Seismic vulnerability assessment of urban centers is a challenging issue that needs to be faced accurately for the earthquake risk of large territorial areas. The selection of suitable methods is a crucial aspect that must be treated according to different evaluation processes, depending on the size of the problem and on the available calculation capacities. A possible strategy consists in analyzing large stocks of buildings, so to include in the analyses all those structural parameters that characterize their response and to involve the variability of the considered features. This would require a high computational effort that should be addressed to the investigation of the response of a large number of models. For this reason, simplified procedures based on engineeristic judgements, are commonly considered a viable way to be undertaken in order to predict damage scenarios. Alternatively, the attention could be focused on a limited number of buildings that are judged to be representative of the whole stock. In this case, more sophisticated analyses could be carried out and the obtained results could be extended to the whole urban center. Based on this premise, this paper presents the results obtained through the application of two different seismic vulnerability methodologies on the historic center of Campotosto, in Italy, which was hit by the last 2016 Central Italy earthquake. The first is an empirical method, applied considering a large stock of 130 buildings, which was calibrated by the authors after the 2009 L'Aquila earthquake for historical centers that are similar to the one studied in this paper. The latter, is a method based on analytical formulations dealt with by the Vulnus software, developed at the University of Padua in Italy, which was used for evaluating the seismic vulnerability of an aggregate building, which has been considered representative of the historic center. The final aim is to compare, also in the light of the damage provoked by the 2016 earthquake, the observed post-seismic scenarios, expressed in terms of fragility curves, derived from the two applied methodologies, in order to prove their reliability and to stress the possible issues related to their implementation at different scales.