Electrical impedance tomography (EIT) utilizes electrodes on a medium's surface to produce measured data from which the conductivity distribution inside the medium is estimated. For the cases that relocation of electrodes is impractical or no a priori assumptions can be made to optimize the electrodes placement, a large number of electrodes may be needed to cover all possible imaging volume. This may occur in dynamically varying conductivity distribution in 3D EIT. Three-dimensional EIT then requires inverting very large linear systems to calculate the conductivity field, which causes significant problems regarding storage space and reconstruction time in addition to that data acquisition for a large number of electrodes will reduce the achievable frame rate, which is considered as major advantage of EIT imaging. This study proposes an idea to reduce the reconstruction complexity based on the well-known compressed sampling theory. By applying the so-called model-based CoSaMP algorithm to large size data collected by a 256 channel system, the size of forward operator and data acquisition time is reduced to those of a 32 channel system, while accuracy of reconstruction is significantly improved. The results demonstrate great capability of compressed sampling for overriding the challenges arising in 3D EIT.