I’ve been thinking about this aspect of having 400+ AI models available through a single subscription. On the surface, it’s liberating—you have every major model available. But practically, how do you actually make the choice?
Let me be specific: say you’re building an automation that extracts data from a website, needs to translate some text, and then analyzes the sentiment of user reviews. Each of those steps could potentially use different models or even different versions of the same model.
Do you pick OpenAI for everything because it’s reliable and you know it? Do you experiment with Claude or other models to see if they’re cheaper or faster for specific tasks? Are there cases where you’d actually want to use different models in parallel and compare results?
Also, what’s your decision-making process? Do you benchmark them, or is it more just “use what I know works”? And has anyone actually seen a meaningful difference in results or speed between different models for the same task, or does it mostly come down to preference and cost?
I’m trying to figure out if the abundance of models is genuinely useful or if it’s mostly marketing value that most people don’t actually leverage.
The abundance is useful if you think strategically. I don’t pick the same model for everything. Different models excel at different tasks.
For data extraction, I often use faster, lighter models—they’re cheaper and good enough. For complex analysis or sentiment detection, I lean toward stronger models like Claude. For specialized tasks like translation or OCR, I check if there’s a dedicated model instead of forcing a general LLM.
My process: start with what I know, measure results and cost, then experiment. Sometimes a cheaper model does 95% as well at a fraction of the cost. Sometimes paying for a stronger model prevents downstream errors that would be expensive to fix.
I’ve definitely seen meaningful differences. Text extraction can work fine with a smaller model, but nuanced sentiment analysis often needs something with better reasoning. Real cost savings come from matching the model to the task complexity, not using the most expensive option everywhere.
The decision-making looks like: what’s the task complexity, what’s my tolerance for error, and what’s the cost difference? If it’s low-stakes, I go cheaper. If accuracy really matters, I spend.
I’ve experimented with this pretty extensively. The practical approach: pick a default model you trust, baseline your workflows against it, then selectively swap in alternatives for expensive or complex steps.
For simple classification tasks—like categorizing extracted text—a smaller model works fine and costs less. For understanding context or making complex decisions, the difference between models is noticeable. Claude handles ambiguity better than some alternatives, but it costs more.
I don’t run parallel comparisons routinely. That gets expensive quickly. Instead, I’ll occasionally test a new model against known good examples to see if it’s worth switching. If the results are comparable, I swap it in permanently. If they’re worse, I stick with the original.
The key insight: you’re not using 400 models because every step needs a different one. You’re using a small subset strategically—usually 3-5 models that work well for your typical tasks.
After building several workflows, I’ve found that model selection matters most for interpretation tasks rather than simple execution. Data extraction works similarly across most models, but understanding context or making decisions based on extracted data shows more variation. I typically use cost-effective models for straightforward tasks and reserve more capable models for complex reasoning. Benchmarking before large-scale deployment helped identify which models provided best value for specific step types. The abundance of options is valuable when approached methodically rather than treating all models as interchangeable.
Strategic model selection based on task requirements yields measurable cost savings without sacrificing accuracy. Simple extraction tasks perform adequately with economical models, while complex analysis benefits from more capable alternatives. I evaluate models through controlled testing against known datasets to identify performance-to-cost ratios. The real value emerges from understanding which model characteristics align with specific task demands rather than optimizing for a single universal choice.