IRT models' advantages can only be realized when the model fits the data set of interest. Therefore, this study aimed to investigate which IRT model will provide the best fit to the data obtained from OZDEBYR OSS 2004 D-II Exam Science Test. In goodness-of-fit analysis, first the model assumptions and then the expected model features were checked. In the model assumption part unidimensionality, local independence, equal discrimination indices, minimal guessing, and non-speeded test administration was investigated. In the expected model features part the invariance of ability parameter estimates and invariance of item parameter estimates were analyzed. In addition, item characteristics curves (ICC) and item information functions (IIF) were analyzed. The results suggested that the most appropriate model data fit was achieved by two parameter logistic model. (Contains 11 tables.)