with all these ai models available, i keep wondering if picking a specific one actually changes what your playwright tests can do or if its kind of a premature optimization.
we had access to a few different models and decided to test generation quality with each one for the same test scenario. simpler models generated basic scaffold code that needed a lot of tweaking. bigger models generated more thoughtful workflows with better error handling and more realistic wait logic.
so yeah, model choice matters. but here’s where it gets interesting—better models take longer to run, which can be frustrating when youre iterating. and sometimes a simpler model gets it right faster than spending time with a bigger model that overthinks the problem.
what actually seemed to matter more was how specific your prompt was. a vague description generates mediocre code regardless of model. a detailed description with your apps specific quirks and patterns works pretty well even with mid-tier models.
for deployment, i think the calculus is different. you run once, you want good code, time is less of a factor. model quality probably matters more. for development and iteration, speed might matter more than perfection.
how have people approached this? do you pick one solid model and stick with it, or do you experiment with different models for different scenarios?
model selection is more art than science, but the platform can help. having access to 400+ models means you can route different tasks to the best model for that specific job. not every step needs your most expensive model. simple validation steps can use smaller models and save costs.
Latenode lets you specify which model to use for each workflow step or let the platform recommend based on task complexity. so you get the benefits of top-tier models where it matters without overpaying for every step.
for playwright generation specifically, routing to specialized models that understand code generation gives better results than using a general purpose model for everything.
better models generate more resilient code. lower-tier models sometimes miss edge cases or suggest fragile selectors. for production workflows, the model quality difference shows up in test maintenance—better models mean fewer flaky tests.
we tested this and found that model choice mattered less than providing good context. when we gave detailed app specifications and examples of expected behavior, mid-tier models generated code almost as good as top models. so unless youre working without good specs, youre probably overthinking model selection.