Route AI queries cost‑efficiently with GPT‑4o‑mini, GPT‑4o and confidence scoring

This workflow implements a cost-optimized AI routing system using n8n. It intelligently decides whether a request should be handled by a low-cost model or escalated to a higher-quality model based on response confidence.

The goal is to minimize LLM usage costs while maintaining high answer quality.

A query is first processed by a cheaper model. The response is then evaluated by a confidence-scoring AI agent. If the response quality is insufficient, the workflow automatically escalates the request to a more capable model.

This approach is useful for building scalable AI systems where most queries can be answered cheaply, while complex queries still receive high-quality responses.

How It Works

Webhook Trigger Receives a user query from an external application.

Workflow Configuration Defines parameters such as: confidence threshold cheap model cost expensive model cost

Cheap Model Response The query is first processed using GPT-4o-mini to minimize cost.

Confidence Evaluation An AI agent analyzes the response quality. It evaluates accuracy, completeness, clarity, and relevance.

Structured Output Parsing The evaluator returns structured data including: confidence score explanation escalation recommendation.

Decision Logic If the confidence score is below the configured threshold, the workflow escalates the request.

Expensive Model Escalation The query is reprocessed using GPT-4o for a higher-quality answer.

Cost Calculation Token usage is analyzed to estimate: total cost cost difference between models.

Final Response Formatting The workflow returns: AI response model used confidence score escalation status estimated cost.

Setup Instructions

Create an OpenAI credential in n8n.

Configure the following nodes: Cheap Model (GPT-4o-mini) Expensive Model (GPT-4o) OpenAI Chat Model used by the confidence evaluator agent.

Adjust configuration values in the Workflow Configuration node: confidenceThreshold cheapModelCostPer1kTokens expensiveModelCostPer1kTokens

Deploy the workflow and send requests to the Webhook URL.

Example webhook payload:

{ "query": "Explain how photosynthesis works." }