Multi-behavior recommendation systems have rapidly developed across various domains, leveraging abundant auxiliary user interactions to enhance the performance of recommendations for target behavior. Although existing approaches have made significant progress in utilizing multi-behavior information, they often fail to balance the preservation of behavior-specific local structures with effective cross-behavioral knowledge transfer. In this paper, we propose MGMRec, a novel multi-granularity graph recommendation framework that employs granular ball theory for enhanced multi-behavior modeling. Specifically, MGMRec introduces a multi-behavior granular ball module to preserve behavior-specific nuances while enabling effective knowledge transfer through granular ball contrastive learning. Furthermore, MGMRec employs a target-aware noise suppression encoder that disentangles informative and uninformative auxiliary signals, followed by attention-based aggregation for target behavior representation. Experimental results demonstrate that our model significantly outperforms state-of-the-art methods, achieving improvements of up to 3.39% and 1.97% in terms of HR@10 and NDCG@10, respectively.