How do I reduce LLM costs by sending simple prompts to cheaper models?

A practical way to cut LLM spend is to route easy requests to cheap models and reserve expensive models for hard ones. This is usually called tiered routing or a model cascade.

Basic idea

1. Classify the request as simple vs. complex.
2. Send simple tasks to a low-cost model.
3. Escalate only when needed to a stronger model.

Good candidates for cheap models

Use smaller/cheaper models for tasks like:

• Summarization of short text
• Classification / tagging
• Extraction of structured fields
• Rewriting / grammar cleanup
• Basic Q&A over provided context
• Drafting boilerplate responses

Keep expensive models for

• Long multi-step reasoning
• Ambiguous user intent
• Code generation/debugging
• High-stakes outputs
• Tasks that need deep context synthesis

Common routing strategies

1. Rule-based routing

Simple heuristics:

• If prompt length < X and contains keywords like “summarize”, “extract”, “classify” → cheap model
• If it asks to “analyze”, “compare tradeoffs”, “debug”, “design” → stronger model

This is easy to implement and cheap, but brittle.

2. LLM router

Use a small model to decide:

• “Can a cheap model handle this?”
• “Should this be escalated?”

Output could be a label like:

• cheap
• expensive
• needs_clarification

3. Confidence-based escalation

Ask the cheap model to respond with a confidence score. If confidence is low, retry with a better model.

4. Two-pass approach

• Pass 1: cheap model generates draft
• Pass 2: expensive model reviews only if necessary

This is useful when you want quality control without paying top-tier costs for every request.

Example routing logic

def route_prompt(prompt: str) -> str:
    p = prompt.lower()

    if any(k in p for k in ["summarize", "extract", "classify", "rewrite"]):
        return "cheap"

    if any(k in p for k in ["debug", "design", "tradeoff", "analyze", "prove", "optimize"]):
        return "expensive"

    if len(prompt) < 500:
        return "cheap"

    return "expensive"

Best practices

• Measure accuracy and fallback rates before fully rolling out.
• Log routing decisions so you can tune the rules.
• Use prompt templates to make simple tasks more predictable.
• Limit context sent to cheap models; only include what they need.
• Cache repeated requests where possible.
• Set escalation triggers when output is malformed, incomplete, or low confidence.

A strong pattern: cascade with validation

1. Cheap model produces an answer.
2. Lightweight validator checks structure or quality.
3. If it fails, send to expensive model.

This often gives the best cost/quality tradeoff.

Practical example

For customer support:

• Billing address extraction → cheap model
• Refund policy interpretation → expensive model
• Short FAQ answer → cheap model
• Complaint with nuanced legal/contract implications → expensive model

Important caveat

Don’t route purely by prompt length. A short prompt can still be hard. It’s better to combine:

• task type
• complexity indicators
• confidence
• fallback checks

If you want, I can also give you:

1. a routing architecture diagram,
2. a Python implementation, or
3. a decision table for which tasks go to which model.