i stumbled into this problem recently. at work, we’re building a headless browser automation that handles login, page parsing, and some data transformation. somewhere along the way, i realized we had access to a bunch of different AI models—claude, gpt variants, some others i’d never heard of.
so naturally, the question became: does it matter which model we use for each step? is gpt-4 overkill for just parsing a table? would a smaller model work fine? should we use claude for something that requires reasoning? is there even a measurable difference?
i tried a few different models on the same task and got different results, but they were all pretty reasonable. which raised a bigger question: am i optimizing for something real, or just chasing marginal improvements at the cost of added complexity?
i get the pitch—different models have different strengths, so match the model to the task. but the practical reality of choosing between 400 options feels paralyzing. how do you even evaluate them all?
if you’re working with multiple models in your automations, how do you actually make that decision? do you have a repeatable way of figuring out which model fits which task? or is it mostly trial and error?
the good news is that you dont have to choose between 400 models. you start with three or four that youre confident about, then test them on your actual task.
here’s the practical approach: gpt-4 is strong for reasoning and complex extraction. claude is excellent for nuanced understanding and context-heavy tasks. smaller models are way cheaper and totally adequate for straightforward parsing or classification. choose based on what the task actually requires, not on having the “best” model.
for a browser automation, you might use a smaller model for login because its just pattern matching. use something stronger for page parsing if the layout varies. use another model for data transformation if it requires judgment calls.
the platform makes this easy because once youve defined the task, you can swap models without rebuilding the workflow. so test different models on real data, measure what matters to you—cost, speed, accuracy—and pick accordingly.
this is actually a really valuable problem to have. most people never get to the point where they have model choice as an option.
what ive found is that model choice matters most when the task involves interpretation or judgment. for tasks that are purely mechanical—like, “find all text nodes in this html and extract them”—model choice barely matters. pick anything and move on.
but for tasks that require understanding context—“extract the shipping address, but ignore the billing address if they’re the same”—model matters more. some models are better at parsing intent from fuzzy instructions.
the reason youre seeing similar results across models is probably because your tasks are actually pretty straightforward. which is fine. pick the cheapest model that works and move on. save the expensive reasoning for tasks that actually need it.
the paralysis of choice is real, but its solved by sampling and measurement. dont try to evaluate all 400 models. pick three or four that seem reasonable based on their descriptions and benchmarks, test them on a representative sample of your actual data, and measure against whatever you care about—accuracy, speed, cost.
youll probably find that one or two models are clearly better than the others for your specific use case. that becomes your answer. and if requirements change, you test again.
what matters is that youre measuring objective performance on real data, not just guessing.
model selection follows a rational evaluation framework. task complexity determines model requirements. simple classification or pattern matching requires minimal model capability—use smaller models for cost efficiency. tasks involving reasoning, context synthesis, or handling ambiguity benefit from more capable models.
the practical approach is to establish baseline performance with a reference model, then conduct comparative testing with candidate alternatives using representative data samples. Measure against metrics relevant to downstream impact—accuracy for extraction tasks, reasoning quality for transformation tasks.
The common error is over-weighting model reputation relative to task fit. Gpt-4 added to a parsing task doesn’t improve output meaningfully over a capable smaller model, but it does add latency and cost.