So I’ve been looking into using multiple AI models in automation workflows. There’s apparently 400+ models available now—GPT versions, Claude variants, Deepseek, and others I’ve never heard of.
My question is practical: when you’re building a workflow that needs AI at multiple steps, how do you actually choose which model to use? Do you pick one and stick with it? Do you try different models at different steps? How do you even evaluate which one is “best” for a specific task?
Right now, accessing different models means managing separate API keys, different rate limits, different pricing. That alone makes trying different models feel like a hassle. I’m wondering if there’s a practical approach to model selection or if most people just pick one and call it a day.
Model selection is actually simpler than it looks once you understand what each model is good at. From my work, I’ve found that you don’t need to test all 400. You probably use three or four models across different tasks.
GPT-4 is solid for complex reasoning and writing tasks. Claude is better for detailed analysis and handling long documents. Smaller models work fine for classification or simple extraction.
The real issue you’re hitting is the API key management nightmare. That’s what stops most people from actually experimenting. If you’re juggling multiple services, switching between them, tracking different usage limits—yeah, that’s a pain.
What changed for me was using a platform that abstracts away that complexity. Instead of managing keys for each service, I can pick the right model for each step within one interface. That made experimentation actually feasible.
The honest answer is that most people overthink model selection. You probably don’t need to use multiple models. Pick one that handles your use cases well and stick with it unless you hit a specific wall.
That said, if you do want to experiment with different models for different parts of your workflow, the overhead of managing multiple API keys and subscriptions is a real drag. You’re maintaining separate accounts, tracking usage across platforms, dealing with different rate limits.
Think about it from a practical standpoint: is the marginal improvement from using a different model worth the operational complexity? Usually not. But if you can access multiple models from one place without that overhead, then experimentation becomes viable.
Model selection depends on your task requirements. Complex reasoning and creative work benefit from larger models. Structured data extraction works with smaller, faster models. The key is matching model capability to task complexity.
Most workflows don’t need many models. Start with one strong general-purpose model and only branch out if you hit performance or capability limitations. The coordination overhead of managing multiple models usually outweighs the benefit unless you’re optimizing for cost or speed at scale.
Most tasks work with 1-2 models. GPT-4 for complex work, Claude for analysis. Pick one that fits your needs, don’t overthink it.
Use GPT-4 for general tasks, Claude for document analysis. Smaller models for classification. Start with one, expand if needed.