How to Evaluate New Coding AI Models Without Blowing Your Budget

A software developer has published a practical framework for assessing the growing stream of new AI coding models without incurring runaway evaluation costs. The approach centers on routing tasks by difficulty tier — easy, medium, and hard — so that cheaper models handle simpler tasks and expensive frontier models are reserved only for complex cases. The developer argues that most public leaderboard results are poor predictors of real-world performance on a specific codebase, making task-tiered local evaluation more meaningful. A working Python sketch is provided, showing how an eval harness can automatically escalate a task to a stronger model only when a cheaper one fails. The method aims to concentrate evaluation spending where model differences are most detectable, rather than running every model against every task indiscriminately.