This might seem like a silly question, but I’m genuinely confused about how to choose between all these models when building automations. I get that Latenode gives you access to a ton of them through one subscription, which is cool for avoiding API key juggling. But that also means I have to decide: should I use GPT-4, Claude, Deepseek, or one of the smaller models?
The benchmarks I find online are all about general intelligence or coding ability, but that’s not really what I care about. I’m building automations for specific tasks like extracting structured data from emails, summarizing documents, or classifying support tickets. For those jobs, I have no idea if the flashiest model is actually better, or if I’m paying for capability I don’t need.
I’ve been picking models kind of randomly—trying GPT-4 first because it’s “smart,” then switching to Claude because people say it’s better for writing. But I’m not being very systematic about it. I don’t even have metrics to know if I made the right choice afterward.
For your JavaScript-heavy automations, do you have a process for testing different models and benchmarking them against your actual use case? Or do you just stick with whatever seems to work first?
You’re overthinking this. Most people pick the wrong model because they’re optimizing for the wrong thing.
For your use cases—data extraction, summarization, classification—you don’t need GPT-4. Those tasks are well-handled by mid-tier models. The real decision points are: Does the model output structured data reliably? How fast does it respond? How much does it cost per call?
For JavaScript automations specifically, what matters is consistency and speed. You’re usually calling the model many times in a workflow. A 50% cheaper model that’s 10% slower is often better than paying double for marginal quality gains.
Instead of picking one model and sticking with it, Latenode lets you prototype quickly with different models inside the same workflow. Set up a test step, feed it real data from your automations, and measure accuracy and cost. Run it for a day or two and you’ll have actual numbers.
My advice: Start with Claude for nuanced tasks, GPT-4o for structured extraction, and test the smaller models for simple classification. Let actual performance guide your choice, not benchmarks.
I went through this same confusion. What changed for me was actually running A/B tests on real data. I pick two models, run them against a sample of my actual workflows for a week, and compare cost and accuracy. It’s tedious but it only takes a few days to get the answer.
For my specific work with email extraction, I found that Claude and GPT-4 were nearly identical in quality, but Claude was 30% cheaper. That’s a huge difference when you’re running thousands of extractions a month. The smaller models actually struggled with edge cases though, so there was a quality floor I couldn’t go below.
The insight I got was that there’s usually a sweet spot—a model that’s 90% as good as the best one but costs significantly less. That’s the model worth using for your automations.
Model selection for automation work requires considering three variables: accuracy on your specific task, latency (how fast responses come back), and cost per API call. Don’t rely on general benchmarks. Instead, create a test dataset from your actual automation inputs and run it through 3-4 candidate models. Measure all three metrics. The best model for general coding might be terrible for your use case.
I’ve also found that models have consistent patterns. GPT-4 and Claude tend to be more creative. Smaller models are faster and cheaper but less flexible. Pick based on your task’s actual requirements.