; Carlini, Nicholas; Li, Boxuan; Raj, Harsh; Bercovich, Ivan; Shi, Lin; Shin, Jeong Yeon; Walshe, Thomas; Buchanan, E. Kelly; Shen, Junhong; Ye, Guanghao; Lin, Haowei; Poulos, Jason; Wang, Maoyu; Nezhurina, Marianna; Jitsev, Jenia; Lu, Di; Mastromichalakis, Orfeas Menis; Xu, Zhiwei; Chen, Zizhao; Liu, Yue; Zhang, Robert; Chen, Leon Liangyu; Kashyap, Anurag; Uslu, Jan-Lucas; Li, Jeffrey; Wu, Jianbo; Yan, Minghao; Bian, Song; Sharma, Vedang; Sun, Ke; Dillmann, Steven; Anand, Akshay; Lanpouthakoun, Andrew; Koopah, Bardia; Hu, Changran; Guha, Etash; Dreiman, Gabriel H. S.; Zhu, Jiacheng; Krauth, Karl; Zhong, Li; Muennighoff, Niklas; Amanfu, Robert; Tan, Shangyin; Pimpalgaonkar, Shreyas; Aggarwal, Tushar; Lin, Xiangning; Lan, Xin; Zhao, Xuandong; Liang, Yiqing; Wang, Yuanli; Wang, Zilong; Zhou, Changzhi; Heineman, David; Liu, Hange; Trivedi, Harsh; Yang, John; Lin, Junhong; Shetty, Manish; Yang, Michael; Omi, Nabil; Raoof, Negin; Li, Shanda; Zhuo, Terry Yue; Lin, Wuwei; Dai, Yiwei; Wang, Yuxin; Chai, Wenhao; Zhou, Shang; Wahdany, Dariush; She, Ziyu; Hu, Jiaming; Dong, Zhikang; Zhu, Yuxuan; Cui, Sasha; Saiyed, Ahson; Kolbeinsson, Arinbjörn; Hu, Jesse; Rytting, Christopher Michael; Marten, Ryan; Wang, Yixin; Dimakis, Alex; Konwinski, Andy; Schmidt, Ludwig |
| Publication Year: |
2026 |
| Collection: |
ArXiv.org (Cornell University Library) |
| Subject Terms: |
Software Engineering; Artificial Intelligence |
| Description: |
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows. Each task features a unique environment, human-written solution, and comprehensive tests for verification. We show that frontier models and agents score less than 65\% on the benchmark and conduct an error analysis to identify areas for model and agent improvement. We publish the dataset and evaluation harness to assist developers and researchers in future work at https://www.tbench.ai/ . |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
http://arxiv.org/abs/2601.11868 |
| Availability: |
http://arxiv.org/abs/2601.11868 |
| Accession Number: |
edsbas.D1674F1D |
| Database: |
BASE |