How does speculative routing improve cost-efficiency in multi-model ChatGPT systems?

Speculative routing uses a small model to predict whether a larger model is needed, reducing unnecessary expensive inference calls.

In multi-model ChatGPT systems, different model sizes are available (small, medium, large). Speculative routing uses a lightweight model to predict output quality or difficulty before invoking a larger model.

If the small model is confident, its response is returned directly. If uncertainty is high, the request is escalated to a larger model. This reduces cost while maintaining quality.

The system relies on confidence estimation, entropy scoring, and historical performance signals to decide routing paths.