How Cursor Router chooses the right model for the task

Cursor Router is a data-driven system that selects among price-efficient and frontier AI models based on real developer traffic, aiming to improve user satisfaction while reducing inference costs. Its two modes, Auto Intelligence and Auto Balance, use Compass, a predictor trained on subsequent user behavior to estimate task complexity, and a taxonomy that classifies tasks, domains, and modifiers to identify models with observed strengths in particular types of work. Simple turns are generally sent to a lower-cost model, while more demanding turns are assigned to eligible frontier models when measured performance gains meet a confidence threshold and fit within each mode’s cost budget. Cursor reports that Auto Intelligence now achieves above Fable-level satisfaction at 68% lower cost, while Auto Balance exceeds Opus 4.8 satisfaction at 41% lower cost, though these results are based on its internal production evaluations. The system is tested through cross-validation, held-out data, and live traffic to account for practical factors such as token use, caching, and model-switching costs, and it is intended to evolve as new models and production data become available.

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