Measuring credit risk and estimating the likelihood of companies failing is one of the most important challenges in the credit sector. Structural and non-structural models are the two main frameworks for estimating default risk and credit risk. However, each of these models has its own strengths and weaknesses. It seems that combining these two frameworks and providing a hybrid model can provide a more accurate prediction of companies' default risk. In the present study, a hybrid model has been used to measure the default risk of listed and over-the-counter companies in the period between 2007-2017 when they have been transferred to the basic market based on the Iranian capital market laws. First, the Merton model (structural models) was used to measure the risk of default of these companies, and then the results of this model were compared with the Z-Altman model (from non-structural models). Then, by regression analysis of different financial ratios, significant variables were identified and Morton's and Z-Altman models were statistically and comparatively compared. The findings show that the hybrid model offers a more accurate prediction of the risk of default than structural and non-structural models. By entering the results of each of these two models into the hybrid model, the statistical power of the hybrid model increases. Therefore, using a combined model will help banks and credit institutions to provide resources to healthier companies with less risk.