What is LLM Routing (Model Routing)?
TL;DR
Dynamically dispatching each query to the right LLM based on content and difficulty — the cost/quality balancing technique that became standard in 2026 production stacks.
LLM Routing (Model Routing): Definition & Explanation
LLM Routing dispatches each query to the best-fit model based on content, difficulty, and required latency. By 2026 it's a standard pattern in production LLM apps for cost reduction, latency optimization, and quality preservation. Common architectures: (1) Cascade Routing (cheap model first, escalate on failure), (2) Classifier-based Routing (a trained dispatcher chooses), (3) Embedding-based Routing (similarity to historical queries), and (4) MoE-style learned routers. Tooling: OpenRouter, Martian, Not Diamond, Portkey, LiteLLM, Helicone. A common operating pattern routes 90% of traffic to Haiku/GPT-5 mini/Gemini Flash, escalating only the hardest 10% to Opus/GPT-5 Pro/Gemini Ultra — and cuts total spend 5-10x. RouteLLM (LMSys, 2024) demonstrated GPT-4-quality output at 85% lower cost. Risks: weak routers degrade quality; output formats differ across providers; multi-vendor ops add complexity. The frontier of AI engineering in 2026 has shifted from 'tune one model' to 'design the model ensemble' — LLM routing is now a core LLMOps competency.