Use Smaller AI Models for Classification and Routing Tasks

Not every AI task needs the most powerful (and expensive) model. Use cheaper, faster models for classification, routing, extraction, and yes/no decisions. Reserve the full-sized models for tasks that genuinely need them -- long-form generation, complex reasoning, or nuanced analysis.

Real-world example: A support ticket workflow classifies incoming emails into categories (billing, technical, feature request, spam), then routes them to the right team. Classification is a simple task that a small model handles perfectly.

Task-to-model mapping:

Classification/routing:
  Model: gpt-4o-mini or claude-3-5-haiku
  Cost: ~$0.0001 per classification
  Example: "Is this email about billing, technical support, or a feature request?"

Data extraction:
  Model: gpt-4o-mini or claude-3-5-haiku
  Cost: ~$0.0003 per extraction
  Example: "Extract the customer name, order ID, and issue from this email."

Summarization (short):
  Model: gpt-4o-mini or claude-3-5-haiku
  Cost: ~$0.0005 per summary
  Example: "Summarize this support ticket in one sentence."

Long-form generation:
  Model: gpt-4o or claude-sonnet-4
  Cost: ~$0.01-0.05 per generation
  Example: "Write a detailed response to this customer's technical issue."

Complex reasoning:
  Model: gpt-4o or claude-sonnet-4
  Cost: ~$0.02-0.10 per query
  Example: "Analyze this contract clause and identify potential risks."
```text
In n8n, configure the OpenAI or Anthropic node to use the appropriate model per task:

```json
// Classification node (cheap model)
{
  "resource": "chat",
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "Classify the following support email into exactly one category: billing, technical, feature_request, spam. Respond with only the category name."
    },
    {
      "role": "user",
      "content": "={{ $json.emailBody }}"
    }
  ],
  "temperature": 0,
  "maxTokens": 10
}
```text
```json
// Generation node (powerful model, only called when needed)
{
  "resource": "chat",
  "model": "gpt-4o",
  "messages": [
    {
      "role": "system",
      "content": "Write a helpful, empathetic response to this customer support ticket. Include specific steps to resolve their issue."
    },
    {
      "role": "user",
      "content": "={{ $json.emailBody }}"
    }
  ],
  "temperature": 0.7,
  "maxTokens": 500
}
```text
```yaml
Cost comparison for 1000 support tickets/day:
  All gpt-4o:      1000 x $0.03 = $30/day = $900/month
  Smart routing:    1000 x $0.0001 (classify) + 200 x $0.03 (generate) = $6.10/day = $183/month
  Savings: ~80%
```text
The key insight: most tickets only need classification and a template response. Only the complex ones need a powerful model to generate a custom reply.

**Related:** [Use Structured Output (JSON Mode) for Parseable Responses](../ai-and-llm-integration/01-use-structured-output-json-mode-for-parseable-responses.md) | [Configure Payload Size and Binary Data Mode for Large Files](../performance-and-large-files/01-configure-payload-size-and-binary-data-mode-for-large-files.md)