How do you benchmark Claude Code vs. Codex inside a software factory?

Benchmarking Claude Code and Codex in a software factory should rely on repeatable evaluations using 20–40 real, already-resolved tasks from an organization’s own repositories rather than public leaderboards, which may not reflect its workflows, codebase, standards, environments, or agent configurations. The comparison should isolate the harness, model, and context variables by freezing repository state and tools, providing equivalent prompts, skills, guidance, and MCP access, matching model tiers, and repeating each task three to five times to account for stochastic outcomes. Results should be assessed per workflow—such as triage, scoped implementation, refactoring, code review, and verification—using measures including cost per accepted outcome, existing-test pass rate, defects introduced or caught, and required human interventions, producing routing decisions instead of a single overall winner. Common mistakes include changing models and harnesses simultaneously, evaluating irrelevant tasks, treating generated diffs as success without considering review acceptance, allowing unequal skills to influence results, and failing to rerun benchmarks after updates. Warp Factories is presented as a version-controlled control plane that supports multi-harness and multi-model experiments, built-in and custom scoring, observer agents, and configurable data handling, while the recommended starting point is one workflow with a clear measurable outcome and human fallback.

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