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Intelligent Model Routing

2 posts · Jul 2026 – Aug 2026

What this trail covers

Cursor Router is an intelligent model routing system for teams and enterprises that automatically routes requests to the most capable and cost-effective model based on query, context, task complexity, and domain. It is trained on live request data and evaluated through online A/B tests to optimize for user satisfaction and cost savings. The router is cache-aware and designed to improve alongside the model frontier, with modes like Intelligence, Balance, and Cost to adjust the trade-off between p

All Cursor trails
  1. How Cursor Router chooses the right model for the taskThis post details the technical implementation and data-driven approach behind Cursor Router's model selection. It introduces 'Compass,' a complexity predictor trained on user satisfaction signals (task completion vs. agent correction) to estimate turn complexity. The post explains how this complexity score is used to set a threshold for routing requests to either price-efficient or frontier models. It also describes the dataset construction from live traffic, capturing performance (user actions) and cost (API pricing, token usage, cache misses) as key metrics for training and evaluation. The core technical contribution is the system for learning optimal model routing based on real-world developer work, moving beyond benchmark scores.
  2. Introducing Cursor RouterThis post introduces Cursor Router, an intelligent model routing system for teams and enterprises. It classifies requests before a model runs, routing to the best model based on query, context, task complexity, and domain. The system was trained on 600k+ live requests and evaluated across millions of live requests in an online A/B test, optimizing for user satisfaction (AFC) and cost savings. It offers three modes: Intelligence, Balance, and Cost, allowing users to adjust the cost-intelligence trade-off. The router is cache-aware in both training and evaluation. Online A/B tests were used for evaluation, measuring user satisfaction and code keep rate. Early access customers reported 30-50% cost savings with no decrease in quality. Cost per commit analysis showed lower costs for Intelligence and Balance modes compared to direct use of Opus 4.8 or Fable.