The coded-aperture snapshot spectral imaging (CASSI) system has garnered significant attention as a promising spectral snapshot-based imaging technique. However, several challenges persist in the development of CASSI systems: 1. High hardware costs. 2. The difficulty of balancing the performance and computational efficiency of the reconstruction network. In this paper, we propose a dual-camera mask-free CASSI system to replace the expensive encoding hardware, thereby reducing the overall cost of CASSI. To address the issues of a high number of parameters and computational demands associated with Transformer-based methods, we design a hierarchical attention-based spectral-spatial transformer. This approach involves embedding local features on both the spectral and spatial channels, while also leveraging global information from these channels to enhance the network’s global learning capabilities. Our experimental results demonstrate the superiority of the mask-free approach, achieving a PSNR of 37.9 dB in a single camera and 44.7 dB in dual cameras. Furthermore, experiments indicate that the mask-free system outperforms state-of-the-art methods while requiring lower computational and memory costs.