One thing that caught my attention about the current automation landscape is the idea of having access to 400+ AI models through a single subscription instead of managing individual API keys for OpenAI, Claude, Anthropic, and all the others.
But this raises a practical question: if you have all these models available, how do you decide which one to use for different parts of your automation workflow?
I get it intellectually—different models are better at different things. Claude might be better for writing, GPT-4 for reasoning, something faster for simple classification. But in practice, how do you actually make that decision when you’re building a workflow?
Do you:
- Just pick one model and stick with it for everything?
- Run tests to benchmark which model performs best for your specific task?
- Use the same model for similar tasks across different workflows?
- Have it be something you configure once and never think about again?
I’m trying to understand whether this “400+ models” thing is actually useful in practice or if it’s mostly marketing hype. When you’re automating a real business process that involves data extraction, transformation, and analysis, do you actually benefit from model variety, or does picking one solid model cover 95% of your needs?