Video snapshot compressive imaging (SCI) is a novel imaging technique. It captures three-dimensional video data using a two-dimensional detector within a single exposure time and then reconstructs the original video data with appropriate algorithms. Although many current algorithms have outstanding performance in the reconstruction tasks of video SCI, the improvement of their reconstruction quality often comes at the cost of sacrificing the reconstruction speed, which significantly reduces the real-time performance of the algorithms. To balance reconstruction quality and speed, this paper proposes an end-to-end deep video SCI reconstruction method based on Mamba-2, namely M2BA-SCI. The M2BA-SCI network consists of a preprocessing module, a token generation block, Mamba attention blocks, and a video reconstruction block. Among them, the Mamba attention blocks are mainly composed of Mamba-2 linear attention blocks and feed-forward neural networks. A large number of experiments on simulated and real video datasets show that M2BA-SCI achieves a more balanced effect compared with previous algorithms. It maintains a relatively fast reconstruction speed while improving the reconstruction quality. In the benchmark grayscale video dataset, the average PSNR is 34.85, the average SSIM is 0.966, and the running time is 0.23 s. In the benchmark color video dataset, the average PSNR is 36.21, the average SSIM is 0.963, and the running time is 1.03 s. M2BA-SCI brings new ideas to video SCI reconstruction and provides a reference for designing algorithms with higher reconstruction quality based on the Mamba model.