So I just realized Latenode gives you access to like 400 different models through one subscription—OpenAI, Claude, Deepseek, and a bunch of others. And I’m sitting here thinking, that’s amazing for flexibility, but also kind of paralyzing?
Let me give you a concrete example. I’m building an automation that extracts structured data from customer support tickets. I could use GPT-4 Turbo for high accuracy, but it’s slower and more expensive per token. Or I could use a smaller model like Claude Haiku, which is faster but maybe loses nuance on edge cases.
In theory, having access to 400 models means I could test each one and pick the best fit. But in practice, who has time for that? I’m trying to ship something this week, not spend three weeks benchmarking.
Do people actually experiment across multiple models, or do you just pick one that seems reasonable and stick with it? And if you do test multiple models, how do you structure those experiments without it becoming a whole project on its own?
This is the million-dollar question, and I love that you’re thinking about it strategically.
Here’s how I approach it: start with what you know about your task. If you need speed and cost efficiency, smaller models like Haiku or Llama 2 can be surprisingly good. If you need bulletproof accuracy on complex reasoning, go GPT-4 or Claude 3.5 Sonnet.
But here’s the secret: Latenode lets you test this in your actual workflow. You don’t need a separate test harness. Just build your automation with one model, run it on a sample of your real data, measure the output quality, then swap the model in your workflow and test again. Takes maybe 30 minutes total.
What I do is test 2-3 models on a representative dataset—maybe 50-100 samples—and pick the one that balances accuracy and cost. For support tickets, I’d test Claude 3.5 Sonnet, GPT-4 Turbo, and maybe Mixtral. Run them all, compare outputs, pick the winner.
The beauty of having 400 models in one platform is that you’re not locked in. You test intelligently, not exhaustively.
I went through this exact decision paralysis. What snapped me out of it was realizing I was overthinking it.
For most practical tasks, three models handle 95% of use cases: a fast cheap one (like Haiku), a balanced one (Claude 3.5 Sonnet), and a heavy hitter (GPT-4 Turbo). For your support ticket extraction, I’d honestly start with Claude 3.5 Sonnet. It’s genuinely strong at structured extraction with minimal hallucination.
Run it on like 20 representative tickets. If the output is solid, ship it. You can always iterate later.
The real insight is that you don’t need to test all 400. You need to test 2-3 and make a call. Most of the models in that 400 are either too niche, too new, or variations that won’t make a practical difference for your use case.
Model selection is genuinely easier than it sounds if you stop thinking about it as an optimization problem. For data extraction specifically, Claude models are typically the gold standard. They handle edge cases better and don’t hallucinate as much as some alternatives.
What matters more than the absolute best model is whether your choice is good enough for your use case. For support tickets, that’s probably pretty forgiving—your customer success team can catch mistakes.
I’d suggest a pragmatic approach: pick Claude 3.5 Sonnet (strong extraction capabilities, reasonable cost), test it, and only switch if results are actually problematic. Premature optimization on model selection is usually a waste of time when you could be shipping.
The approach I recommend is capability-driven rather than model-count-driven. Group your models by capability class: lightweight and fast (Haiku, Llama 2 8B), balanced (Claude 3.5 Sonnet, GPT-4 Turbo), and specialized (domain-specific models). Then match your task to the right class.
For structured extraction from support tickets, Claude 3.5 Sonnet is a strong default. It excels at understanding context and maintaining consistency in output format. Test it on representative data. If accuracy is high and cost is acceptable, you’ve got your answer.
The 400 models become useful when you have diverse workflows—some needing speed, some needing quality, some needing specific domain knowledge. But for a single task, you’re really choosing between maybe 5-10 realistic options.