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METR · February 13, 2026

Compare agent scaffolds under matched models, budgets and interaction conditions

analysis Model EvaluationAI Engineering
Why it matters

METR compared Claude Code and Codex with its usual scaffolds using Opus 4.5 and GPT-5 on autonomous time-horizon tasks. Neither specialized scaffold showed a statistically significant advantage in those comparisons. The investigation corrected scoring artifacts and examined budgets, wrapper errors and agents expecting a human response. The result is specific to the tested models, versions and unattended task distribution, rather than a current ranking of coding products.

My takeaway: Hold the model and task set fixed when evaluating a harness change. Inspect large performance differences for wrapper or scoring errors, account for token limits and retries, and state whether human interaction is allowed. Recheck the comparison when models, tools or task distributions change.
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