How Cursor Router chooses the right model for the task
Cursor Router learns task complexity and model fit from production behavior, showing why agent routing should include correction signals, cache costs, and per-task performance.
Cursor routes each turn in two stages: Compass estimates complexity, then a production-derived taxonomy selects among eligible frontier models. **Auto Intelligence costs 68% less than Fable**, while **Auto Balance costs 41% less than Opus 4.8** and reports higher satisfaction.
Builders implementing model routing should learn from real task outcomes, including corrections and task progression, rather than rely only on benchmarks. Include token use, cache misses, model-switching costs, confidence thresholds, and an explicit per-turn budget.
Cursor routes each turn in two stages: Compass estimates complexity, then a production-derived taxonomy selects among eligible frontier models. **Auto Intelligence costs 68% less than Fable**, while **Auto Balance costs 41% less than Opus 4.8** and reports higher satisfaction. Builders implementing model routing should learn from real task outcomes, including corrections and task progression, rather than rely only on benchmarks. Include token use, cache misses, model-switching costs, confidence thresholds, and an explicit per-turn budget. Cursor's satisfaction measure is inferred from subsequent user behavior, so it is a proxy rather than a direct quality score. The reported results come from Cursor traffic and may not transfer to another workload, user population, or pricing mix.
This turns model routing into a per-turn, budget-aware learning problem grounded in production behavior, not a static benchmark lookup. It contrasts directly with task-consistent routing: Cursor accepts switching costs to match each turn, while acknowledging that inferred satisfaction and workload-specific economics limit how broadly its reported savings can be applied.