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.
The Economics of Model Selection
| Model | Input Cost (per 1M tokens) | Best For |
|---|---|---|
| Llama 3.1 8B | $0.06 | Classification, simple Q&A, extraction |
| GPT-4o Mini | $0.15 | Code gen, summarization, analysis |
| GPT-4o | $5.00 | Complex reasoning, nuanced generation |
| Claude 3.5 Sonnet | $3.00 | Long-form writing, complex tasks |
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:
const response = await fetch("https://neuralrouting.io/v1/dispatch", {
method: "POST",
headers: { "X-API-KEY": "nr_live_..." },
body: JSON.stringify({
messages: [{ role: "user", content: prompt }],
routing_mode: "cost" // auto | cost | speed | quality
})
});
Real-World Results
Teams using intelligent LLM routing consistently report:
- 71–89% reduction in monthly AI API costs
- <200ms added latency from routing overhead (offset by cache hits)
- No measurable quality degradation on routed task types
The semantic cache layer compounds these gains: cached responses cost 50x less than live inference and return in under 10ms.