In order to find out the optimum combination of the evaluation parameters for the selection of the best drug near infrared (NIR) universal quantitative model during model optimization, 13 common evaluation parameters of NIR quantitative models were collected and arranged from commercial chemometrics software or references based on the requirements of validation of quantitative analytical procedures of ICH (International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use). Then all these parameters of 92 drug NIR universal quantitative models were calculated and analyzed. By studying the correlation of these parameters, the optimum combination of evaluation parameters for drug NIR universal quantitative models was determined. And the value range of these parameters in the optimum combination was also obtained. Root mean square error of cross-validation(RMSECV)/root mean square error of prediction (RMSEP), average relative deviation (ARD) and ratio of (standard error of) prediction (validation) to (standard) deviation (RPD) were used as the key parameters to evaluate the model accuracy. Most of RMSECV/RMSEP was within 3%, and the value of RMSECV was roughly equivalent to the average absolute deviation of the corresponding model. Most of RPD was more than 2. The value of ARD was related to the type of universal models (such as the drug preparation and packing) and the content range which the test sample belonged to. Determination coefficient (R2) was used as the key parameter to evaluate the model linearity and most of its values were from 80% to 100%. The ratio of RMSEP to RMSECV was selected as the key evaluation parameter of model robustness and its value was usually within 1.5. The standard deviation of repeated measurement data was chosen to evaluate model precision. And it was an important parameter for standardizing operation of NIR instruments and studying the feasibility of model transfer in different instruments. However, the parameter for NIR universal quantitative models received much less attention in previous studies and it was difficult to give a value range for this parameter at present. All the results can not only provide evidence for evaluation of drug NIR universal quantitative models for the model builders or users, but also supply basic data to establish and improve the parameter evaluation system of drug NIR universal quantitative models. Near infrared spectroscopy; Universal quantitative models; Model optimization; Evaluation parameters; Accuracy; Linearity; Precision; Robustness