Multi Model Routing

Overview

This pattern addresses the inefficiency of using a single, high-capability LLM for every task. By implementing multi-model routing, you can intelligently route incoming requests to different tiers of AI models—Fast, Standard, or Deep—based on the complexity and scope of the required work.

The core principle is cost-aware scaling: use the cheapest model that meets the functional requirements, reserving expensive, powerful models only for truly complex reasoning tasks.

Capabilities

• Tiered Task Assignment: Categorizes tasks into three distinct difficulty levels (Fast, Standard, Deep).
• Cost Efficiency: Minimizes API expenditure by defaulting to lighter models for simple operations.
• Contextual Routing Logic: Uses heuristics based on task mechanics, context size, and required reasoning depth to select the optimal model tier.

Example Use Cases

1. Simple Refactoring: For tasks like renaming a variable or fixing a typo (mechanical), route to a 'Fast' model for instant feedback.
2. Feature Implementation: When building out new logic that requires understanding business rules and writing associated tests, route to a 'Standard' model.
3. Architecture Design: For planning major system migrations or analyzing complex race conditions across multiple interconnected services, utilize the 'Deep' model tier for maximum reasoning power.