One thing that initially seemed overwhelming is having access to 400+ AI models through a single subscription. Like, that’s a lot of choice. Too much choice, maybe?
My first instinct was to default to the “best” model—Claude, GPT-4, whatever the current top-tier option is. But using the strongest model for every step in your workflow feels wasteful. A complex model trained for nuanced reasoning might be overkill for extracting structured data from a webpage.
I started thinking about this differently. Each step in a browser automation workflow has different requirements:
Data extraction from structured elements — mostly pattern matching
Validation logic — straightforward rule checking
Data transformation — formatting, deduplication
Using a heavy reasoning model for all of these costs money and adds latency unnecessarily. But using a lightweight model for complex decision-making might cause failures.
The interesting part is that the platform lets you choose different models for different steps in the workflow. So theoretically, you could optimize—stronger model for page interaction where understanding is important, lighter model for extraction where speed matters.
But how do you actually make that choice? Is it trial and error? Do you test different models and compare? Or does the platform have recommendations based on the task type?
How do others approach this? Are you running all automation with a single strong model, or are you experimenting with model selection per step?
Most people default to a single strong model for everything, which is inefficient. But the platform is designed to let you optimize per-step. Different tasks have different model requirements.
Data extraction from structured HTML doesn’t need advanced reasoning. Smaller, faster models work fine. Page interaction and decision-making benefit from stronger models. You can mix them in the same workflow.
The practical approach is start with a solid baseline model, run your workflow, then experiment with lighter models in non-critical steps. You’ll see immediate latency improvements and cost reductions without sacrificing accuracy. The platform gives you the flexibility to test and compare directly.
I used to run everything with GPT-4 out of habit. Then I actually looked at what each step in my workflows was doing. Turns out, maybe 20% of steps actually needed heavy reasoning. The rest was extraction, formatting, simple conditional logic.
Swapping lighter models into those simpler steps cut my execution time by 30% and costs by maybe 40%. The AI quality stayed the same because those tasks don’t require deep reasoning anyway. The platform makes it trivial to test different models in different steps.
My process now is: start with a good baseline, identify bottleneck steps, experiment with lighter models there, measure the impact. It’s not complicated optimization, just obvious efficiency.
Model selection should match task complexity. Data extraction from structured content works fine with lightweight models. Page navigation decisions, understanding ambiguous page states, interpreting error messages—those genuinely need stronger models.
The straightforward approach is to analyze each workflow step, classify the complexity required, then assign appropriate models. You’re not overthinking this. Most workflows benefit from using 2-3 different models rather than one universal model.
Test it empirically. Run a workflow with one model, measure quality and speed. Swap in a lighter model for non-critical steps and compare. The differences are usually obvious within a few test runs.
Use strong models for complex decisions, lightweight for extraction and formatting. Mix models per step to optimize cost and speed. Test different combinations.