AI Model Routing: How to Cut Costs Without Losing Quality
How to route AI requests to cheaper models intelligently — the technique used by companies spending millions on AI to stay profitable.
This page is periodically reviewed to reflect current pricing and plan changes.
What Is Model Routing?
Model routing means sending different AI requests to different models based on their complexity and requirements. Simple tasks go to cheap, fast models. Complex tasks go to expensive, capable ones.
Done well, routing cuts AI costs by 60-80% with no visible quality degradation to end users.
Rule-Based Routing
The simplest approach: classify requests by type before sending.
Examples: - Short Q&A under 100 tokens → GPT-4o mini ($0.0001/request) - Code generation → Claude 3.5 Haiku or Sonnet - Long document analysis → Gemini 1.5 Pro (for its 2M context) - Classification tasks → Gemini Flash (cheapest capable model) - Complex reasoning → GPT-4o or Claude 3.5 Sonnet only
Implement as a switch statement in your API layer. No ML required.
LLM-Based Routing
Use a cheap, fast model to classify whether a request needs a premium model. The router itself becomes the optimization:
1. Send request to GPT-4o mini with a classification prompt 2. If it returns 'complex': route to GPT-4o (costs $0.015 more) 3. If it returns 'simple': answer in mini (saves $0.015)
The classification overhead is tiny. At scale, this pays for itself many times over.
DeepSeek V3 — perfect cheap-tier router target
Most routing strategies send 70–80% of traffic to a cheap workhorse model. DeepSeek V3 costs ~10–20× less than GPT-4o while handling routine prompts well.
Fallback Routing
Start cheap, escalate on failure:
1. Send request to Tier 1 model (cheap) 2. Evaluate response quality against your criteria 3. If quality threshold not met → retry with Tier 2 model
This works well when you have evaluatable success criteria (code that compiles, JSON that parses, classification with confidence score).
Tools for Routing
Several frameworks handle routing out of the box:
- LiteLLM: Unified API wrapper with fallbacks and load balancing
- RouteLLM: Open-source router from Berkeley, optimized for cost-quality trade-offs
- Martian: Managed routing service that learns from your quality feedback
- OpenRouter: Marketplace with built-in routing capabilities
For most applications, LiteLLM or a simple custom router is sufficient.
Key Takeaways
- →Model routing routes different task types to different models based on complexity
- →Rule-based routing is the simplest starting point — no ML required
- →Typical savings: 60-80% with properly tuned routing
- →Start with a two-tier system: cheap default, expensive escalation
- →LiteLLM and RouteLLM are the most popular open-source tools for this
Editorial context
Who is this for?
Developers, startups, and teams who want to reduce their AI API or subscription costs without sacrificing quality.
When NOT to use this
Users who need real-time data, image generation, or proprietary enterprise integrations may need more specialised tools.
Pricing insights
AI pricing varies widely — some models charge per token while others use flat subscriptions. Token-based APIs are usually cheaper for moderate usage, while subscriptions suit power users with high and consistent volume.
Alternatives to consider
Consider DeepSeek V3 for cost-effective coding and writing, Gemini Flash for fast tasks, or Claude Haiku for lightweight structured work. Use the calculator to compare your specific usage.
Final verdict
The cheapest AI tool is the one that fits your exact workload. Use the cost calculator and decision engine on this site to find your optimal stack — most users can cut AI spend by 50% or more.