Model Routing Is the Quiet AI Advantage: Why One LLM Is No Longer Enough

The end of the one-model strategy For the first wave of generative AI products, the default question was simple: which large language model should we use? Teams compared benchmark scores, context windows, pricing pages, and latency charts, then made a single platform decision. That approach worked when AI features were narrow and experimental. It is starting to break down. Modern AI applications are no longer just chat boxes. They summarize documents, classify support tickets, write SQL, extract structured data, call tools, search knowledge bases, generate code, and coordinate multi-step agent workflows. Each of those tasks has a different tolerance for cost, speed, reasoning depth, creativity, and accuracy. A frontier model may be excellent for strategic reasoning but wasteful for short classification. A small open model may be perfect for extraction but risky for legal drafting. A fast multimodal model may be ideal for scanning screenshots but unnecessary for plain text. This is why model routing is becoming one of the most important layers in the AI stack. What is model routing? Model routing is the practice of sending each AI request to the most appropriate model based on