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This article introduces SWE-Bench Pro V2, a rigorously audited benchmark designed to maintain difficulty and accuracy as coding agents advance. By retiring tasks that became too easy and hardening the evaluation protocol against data leakage, it provides a more reliable metric for assessing long-horizon software engineering capabilities. The release includes a distinct "Hard" split that effectively separates frontier models from other systems, offering critical insights into current agent limitations.
SWE-Bench Pro V2: A Cleaner, Harder-to-Game Coding Benchmark
arXiv:2610.24206v1 Announce Type: cross Abstract: SWE-Bench Pro is a widely used benchmark of long-horizon software engineering tasks for coding agents. As agents improve, they reach task issues earlier agents never touched and interact with the evaluation environment in ways the original harness did not anticipate. We present SWE-Bench Pro V2, a revised public split built to keep the benchmark accurate as capabilities advance. Agentic audits, expert adjudication and blind re-implementation repaired or retired 480 of the 731 tasks: 23 contracted engineers spent 404 hours, each working in a language they write professionally, and every revised instruction was verified by a second engineer solving the task from the text alone. A hardened protocol restricts network access to the model endpoint and re-grades every patch in a clean container, so agents cannot retrieve upstream fixes or get credit for changes outside their patch. On the 642 V2 tasks frontier agents resolve up to 99.4% while other systems stay clearly separated, and a 51-task Hard split spreads systems from 52.9% to 98.0%. We release the benchmark and distill lessons for maintaining agent benchmarks over time.
Abstract:SWE-Bench Pro is a widely used benchmark of long-horizon software engineering tasks for coding agents. As agents improve, they reach task issues earlier agents never touched and interact with the evaluation environment in ways the original harness did not anticipate. We present SWE-Bench Pro V2, a revised public split built to keep the benchmark accurate as capabilities advance. Agentic audits, expert adjudication and blind re-implementation repaired or retired 480 of the 731 tasks: 23 contracted engineers spent 404 hours, each working in a language they write professionally, and every revised instruction was verified by a second engineer solving the task from the text alone. A hardened protocol restricts network access to the model endpoint and re-grades every patch in a clean container, so agents cannot retrieve upstream fixes or get credit for changes outside their patch. On the 642 V2 tasks frontier agents resolve up to 99.4% while other systems stay clearly separated, and a 51-task Hard split spreads systems from 52.9% to 98.0%. We release the benchmark and distill lessons for maintaining agent benchmarks over time.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2610.24206 [cs.SE]
(or arXiv:2610.24206v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2610.24206 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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