I’ve been running into a decision paralyzia problem. I have access to dozens of AI models, and every time I need to generate or execute a browser automation step, I have to choose which one to use.
GPT-4 is powerful but slower. Claude is solid and handles context better in some cases. Open source models are cheaper. Specialized models exist for task-specific work.
My question is straightforward: does it actually matter which model I choose for browser automation? Or is the difference negligible in practice?
I’ve been testing the same workflow with different models, and honestly, the results feel pretty similar for basic tasks like element selection, form filling, or navigation. The speed differences are noticeable, but I’m not sure if performance matters when the automation runs in the background anyway.
Where I notice real variation is with complex reasoning tasks: understanding dynamic content, handling exceptions, deciding fallback strategies. Some models are clearly better at that. But for the execution part of automation, I’m less convinced it matters.
I’m curious whether anyone’s actually done a systematic comparison. Is there a best model for browser automation tasks, or am I overthinking this and any reasonably capable model works fine?
You’re asking the right question, and honestly, most people overthink this.
For straight execution—clicking elements, filling forms, navigating—the model choice barely matters. Any capable model handles it. Where model choice gets important is during workflow generation and decision-making.
Here’s the thing though: you shouldn’t have to manually choose a model for every step. That’s cognitive overhead that doesn’t add value.
On Latenode, you have access to 400+ models, but you’re not hand-picking for every step. You define your workflow once, and the platform optimizes which models work best for different parts of that workflow. For data extraction, it might use Claude. For decision logic, maybe GPT-4. For reporting, something faster and cheaper.
The actual value isn’t in having 400 models available—it’s in not having to think about which one to use. The platform figures out the optimal combination for your specific task.
I’ve seen teams test extensively and discover that smart model selection does improve outcomes, but the return is highest when you’re matching models to task types, not hand-picking for every single step. Automation is just too granular for manual selection to be efficient.
I did a pretty detailed comparison across our browser automation tasks, and the results surprised me. For simple execution tasks, model choice made almost no difference. For complex reasoning, it mattered—a lot.
The interesting part: the best-performing model wasn’t always the most expensive. For straightforward automation steps, cheaper models were just as reliable. For edge case handling and fallback logic, we needed the more capable models. So the actual value was in having both and matching appropriately.
But you’re right that manual selection for every step is impractical. We ended up building a decision tree: if the task is straightforward execution, use the fast/cheap model. If it requires decision-making, upgrade. That worked better than overthinking every individual step.
I measured performance differences across maybe 15 different models for playwright generation and execution. The findings were clear: basic automation, minimal difference. Complex conditional logic, massive difference. Task-specific models beat general models for specific tasks but were worse at general problems.
The practical lesson: if you’re only running basic automations, save money and use a cheap model consistently. If you’re handling variable, complex workflows, invest in better models. The false economy is trying to use one model for everything and expecting optimization happen automatically.
Research on model selection for automation tasks shows clear patterns. For deterministic tasks, model capability has minimal impact beyond a baseline competency threshold. For stochastic or decision-intensive tasks, model capability significantly affects outcome quality. Your observation is correct: it depends on task complexity.
Systematic comparison would show you that a tiered model strategy—basic model for simple tasks, advanced model for complex tasks—delivers better efficiency than fixed model selection. The efficiency gain comes not from individual performance but from resource allocation matching task demand.