Splitting Responsibilities Between Claude and Codex: How I Stopped Choosing One Neural Network and Boosted Both

A Go developer running his own project describes how he split roles between Claude and Codex instead of picking one AI model for all coding tasks. He argues that giving a single model the entire workflow—from requirement analysis to self-verification—is flawed because it lacks external control. His solution: Claude acts as tech lead (breaks down tasks, writes PLAN.md, reviews diffs, makes final decisions), while Codex implements the plan (writes tests, refactors, fixes issues). The two models cross-check each other: Codex reviews Claude's plan before implementation, and Claude reviews Codex's diff after. This leads to better quality without increasing cost, as expensive reasoning is used only for planning and review, while cheaper models handle bulk code reading and boilerplate. He details his setup: Claude Opus/high for decisions, Codex GPT-5.6 Terra/high for implementation, with escalation to Terra/xhigh or Sol for complex tasks. He emphasizes that 'high' effort setting is the minimum comfort level for development on economical models, as lower settings miss context and cause extra iterations. The article includes full installation instructions for Claude Code, Codex CLI, and the codex-plugin-cc plugin, plus a complete SKILL.md file that automates the workflow. The guide notes that the same workflow works from any terminal and does not require VS Code, though he finds it convenient.