What is LLM Router?
TL;DR
A layer that picks the right model (GPT-5, Claude 4.7, Gemini 3, Llama 4) per query — typically saving 30-70% in cost while preserving quality. The 2026 default for production LLM apps.
LLM Router: Definition & Explanation
An LLM Router routes each request to the optimal model based on task type, difficulty, latency budget, and cost. Common implementations: (1) Martian Router (cost-aware Smart Routing), (2) NotDiamond (open-source, 30-50% cost reduction in benchmarks), (3) RouteLLM (LMSYS), (4) OpenRouter (100+ models behind one API), (5) Portkey (enterprise gateway), (6) LiteLLM and LangChain Router primitives. How it works: (a) pre-route classification with a small BERT-class model (easy/hard, code/chat, long/short), (b) score candidate models against past performance, price, and latency, (c) route, (d) fallback on failure. Wins: (I) 30-70% cost reduction (cheap model for easy queries, frontier for hard ones), (II) latency optimization, (III) avoid vendor lock-in, (IV) one API for many models. 2026 trends: Anthropic Claude Skills, OpenAI Agents, and Google AgentSpace assume multi-model routing as a primitive; tighter integration with prompt caching and speculative decoding; edge + cloud hybrid routing for privacy. Caveats: routing fragments cache hit rate, adds 50-100ms of routing latency, and complicates debugging.