LLM Model Routing: How to Save 50–70% by Sending Requests to the Right Model
Route simple requests to cheap models and complex ones to frontier models. Practical guide to rule-based, LLM-based, and semantic routing — with real cost calculations.
Most teams use one LLM for everything — their best model, applied to every request regardless of complexity. This is the most expensive possible approach. Model routing sends each request to the cheapest model capable of handling it. Applied correctly, routing typically reduces total API costs by 50–75% with no measurable quality drop on overall output.
Why Routing Works: The Complexity Distribution
In any production LLM application, the distribution of request complexity is heavily skewed. Analysis of real-world chatbot and agent workloads consistently shows:
If you route 50% of traffic to a model that's 10x cheaper, your blended cost drops by ~45%. The math compounds further when you add a third tier.
Three-Tier Routing Example: Anthropic Stack
Blended cost at this split (1,000 input + 400 output tokens avg):
How to Build a Router
Option 1: Rule-Based Routing (Fastest)
Define routing rules based on measurable request properties. No ML required — zero latency overhead:
Option 2: LLM-Based Routing
Use a cheap fast model (Haiku, GPT-4.1 Nano, Grok 3 Mini) to classify each request and select the appropriate tier. The classifier call adds ~100ms latency and costs fractions of a cent — worth it if it saves expensive frontier calls.
Reply with exactly one word: SIMPLE, MEDIUM, or COMPLEX.
SIMPLE: greeting, FAQ, short factual, yes/no.
COMPLEX: code, agents, analysis, multi-step.
Option 3: Semantic Routing with Embeddings
Pre-define topic clusters (e.g., "billing queries", "technical support", "creative writing") with example embeddings. At runtime, embed the user request and find the nearest cluster. Route based on cluster-to-model mappings. Highest accuracy, but requires setup and a vector store.
Routing for OpenAI Stack
Common Routing Mistakes to Avoid
Bottom Line
Model routing is the highest-leverage cost optimization after prompt caching. Start simple: add a rule-based router that sends short, simple requests (<500 tokens, no code) to your cheapest model. Measure quality at each tier. Expand routing rules based on what you observe. A well-tuned three-tier router consistently achieves 50–70% cost reduction with no perceptible quality drop for end users.
Use our cost routing calculator to model your expected savings, or compare model pricing at the compare tool →