What is model routing in AI?
Model routing sends each request to the model or tool path best suited for speed, cost, accuracy, or reasoning depth.
The short version
Model routing is the decision layer that chooses which AI model handles a task. A simple question may go to a fast cheaper model, while coding, legal review, or deep research may go to a stronger model or multiple models.
Why routing matters
No single model is best at everything. Some are faster, cheaper, better at reasoning, better at long context, better at writing, or better at tool use. Routing lets a product balance quality and cost.
Common routing signals
Routers may look at topic, complexity, user tier, safety risk, need for live data, file size, language, latency target, or whether the answer needs citations.
Risks of bad routing
Bad routing can send hard questions to weak models, overpay for simple tasks, skip live data when it matters, or create inconsistent answers across the product.
What good routing looks like
Good routing is measured. Teams compare outcomes, latency, cost, user feedback, and failure types, then adjust the router instead of guessing.
Related questions to ask AskClash
- Should I use one AI model or many?
- How do AI apps reduce inference cost?
- What is a multi-model AI system?