Running two AI models against each other for code review costs more, but catches errors a single model misses.
A technique circulating among developers uses OpenAI's Codex to audit code that Claude writes. The workflow is direct: generate code with Claude, then pass that output to Codex with a prompt asking it to identify bugs, security gaps, or logic errors. Because the two models were trained differently and have different architectural designs, they tend to have different blind spots - what Claude glosses over, Codex may flag, and vice versa.
The approach works best for high-stakes code: authentication systems, payment flows, data validation pipelines. For quick utility scripts or throwaway tools, the extra API cost and setup probably isn't worth it.
Single-model validation has real limits. When Claude writes code and Claude reviews it, the same reasoning patterns that introduced a bug can miss it during review. Bringing in a second model trained on different data adds a genuinely independent perspective. Some teams extend this further, running outputs through multiple models before committing anything to production.
The main friction is workflow setup and cost - you're paying for two inference calls (the actual compute process of running the model) per review, and you need to pipe output between systems. For developers who've spent hours debugging subtle AI-generated errors, catching those issues before they ship is usually cheaper than finding them after.