Multi-Model AI Pipelines: Using the Right Model for Every Job

Most AI integrations talk to one model. The production ones run fleets. Here's what I've learned building multi-model pipelines — where to use expensive models, where cheap ones outperform them, and how to wire it together without losing your mind. The Mental Model Shift Stop thinking "what's the best model" and start thinking "what's the right model for this job." A frontier model like Claude Opus or GPT-4o is extraordinary at reasoning, nuanced writing, and complex decisions. It's also 50-100x more expensive per token than smaller models. Running everything through it is like hiring a senior engineer to do data entry. The flip side: cheap models have gotten genuinely good at well-defined, structured tasks. Classification, extraction, templated generation, routing decisions — Haiku and GPT-4o-mini handle these reliably at a fraction of the cost. Multi-model pipelines exploit this gap intentionally. The Classic Split Use expensive models for: Complex reasoning and analysis Nuanced judg