In this paper, we propose and compare a method that uses logit-based GOP scores instead of the conventional softmax-based probability-based GOP for pronunciation assessment. We conduct experiments on two L2 English corpora of Dutch and Mandarin speakers, and evaluate the correlation between the classification performance and human rater scores. The results show that the logit-based method outperforms the probability-based GOP in classification performance, but the effect varies depending on the dataset characteristics. The maximum logit GOP matches human perception the best, suggesting that a hybrid method that combines various GOP scores is effective in considering both probability and logit features in a balanced way. Our results suggest that a hybrid GOP method that includes uncertainty modeling and phoneme-wise weighting can improve pronunciation assessment.