I’ve been thinking about having all these different AI models available—OpenAI, Claude, Deepseek, and dozens more—all under one umbrella. On paper that sounds amazing. But practically, I’m wondering how you actually choose which one to use when you’re building automation.
Like, if I’m building a Puppeteer workflow that needs to analyze page content and decide what action to take next, does it matter which model I pick? Do some models handle web content analysis better? Are there models that are better at specific tasks but worse at others?
And then there’s cost and speed. Are some models cheaper but slower? Are there models that are overkill for simple classification but essential for complex reasoning?
I don’t want to just default to the “latest biggest model” for everything if there are cheaper or faster alternatives that work just as well for certain tasks. But I also don’t want to spend hours benchmarking every model against every use case.
How do people actually approach this? Do you test a few models, pick one that works, and stick with it? Or is there some logical way to think about model selection that makes it simpler?
This is the right question to be asking. The way most people handle it is by starting with a model that fits the task complexity, then optimizing from there.
For simple tasks like classification or data extraction from web pages, you don’t need the most expensive model. Claude’s smaller models or GPT-3.5 are often perfect and cost way less. For complex reasoning—like analyzing business logic across multiple pages and planning a multi-step workflow—you’d use a larger model.
The platform makes this easier because you can swap models without rewriting your workflow. Test with one model, see how it performs, switch to another if needed. Over time you develop intuition for which models work best for your common tasks.
Most teams land on 2-3 models they use repeatedly. Not because they have to, but because they figure out what actually works best for their specific patterns.
I’ve been thinking about this a lot too. The honest answer is that I started with whatever seemed most capable, and then I slowly realized I was spending way more than I needed to.
Now I think in categories. Basic text analysis or classification? Smaller model, cheap, fast enough. Understanding context from complex documents? Medium model. Actual reasoning or planning multi-step workflows? Larger model.
For web content extraction specifically, I’ve found that smaller models are often just as good as huge ones. A site’s HTML is structured and predictable. You don’t need genius-level reasoning to extract a price or a product name. But if you’re extracting data that requires understanding context or making judgment calls, then yes, a better model helps.
I think the key insight is that more capability doesn’t always mean better results for your specific task. It just means higher cost.
Model selection should be driven by task-specific requirements, not by selecting the most capable model universally. Classify tasks by cognitive demand: factual extraction from structured content (low demand), analysis requiring context (moderate demand), planning and reasoning across multiple sources (high demand).
Cost scales dramatically across models. A task requiring a mid-tier model costs 3-5x that of a base model, but may provide only 15-20% performance improvement. For scalable systems, this compounds rapidly. Define minimum performance thresholds for each task type, then select the least expensive model meeting that threshold.
Experience suggests that most web automation tasks cluster in the low-to-moderate demand range. Test comprehensively before committing to a model tier.
Simple extraction? Use cheaper model. Complex reasoning? Use bigger model. Test a few, find what works, stick with it. Most automation doesn’t need the most expensive option.