My AI Refactor Was Slow. The Model Wasn't Guilty.

Everyone talks about what developers do while AI codes. Mostly, we wait. So I measured the waiting. The model was not guilty.

For two weeks my workflow looked like this. Copy a module. Write a refactor prompt. Paste both into an AI coding assistant. Wait for a diff. Apply the diff. Run the tests. Every spinner felt like weak inference. Every pause felt like a slow model.

I was looking at the wrong suspect.

A refactor has four phases. Serialize the prompt. Wait for the server. Stream the tokens. Apply the diff. I timed each one. How else do you convict the right suspect? I kept one graph. This is the story of that graph.

I picked MonkeyCode for the experiment. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The project is open source, and the current free tier includes 10 million tokens and a free server option. That gave me two network paths to compare without paying for either.