Number of cores per multi-core processor die, as well as variation between the maximum operating frequency of individual cores, is rapidly increasing. This makes performance binning of multi-core processors a non-trivial task. In this paper, we study, for the first time, multi-core binning metrics and strategies to evaluate them efficiently. We discuss two multi-core binning metrics with high correlation to processor throughput for different types of workloads and different process variation scenarios. More importantly, we demonstrate the importance of leveraging variation model data in the binning process to significantly reduce the binning overhead with a negligible loss in binning quality. For example, we demonstrate that the performance binning overhead of a 64-core processor can be decreased by 51% and 36% using the proposed variation-aware core clustering and curve fitting strategies respectively. Experiments were performed using a manufacturing variation model based on real 65 nm silicon data.