Separating signal from noise in coding evaluations

Accurately measuring our models’ capabilities is important for sound deployment and safety decisions, including decisions under OpenAI’s Preparedness Framework⁠(opens in a new window). With each model release, we report results for a variety of external and internal benchmarks to track model progress. When evaluations have flaws that affect results, they can give a false understanding of capabilities, misrepresenting safety cases and affecting research priorities.

We recently investigated how one of the most widely used coding benchmarks, SWE-bench Verified, had fundamental design and contamination issues, and found that the eval no longer provided meaningful signal on software development capabilities. At the time, we encouraged the wider community to switch to SWE-Bench Pro.

SWE-Bench Pro⁠(opens in a new window) was designed to improve on SWE-bench Verified by testing models on longer horizons and more realistic coding tasks to better track agentic coding capabilities. As in SWE-bench Verified, tasks are sourced programmatically from the history of feature changes in a set of public and private repositories. Models are required to implement a solution that passes new tests for a feature, without breaking existing functionality. On the 731-task public split, frontier models improved from a pass rate of 23.3% to 80.3% in eight months.

We’ve since performed a similar audit on SWE-Bench Pro, reviewing the dataset using a datapoint analysis pipeline. The pipeline reviewed model attempts at the task, task metadata, and failure traces to flag likely evaluation flaws. Each flagged task was then assessed through multiple investigator-agent passes and independently reviewed by five experienced software engineers, with disagreements escalated for further investigation.

The issues primarily fell into four categories: