Building a Cost-Aware LLM Router: Automatically Pick the Cheapest Model for Each Task

Not every task needs your most powerful model. A cost-aware routing layer can cut API spend significantly without sacrificing output quality where it matters.

The core idea: score each incoming task by complexity, route low-complexity tasks to Flash-tier models, high-complexity ones to Max/Pro-tier. Using RouteAI pricing as a reference: Qwen3.5 Flash at $0.06/M vs Qwen3.7 Max at $1.50/M — same token volume, 25x cost difference.

Implementation: maintain a model_router function that takes a prompt and task type, returns a model ID, then pass that ID to a single RouteAI OpenAI-compatible client. Routing logic stays completely decoupled from API call logic. Swapping models requires zero interface changes.

This pattern works especially well when you're running many tasks in parallel — document processing pipelines, batch summarization, multi-step agents where different steps have different complexity profiles.