WiFi is one of the most important communication modes at present, and indoor localization systems based on WiFi signals are most promising for widespread deployment and application in daily life. The latest research shows that such a system can achieve submeter-level localization accuracy when it utilizes the channel state information (CSI) obtained during WiFi communication for target localization. However, the accuracy of localization in experimental scenarios depends on many factors, such as the location of the test points, the layout of the WiFi devices, and that of the antennas. Moreover, the WiFi localization systems deployed often fail to provide the desired accuracy since performance prediction methods for WiFi CSI localization are still unavailable. For the above reasons, this study develops a performance prediction model for WiFi CSI localization that applies to diverse scenarios. Specifically, the study defines the error infinitesimal function between a pair of antennas on the basis of the basic physical CSI localization model. The error infinitesimal matrix and the corresponding heat map of localization performance are generated by analyzing the localization space. Then, multi-antenna fusion and multi-device fusion methods are adopted to extend the antenna pairs, thereby constructing a general performance prediction model for CSI localization. Finally, the study proposes integrating the abovementioned heat map with scenario maps to give due consideration to actual scenario maps and ultimately provide a customized performance prediction solution for a given scenario. In addition to the theoretical analysis, this study verifies the effectiveness of the proposed performance prediction model for localization with experimental data in two scenarios. The experimental results show that the actual localization accuracy is consistent with the proposed theoretical model in variation trend, and the model optimizes the localization accuracy by 32%–37%.