Artificial intelligence benchmarks are an important mechanism for measuring model progress and guiding deployment decisions. However, benchmarks quickly"saturate", making it difficult to differentiate models and diminishing their long-term value. In this study, we define benchmark saturation and analyze it across 60 language model benchmarks using 14 properties that relate to saturation. We find that nearly half of the our benchmarks exhibit saturation, with rates increasing with age. Further, we find that resilience to saturation is impacted by expert-curation, not by public test data. Our results suggest that design choices can extend benchmark longevity and inform more durable evaluation approaches.
@misc{akhtar2026aibenchmarksplateausystematic,
title={When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation},
author={Mubashara Akhtar and Anka Reuel and Prajna Soni and Sanchit Ahuja and Pawan Sasanka Ammanamanchi and Ruchit Rawal and Vilém Zouhar and Srishti Yadav and Chenxi Whitehouse and Dayeon Ki and Jennifer Mickel and Leshem Choshen and Marek Šuppa and Jan Batzner and Jenny Chim and Jeba Sania and Yanan Long and Hossein A. Rahmani and Christina Knight and Yiyang Nan and Jyoutir Raj and Yu Fan and Shubham Singh and Subramanyam Sahoo and Eliya Habba and Usman Gohar and Siddhesh Pawar and Robert Scholz and Arjun Subramonian and Jingwei Ni and Mykel Kochenderfer and Sanmi Koyejo and Mrinmaya Sachan and Stella Biderman and Zeerak Talat and Avijit Ghosh and Irene Solaiman},
year={2026},
eprint={2602.16763},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2602.16763},
}
See the full reference list in the paper.