I’ve been thinking about the fact that there are so many AI models available now, and when you’re building Playwright automations, you might want to use different models for different tasks. Generate test data here, analyze failures there, create scripts somewhere else.
But I’m overcomplicating this? Does it actually matter which model you use for each step, or are the differences marginal enough that you just pick one and move on?
My intuition is that for something like generating Playwright scripts, a larger, more capable model probably does better than a smaller one. But for generating test data or sanitizing logs, maybe the overhead of using GPT-4 isn’t worth it when a faster, cheaper model works fine.
Also, I’m wondering about consistency. If you switch models mid-workflow, do you get inconsistent outputs? Or are they similar enough that it doesn’t matter?
Has anyone done actual testing to see if model choice impacts the quality of generated Playwright workflows or test data, or is this overthinking it?
You’re not overthinking it—model choice absolutely matters, but not everywhere. For Playwright script generation, a stronger model like Claude or GPT-4 is worth it. They understand timing semantics and error handling better. Switching to a faster model for that is false economy.
For data generation and log analysis, you’re right that a smaller model often works fine and is cheaper. The smarter approach is picking the right tool for the job, not using the strongest model everywhere.
Latnode makes this easier because you can specify which model to use at each step. So you’re generating scripts with Claude, test data with a faster model, and analysis with whatever fits. The platform handles the consistency—outputs stay compatible across the workflow.
One tip: test both locally before committing. Run the same task with two models and compare. You’ll see where the difference matters and where it doesn’t. That’s faster than guessing.
I’ve gone through this exact decision tree on a few projects. For me, the pattern that emerged is that generation tasks benefit from stronger models, but filtering and routing tasks don’t. So when I’m writing Playwright scripts, I use Claude. When I’m routing test results to different handlers, I use something lighter.
The consistency question is real though. If you switch models, make sure the output format is the same. That’s where things break—not in quality, but in format compatibility.
From my experience, model selection matters most when you’re generating new code or complex logic. For parsing, classification, and routing tasks, the difference is negligible if the prompt is clear. I’d recommend starting with one solid model—Claude or GPT-4—for all critical steps, then optimize later if cost becomes an issue. You’ll find that the prompting matters much more than which model you pick.