I’ve been experimenting with workflows that use different AI models for different tasks. Like, using one model for extracting text from pages, another for summarizing what it found, and a third for deciding whether the data is valid.
The thing is, when you have access to that many options, the choice paralysis is real. I spent a day trying different combinations and I honestly can’t tell if I’m optimizing or just going in circles.
For data extraction, I’ve noticed some models are faster but less accurate. For decision-making logic, some give you structured output but take longer. For summarization, some compress things too aggressively.
I started keeping notes on what worked for each type of task, but it’s manual and feels inefficient. I’m wondering if there’s a pattern I’m missing or if it actually doesn’t matter that much in practice.
Does anyone have a system for picking the right model, or do you just stick with one and call it a day?
The honest answer is that it matters less than people think, but it does matter in specific ways.
I started by testing every model for everything, which was pointless. What actually helped was thinking about what each step needs. For extraction, you want reliability over speed. For classification, you want consistency. For creative work, you want the newest model.
Then I stopped manually picking and let the platform handle routing based on the task type. That sounds like I’m giving up control, but actually it’s the opposite—I’m delegating the optimization to something that tests every combination.
The benefit of having 400 models isn’t that you need to pick the perfect one. It’s that you can match the right tool to each job without paying per-model licensing. You pick the few that work best for your common patterns and leave the rest as backups.
https://latenode.com has docs on this that might help you set it up.
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I spent way too much time on this before I realized something simple: the difference between most models for extraction is marginal. What actually mattered was consistency.
I picked Claude for extraction because it handles edge cases well. I use GPT-4 for decisions because it reasons better. For summarization, the faster models work fine because the task is simpler.
Once I locked those in, I stopped thinking about it. The time I saved by not second-guessing myself was actually bigger than any performance gain from micro-optimizing model selection.
Model selection comes down to what you’re actually trying to optimize for. If speed is the bottleneck, use the fastest model that’s accurate enough. If accuracy is critical, use the most capable one that fits your budget.
The mistake I made initially was trying to optimize everything at once. I’d run a task with five different models and compare results, but the differences were tiny and irrelevant to what I actually needed. Focus on the constraint that actually hurts you—speed, cost, or accuracy—and optimize for that single thing.
Most people overthink this. For browser automation specifically, you rarely need more than three models. One for extraction, one for analysis, one for decision-making. That covers 90% of use cases.
The pattern I use is: test with the cheaper model first, then upgrade to a better one only if it fails regularly. You’ll find that most tasks work fine with mid-tier models and the premium ones are only necessary for edge cases.
just start with claude for everything. works good enough for most tasks. switch to gpt-4 only when you hit limits. that’s basically it. no need to overthink
Test one model per task. Use its metrics to decide if you need better. Most workflows use 2-3 models total, not all 400.
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