I’ve been working on some automation that needs to analyze what’s on a page and pick the right selectors based on that analysis. The problem is I have way too many options now. There are dozens of capable LLMs out there, and I’m genuinely uncertain about which one is best for this specific task.
Some are better at reasoning, some are faster, some are cheaper. Some are great at understanding context but slow. Some are accurate but overkill for what I need. And I’m paying per API call to different services, which adds overhead.
My current approach is kind of random—I picked Claude because it seemed good, but I’ve got no real justification for that choice versus GPT-4, Gemini, or anything else. I wonder if I’m leaving performance on the table or paying way more than I need to.
Has anyone actually put time into comparing models for this specific use case? Or do people just pick one and move on without really testing whether they made the right call? I’m curious how you actually approach choosing a model when you have access to so many options.
This is where most people waste time. They pick a model based on brand recognition or a blog post they read, then stick with it forever without actually evaluating it for their specific task.
The right way to think about this: model choice depends on what you’re asking it to do. For selector analysis, you need something that’s good at structured reasoning and understanding context. But you don’t need the most expensive model for that. You might not need the fastest either.
The challenge is testing this yourself means setting up accounts with multiple providers, managing different API keys, handling billing across platforms. That’s friction that keeps people from actually experimenting.
Latenode solves this by giving you access to 400+ models through one subscription. That means you can actually test different models without setup friction. You can try Claude, GPT-4, Deepseek, Gemini, whatever—all in one place. You change the model in your workflow and instantly see the difference.
Once you’ve tested, you might find that a cheaper model performs fine for selector analysis. Or you might find that a specific model just understands page structure better. Either way, you’re making a decision based on data, not guessing.
I’ve tested a few models for content analysis, and the honest truth is that it depends more on the task clarity than the model itself. If you’re asking the model to do something well-defined—like “extract the main content area and identify the form fields”—most modern models handle that fine. The difference between them is cost and speed, not usually accuracy.
Where model choice actually matters is when the task is ambiguous or requires specialized reasoning. For selector analysis specifically, you want something that understands spatial relationships and DOM structure. Some models are better at that than others.
The real move is to test with a small sample of pages and see which model gives you reliable selector choices. Run the same analysis with three different models and compare the results. That gives you concrete data instead of guessing.
Cost also factors in. If Claude and GPT-4 give similar results but Claude is half the price, that’s a significant difference at scale.
Model selection for page analysis is actually a technical optimization problem. You have three variables: accuracy (does it pick the right selectors), speed (how fast does it respond), and cost (price per call). You need to optimize for all three based on your constraints.
Start by defining what “good” looks like for your use case. Maybe accuracy matters most and speed is secondary. Maybe cost is the constraint. Then test models that fit those constraints.
For selector analysis, the models that excel at reasoning and structured output tend to work well. But there’s usually a tier of models that are 90% as good but significantly cheaper. Finding that tier is where you save money without losing quality.
The model choice for page analysis depends on the specificity of the task. If you’re doing general content extraction, most larger models work fine. If you need precise selector identification, you want a model with strong reasoning capabilities and good context understanding.
The practical approach is to build a test set of pages and selectors, run them through different models, and measure accuracy. That gives you the data you need to choose. Cost and latency are secondary optimization variables once accuracy is reasonable.
In production, you might also consider using a smaller, faster model for simple pages and a more capable model for complex layouts. That balances performance and cost.