The present study examined the performance of the bi-factor multidimensional item response theory (MIRT) model and higher-order (HO) cognitive diagnostic models (CDM) in providing diagnostic information and general ability estimation simultaneously in a listening test. The data used were 1,611 examinees' item-level responses to an in-house EFL listening test in China and five content experts' item-attribute coding results of the test form. The bi-factor MIRT model was compared with five CDMs with and without a higher-order structure in terms of model fit, attribute classification and general ability estimation. The results showed that the bi-factor MIRT model provided the best model-data fit, followed by the HO-G-DINA model, the saturated G-DINA model, and other reduced CDMs. The HO-G-DINA model produced attribute classification results more similar to the G-DINA model, whereas the bi-factor MIRT model offered better results in discriminating examinees' general listening ability. The findings of this study highlighted the feasibility of using the bi-factor MIRT model as an attractive alternative for diagnostic assessment, especially in language assessment where attributes are assumed to be continuous.