Practical Agent Model Switching

Quick, effective heuristics for picking the right AI model for the job

Windsurf AI has quickly solidified its role in my development workflow. With this change comes a necessary shift in thinking about the work. New rhythms, new patterns to feel out. And unexpected new skills to refine: like knowing when to switch models for a given task. I classify these models into three types. They’re far from official, but they’ve held up well for me: performance, thinking, and basic.

Performance

These are often the newest, most expensive models to use. They’re built for complexity, for context-heavy tasks that need precision and depth. If I’ve got something big and tangled to accomplish, and I want it done right, I bring out the heavy artillery. It lets me pair my experience with something that doesn’t blink and doesn’t get tired.

Thinking

These versions I love. These models think out loud. You give them a question, and they don’t just spit out an answer—they lay the whole thought process on the table. Brilliant. When you’re venturing into unfamiliar territory you couldn’t ask for more. Like, say, implementing Postgres’ tsvector in Elixir for the first time. You’re not just solving a problem; you’re learning. And better still, you get to catch it when it goes off the rails. You get to push back. You get smarter.

Basic

These models are my go-to grunts. Reliable, cheap, do-as-I-say. I use them when I know exactly what needs to be done—I just don’t feel like doing it. The work is simple, the outcome clear, and I don’t need the thing getting creative. One caveat: I keep them boxed into a single file. I’ve seen too much chaos when a basic model tries to go beyond a single file. They don’t reason well across boundaries. But if it’s a focused task you know your way around, this is your cost-effective, zero-fuss answer.