During the past decade, model order reduction (MOR) has become key enabler for the efficient simulation of large circuit models. MOR techniques based on balanced truncation (BT) offer very good error estimates and can provide compact models with any desired accuracy over the whole range of frequencies (from dc to infinity). However, in most applications the circuit is only intended to operate at specific frequency windows, which means that the reduced-order model can become unnecessarily large to achieve approximation over all frequencies. In this article, we present a frequency-limited approach which, combined with an efficient low-rank sparse implementation of the extended Krylov subspace (EKS) method, can handle large input models and provably leads to reduced-order models that are either smaller or exhibit better accuracy than full-frequency BT.