Cursor Router Cuts Costs While Boosting Satisfaction
Cursor announced improvements to its Cursor Router, a system that selects AI models for coding tasks. Auto Intelligence now achieves above Fable-level user satisfaction at 68% lower cost, a further 18% reduction since launch, while Auto Balance outperforms Opus 4.8 at 41% lower cost, with an additional 8% cost reduction and 3% satisfaction increase. The router uses production traffic data rather than benchmarks to make model choices.
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On July 22, we launched Cursor Router with two new configurations, Auto Intelligence and Auto Balance. Since then, we have continued improving both modes as new models have arrived and our routing system has learned from more production traffic. Today, Auto Intelligence delivers above Fable-level user satisfaction at 68% lower cost, a further 18% reduction since its launch. Auto Balance outperforms Opus 4.8 at 41% lower cost, a further 8% reduction over the same period, while further increasing user satisfaction by 3%. We're working towards a Cursor Router that improves alongside the model frontier. This post explains how the current system works. ## A data-driven approach to routing Cursor Router is built around the idea that model selection should be learned from how models perform on real developer work, rather than inferred from benchmark scores. The router makes each decision using signals from the current turn and recent conversation state. These include structured features such