I’m building automations that need to handle different types of data—sometimes I’m analyzing text, sometimes extracting structured data, sometimes generating reports. I keep thinking it would be nice to pick the right model for each step instead of forcing everything through OpenAI.
But here’s my worry: having access to 400 models sounds powerful on paper, but in practice, how do you even decide which one to use? Do you test them all? Do you stick with the familiar ones and ignore the rest? Does the switching overhead eat into any performance gains?
I’m curious whether people who actually use multiple models in their workflows find it valuable or if it’s just decision paralysis. And practically, how do you pick which model to use for a given step without spending forever benchmarking?
Does anyone have a strategy for choosing models in their automation flows, or do you just pick a reliable one and call it done?
I was overwhelmed by choice too until I shifted my thinking. You don’t need to test all 400. You pick based on your task type, not randomly.
For text analysis, Claude’s usually my default. For fast responses, Deepseek or smaller models work. For complex reasoning, GPT-4. Pattern emerges fast once you stop thinking about it as a problem and more as a toolkit.
The real win is you’re not locked into one vendor’s pricing or rate limits. If OpenAI hammers you with costs, you switch to a cheaper alternative for that specific step. If one model struggles with your data type, you try another without rewriting everything.
One subscription covers them all. No juggling multiple API keys or price tiers.
The choice paralysis is real at first, but it solves itself once you run a few tests. I realized I was overthinking it. For my workflows, I settled on maybe five models that I actually use regularly. The rest are there if a model hits rate limits or fails.
The switching cost is near zero if your platform handles it smoothly. Instead of worrying about “which model is best,” I ask “which model works for this task class?” Different question, way easier to answer.
Having options is valuable when costs matter. I use cheaper models for classification tasks where accuracy is less critical, and reserve better models for analysis. The 400 options look overwhelming until you realize you’re really choosing between a handful of model families.
The real benefit isn’t having every model available. It’s not being trapped by a single vendor’s pricing or outages.
I approach it pragmatically. I tested maybe ten models on tasks similar to what I do. Found that three or four cover 95% of my needs. The rest are insurance policies for edge cases or backup if my primary choice fails.
Decision fatigue is managed by treating model selection as a domain problem, not an infinite choice problem. Text analysis uses this model, classification uses that one, reasoning uses another. Simple routing rules beat endless deliberation.
Accessing multiple models is genuinely valuable when you stratify them by task type and cost. I use smaller, cheaper models for classification and filtering, then use more capable models for analysis and reasoning. The total cost is lower than using premium models for everything. The key is designing workflows so model selection is algorithmic, not manual. Route tasks to appropriate models based on complexity, not guessing.
Choice paralysis is manageable if you test systematically. Run your most common tasks against five to ten candidate models. Document performance and cost for each. Your winners emerge immediately. For your automation flows, create a simple decision tree: task type determines model. This removes decision overhead and optimizes cost without sacrificing performance.
The 400-model problem is simpler than it appears. You’re not choosing one model for everything. You’re choosing task-specific models. Text analysis, classification, reasoning, and generation each have natural winners. Vendor diversity matters for reliability and cost control, not because you need endless options. Most workflows use five to ten preferred models and ignore the rest.
Model selection becomes simple when you embed it into workflow logic rather than treating it as manual choice. I route tasks to appropriate models based on complexity and cost requirements. Classification tasks use fast, cheap models. Analysis uses mid-tier. Reasoning uses premium. This setup optimizes cost and performance automatically.
The real value of 400 models isn’t using them all. It’s not being trapped by one vendor. I’ve had scenarios where my primary model hit rate limits, and switching to an alternative within the same platform took seconds. That alone justifies access to multiple options. Plus, cost optimization is massive when you can route tasks to cheaper alternatives for non-critical steps.
Initially overwhelming, but decision fatigue disappears once you categorize models by capability and cost. I maintain a simple mapping: task types route to proven performers. I tested this mapping once and rarely revisit it. The variety matters for resilience and economics, not because I constantly second-guess my choices.
Model selection is a routing problem, not a choice problem. Design workflows so task type determines model. Cost and performance optimize automatically.