Comparison
Inference vs Model Router
Inference and Model Router are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
When you would reach for Model Router
Cost-sensitive applications with diverse query difficulty. Skip for narrow, uniformly hard workloads.
A support bot routing FAQ-style queries to Haiku ($0.25/Mtok) and complex multi-step ones to Sonnet ($3/Mtok); avg cost drops 70%.
Frequently asked
What is the difference between Inference and Model Router?
Inference: Inference is what happens when you actually run a trained model on new input. For LLMs that means generating tokens one at a time, with sampling and a KV cache. Model Router: A model router picks the cheapest model that's likely to handle a given request well — based on a small classifier, embedding similarity, or rule-based filters — so you don't pay frontier prices for trivial queries.
When should I use Inference vs Model Router?
Inference is the right concept when you are focused on inference. Cost-sensitive applications with diverse query difficulty. Skip for narrow, uniformly hard workloads.
Are Inference and Model Router the same thing?
No. Inference is inference; Model Router is infrastructure. They are related but address different parts of the AI stack.