Infiltration process is one of the most important components of the hydrologic cycle. The ability to quantify infiltration is of great importance in watershed management. Prediction of flooding, erosion and pollutant transport all depend on the rate of runoff which is directly affected, by the rate of infiltration. Quantification of infiltration is also necessary to determine the availability of water for crop growth and to estimate the amount of additional water needed for irrigation. Thus, an accurate model is required to estimate infiltration process. In this study, the ability of seven different infiltration models (i.e., Philip (PH), Soil Conservation Service (SCS), Kostiakov (KO), Horton (HO), Swartzendruber (SW), Modified Kostiakov (MK) and Revised Modified Kostiakov (RMK) models) to fit infiltration data were evaluated. For this purpose, 95 sets of infiltration data with four-texture classes were utilized. Comparison criteria including Coefficient of Determination (R2), Mean Root Mean Square Error (MRMSE), Root Mean Square Error (RMSE) were used to determine the optimum model. The greatest amounts of R2 values were obtained with RMK, MK and Swartzendruber models. The SCS model with two parameters yielded to the lowest R2. According to the results obtained from mean of RMSE (MRMSE) values, the MK model provided the lowest values, indicating that infiltration was well described by this model. The results of ranking models according to two criteria: RMSE, and MRMSE, indicated that based on RMSE the goodness of cumulative infiltration can be estimated by the RMK, MK, Kostiakov, Swartzendruber, Horton, Philip, SCS models, respectively. But according to the MRMSE statistics cumulative infiltration can be estimated by the MK, RMK, Swartzendruber, Philip, Kostiakov, SCS and Horton models, respectively. Based on the results of ranking model the CSC model obtained the lowest ranking between the all of the models.