Most "which AI coding model should I use?" advice is based on someone else's codebase, someone else's prompts, and someone else's tolerance for wrong-but-confident answers. With free model access and free hosted servers becoming common across coding platforms, the real bottleneck is no longer access — it's evaluation. This article shows how to build a small, reproducible harness that scores models against tasks drawn from your repository, so your choice is based on evidence you can rerun.
Why public benchmarks don't answer your question
Public benchmarks measure aggregate performance on curated tasks. Your day-to-day work is narrower and stranger: your naming conventions, your framework versions, your tests. A model that tops a leaderboard can still mangle your ORM migrations or hallucinate your internal utility functions. The fix is cheap: a fixed prompt suite, a fixed scoring rubric, and a runner script you can point at any OpenAI-compatible endpoint.
Step 1: Build a task suite from your own repo
Pick 6–10 tasks that represent what you actually ask a coding assistant to do. Good candidates:
