Training student models on synthetic data generated by strong teacher models is a promising approach to distilling the capabilities of teachers. However, existing studies reveal that stronger models are not always optimal teachers, suggesting a mismatch between the teacher’s output and the student’s learning ability. To address this issue, we propose PerSyn (Personalized data Synthesis), a novel and efficient approach that customizes synthetic data to align with the learning capabilities of the student model. Specifically, our PerSyn method routes each prompt to its optimal teacher via a query-level router that jointly considers the student models’ learnability and teacher models’ response quality. It successfully transfers the synthesis paradigm from the conventional "Generate then Select" to a more efficient manner, i.e., "Route then Generate", eliminating the need for all teacher models to generate parallel responses across the entire prompt set. Extensive experiments across different model families and scales demonstrate that PerSyn consistently outperforms all baselines on six benchmarks, including instruct tuning and math reasoning settings. Further analysis verifies the effectiveness of PerSyn and offers extra insights to propel future research. Our code is available at https://anonymous.4open.science/r/PerSyn-8D85.