Modeling of the claim frequency is crucial from many respects in the issues of non-life insurance such ratemaking, credibility theory, claim reserving, risk theory, risk classification and bonus-malus system. For analysing claims in non-life insurance the most used models are generalized linear models, depending on the distribution of claims. The distribution of the claim frequency is generally assumed Poisson, however insurance claim data contains zero counts which effects the statistical estimations. In the presence of excess zero, there are more appropriate distributions for the claim frequency such as zero-inflated and hurdle models instead of a standard Poisson distribution. In this study, using a real annual comprehensive insurance data, the zero-inflated claim frequency is modeled via several models with and without consideration of zero-inflation. The underlying models are compared using information criteria and Vuong test. Parameter estimations are carried out using the maximum likelihood.