LLM Routing Explained: How Smart Model Selection Saves 85% on AI Costs
LLM routing automatically selects the cheapest model capable of handling each prompt. Here's how it works, why it matters, and how to implement it.
NR
NeuralRouting Team
April 10, 2026
What Is LLM Routing?
LLM routing is the practice of analyzing each incoming prompt and dispatching it to the most cost-effective model that can produce a satisfactory response. Instead of sending every request to GPT-4, a router classifies the task and selects from a tiered pool of models.
The core insight: not all prompts are equal. A customer asking "what are your business hours?" doesn't need the same model as a developer asking for a complex code refactor.
At 100,000 requests/month, routing 70% to economy models saves $3,400–$4,800/month compared to always using GPT-4o.
How a Router Classifies Prompts
A well-designed router evaluates several dimensions in real time:
1. Task Type Detection
Using a lightweight intent classifier, the router identifies the task category: summarization, coding, reasoning, creative, Q&A, extraction. Each category maps to a minimum required capability tier.
2. Complexity Scoring
A 0–10 complexity score is derived from token density, question structure, and semantic complexity indicators. High complexity scores route to premium models regardless of task type.
3. Confidence Thresholds
The router maintains a confidence matrix that tracks historical quality scores per model/task combination. If an economy model has a poor track record on a specific task type, the router escalates automatically.
Implementing LLM Routing
Building a router from scratch requires:
A classification model (adds latency and cost)
A model pool with failover logic
Quality monitoring to detect regressions
A feedback loop to improve routing decisions over time
This is substantial infrastructure. NeuralRouting provides all of this as a managed proxy: