Continual learning aims to enable models to learn sequentially from continuously incoming data while retaining performance on previously learned tasks.With the Contrastive Language-Image Pre-trained model (CLIP) exhibiting strong capabilities across various downstream tasks, there has been growing interest in leveraging CLIP for continual learning in such scenarios.Most existing works overlook the inherent modality gap in CLIP, a key factor in its generalization and adaptability. In this paper, we analyze the variations in the modality gap during the fine-tuning of vision-language pre-trained models.Our observations reveal that the modality gap effectively reflects the extent to which pre-trained knowledge is preserved.Based on these insights, we propose a simple yet effective method that improves CLIP’s performance in class-incremental learning.Our approach leverages modality gap preservation to mitigate forgetting and modality gap compensation to enhance the capacity for new data, introducing a novel modality-gap-based perspective for continual learning. Extensive experiments on multiple benchmarks demonstrate that our method outperforms existing approaches without requiring additional replay data.