Model Routing: How to Cut AI API Costs 40-70% Without Losing Quality

What is model routing?

Model routing is the practice of sending different API requests to different models based on the complexity, priority, or type of each request. Instead of using your most expensive model for everything, you classify incoming requests and route them to the cheapest model that can handle them well.

This is the single most effective strategy for reducing AI API costs. Teams that implement routing typically see 40–70% cost reductions without meaningful quality loss for end users.

Why one-model-fits-all is expensive

Most teams start by calling GPT-4o or Claude Sonnet for every request. But the reality is that many requests are simple: classification, entity extraction, formatting, summarization of short text, or yes/no questions.

These tasks don't need a $10/1M-output-token model. GPT-4o-mini at $0.60/1M output tokens handles them identically — at 94% less cost per token.

How to build a routing layer

A practical routing approach has three tiers:

• Tier 1: Fast and cheap — GPT-4o-mini, Claude Haiku, or Gemini Flash. Use for classification, extraction, simple Q&A, and formatting tasks. These models cost $0.15–$0.80 per 1M input tokens.
• Tier 2: Balanced — GPT-4o, Claude Sonnet, or Gemini Pro. Use for code generation, multi-step reasoning, long-form writing, and complex analysis. $2.50–$5.00 per 1M input tokens.
• Tier 3: Premium — o1, Claude Opus. Use only for tasks that explicitly need advanced reasoning, research, or nuanced creative work. $15+ per 1M input tokens.

Routing strategies

You can route requests using several approaches, from simple to sophisticated:

• Endpoint-based — Different API endpoints use different models. Your chatbot uses Haiku; your code assistant uses Sonnet.
• Keyword-based — Check for complexity indicators in the prompt. If the request involves code, math, or multi-step reasoning, route to Tier 2.
• Classifier-based — Use a cheap model (or a local classifier) to classify the request complexity, then route accordingly.
• Fallback chains — Try Tier 1 first. If the response quality is too low (detected by a quality check), retry with Tier 2.

Measuring the impact

After implementing routing, you need to track two things: cost savings and quality impact. Use MeterFox to monitor per-model spend over time. You should see your expensive model usage drop significantly while total request volume stays constant.

For quality, track user satisfaction or task success rates per model tier. If Tier 1 handles 60% of requests with the same quality score as Tier 2, your routing is working. If quality drops, tighten the routing criteria.

Real-world example

A customer support platform routes 10,000 requests/day. Before routing, everything goes to Claude Sonnet at ~$3/1M tokens. After routing:

• 6,000 requests (FAQ answers, order lookups) → Claude Haiku: ~$0.80/1M tokens
• 3,500 requests (complex troubleshooting) → Claude Sonnet: ~$3/1M tokens
• 500 requests (escalations, edge cases) → Claude Sonnet with longer context: ~$3/1M tokens

Result: 40–50% cost reduction with the same customer satisfaction scores. Track these numbers in your cost dashboard to prove the ROI of your routing layer.