How to Choose an AI Model for Coding Tasks
Compare models using real repository tasks, test results, and the amount of review each answer needs.
Start with a task you can check
Choose a small task from your repository: fix a reproducible bug, add input validation, or explain an unfamiliar function. Write down the expected result first. A test or a clear acceptance criterion gives you a better comparison than choosing the answer that sounds most confident.
Keep the context consistent
Give each candidate the same prompt, files, and constraints. Include the runtime version and validation commands. Avoid showing one model the entire repository while another sees only an error message. Use separate conversations so earlier answers do not influence the comparison.
Measure the work left for you
Check whether the proposed change builds, passes the relevant checks, and handles the original failure. Record how many corrections you requested and review new dependencies. A fast response can still cost more developer time if it creates a broad refactor for a narrow bug. Inspect the diff as well as the final explanation.
Match the model to the scope
Try a faster candidate for repetitive edits and bounded questions, then evaluate other candidates for reasoning across several files. Treat that as a hypothesis to test, not a guarantee about any model family. Rerun representative tasks when you change models or upgrade your client.
Verify availability and configuration
Use the exact model ID from the current catalog rather than a display name remembered from another tool. Check that your client supports the required API format and your plan includes the model. Keep a short record of the task, model, result, and review time so your next choice is based on evidence from your own work.