How to Choose an AI Coding Model: Test It on Your Own Code

Write down the tasks and how you will check them

Fixing a bug, building a screen and understanding a large repository call for different abilities. Pick 5–10 tasks you meet in daily work. Describe the input clearly, save the expected result, and define the pass condition: it compiles, a feature check passes, or a person approves it. Do not let a model grade its own work.

Pick a few candidates from the coding leaderboard

Use the coding leaderboard to narrow the field, then check the exact model version, the evaluation sources and how much evidence covers it. The consensus index is for comparison and is not a code accuracy rate. When ranks are close or evidence is thin, keep one more candidate. The top model may still not fit your project's constraints.

Compare with the same context

Give every candidate the same code, task description and tool permissions. Record whether the first attempt works, how many fixes it needs, the time taken, and input and output tokens. For agent workflows, also record the tool calls made and which files were changed. Check the result before judging how polished the answer looks.

A simple log: task / model version / first-try pass / number of fixes / total tokens / total time. Repeat a few tasks so that one lucky success does not decide the whole choice.

Split the budget by task

You can send routine edits to a cheaper model and hard-to-locate problems to a stronger one, but every switch adds context and maintenance cost. Get one model working reliably before deciding whether a second one is worth it.