Why Verification Cost, Not Model Strength, Should Drive AI Coding Decisions

A developer argues that the most important question when using AI coding tools is not which model is most capable, but how quickly and reliably its output can be verified. Low-cost models are considered effective for tasks with short verification paths, such as README edits, changelog notes, and formatting scripts, where errors are immediately visible. For testable work, tightly scoped prompts with explicit test cases help keep cheaper models operating within a verifiable frame. However, tasks involving fallbacks, permissions, billing logic, or backwards compatibility carry high verification costs and demand stronger models plus thorough human review. The core insight is that the true expense in AI-assisted coding often lies in building trust through verification, not in generating the code itself.