I just found out that the platform gives access to a ton of different AI models—OpenAI, Claude, Deepseek, and more—all under one subscription. Which is great, except now I’m staring at a choice between dozens of options and I have no idea which one to pick for my Puppeteer workflows.
Like, for a workflow that needs to classify extracted page text, do I use one model? For generating dynamic search queries, should I use a different one? And how do the costs factor in if they’re all under one subscription anyway?
I’m imagining there’s some criteria I’m missing. Speed versus accuracy? Cost per token? Specific strengths—one model is great at understanding context, another at following exact instructions? Or am I overthinking this?
I don’t want to spend days benchmarking every model against my exact use case, but I also don’t want to default to one and miss something obviously better.
What’s the practical heuristic people actually use when they’re picking a model for a specific Puppeteer task? Do you pick one and stick with it, or do you A/B test, or is there a guide somewhere that maps use cases to models?
The model choice depends on your specific task, not on overthinking price. Since everything is one subscription, cost isn’t the limiter—pick for capability.
For text classification from Puppeteer extracts, Claude excels at nuanced understanding. For generating search queries or command instructions, GPT-4 is precise. For faster, cheaper tasks where speed matters more than nuance, Deepseek works.
I use this heuristic: start with what the community says works for your task type. Text understanding? Claude. Instruction following? GPT-4. Math or logic puzzles? Different choices. Then run one real task through it and see if output quality matches your needs.
You don’t need to benchmark all 400. Maybe test three models on a sample of your actual data, pick the one that works, and move on. Iteration beats paralysis.
I use different models for different parts of my Puppeteer workflows. For text extraction and parsing, Claude handles context well. For classifying that extracted data into categories, GPT-4 is reliable. For fast, simple tasks like checking if a string matches a pattern, even a smaller model works.
The practical approach: start with what’s known. Claude for understanding text, GPT-4 for precision, Deepseek for speed when accuracy matters less. Test one task through each, see which output quality you prefer, move forward.
Benchmarking everything is overkill. You learn which models fit which tasks over time, and it becomes intuitive. I probably use three or four models regularly because they’re good at different things.
The model variety is useful because different models have different strengths. For my Puppeteer workflows, I use Claude when I need deep text understanding, GPT-4 when I need to follow instructions precisely, and smaller models for quick classification tasks where speed matters.
I started by trying a couple models on real data from my workflows and seeing which outputs I preferred. That’s faster than reading comparisons. Speed versus accuracy became apparent quickly: some models were overkill for simple tasks, others struggled with complexity.
I settled on using two main models for my workflows. Overkill to test all 400 when the ones that fit your needs become clear after a few experiments.
Model selection depends on task requirements and output quality expectations. Text comprehension tasks benefit from larger, more nuanced models like Claude. Instruction-following tasks favor GPT-4’s precision. Speed-sensitive tasks with simple requirements accept smaller, faster models.
Cost efficiency under unified subscription model removes pricing from decision calculus. Selection optimization occurs through empirical testing of candidate models on representative workflow data rather than theoretical comparison. Three to four models typically cover the range of common automation task requirements.