On paper, switching AI models sounds smart.
Use ChatGPT for speed. Claude for deeper thinking. Perplexity for research.
In practice, it usually looks like this:
- log into a new tool
- start a new chat
- re-explain the project
- re-state constraints
- re-paste examples
By the time you’re ready to ask your real question, you’ve lost momentum.
The Problem Isn’t the Models
Most comparisons focus on which AI is “better”.
That misses the point.
The real cost isn’t model quality.
It’s switching friction.
Even if one model is 10–15% better for a task, that advantage disappears if:
- you have to restate everything
- you lose the thread of your work
- you avoid switching because it’s annoying
That’s why most people stick with one model even when they know another would be better.
What People Actually Optimize For
Without realizing it, users optimize for:
- lowest friction
- least re-explaining
- fastest path back to “where I was”
Not “best reasoning benchmark”.
When Switching Finally Makes Sense
Switching models only becomes worth it when:
- your context moves with you
- the new model already knows what you’re working on
- you can jump between tools without resetting
At that point:
- Claude can handle deep analysis
- ChatGPT can handle fast iteration
- Perplexity can handle research
And you don’t pay a cognitive tax every time.
Until then, switching feels like starting over — because it is.