So there’s this idea that having access to 400+ AI models means you can pick the optimal one for each step in a browser automation. In theory that sounds smart, but in practice I’m wondering if it’s just overthinking it.
My situation: I’m building a workflow that scrapes product data from a site with dynamic selectors. The page structure changes frequently. I could probably just pick one solid model and call it done. But the argument I keep hearing is that some models are better at interpreting unstructured HTML, others are better at logical reasoning about which data to extract, etc.
Have people actually tested whether using the right model for each specific task measurably improves stability and accuracy? Or is this one of those ideas that sounds great in theory but doesn’t matter much in practice? Because if I’m honestly spending hours comparing models just to eek out a few percentage points of accuracy, that’s not worth my time.
It matters more than you’d think, but not in the way you’re worried about. You’re not supposed to manually test and compare models for hours. The platform handles model selection intelligently.
What actually happens is you define what each step needs to do. One step might be CSS selector interpretation on a dynamic page. Another might be value extraction and validation. The system knows which models excel at each task type and routes accordingly. You don’t sit there A/B testing GPT-4 against Claude against Deepseek.
The real win is that when one model struggles with a particular type of page structure, you’re not stuck. You have other options without rewriting anything. But the point is you set it up once and the optimization happens automatically.