We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of discrete audio representation, i.e., tokens. Unlike prior work, MAGNeT is comprised of a single-stage, non-autoregressive transformer encoder. During training, we predict spans of masked tokens obtained from the masking scheduler, while during inference we gradually construct the output sequence using several decoding steps. To further enhance the quality of the generated audio, we introduce a novel model rescorer method. In which, we leverage an external pre-trained model to rescore and rank predictions from MAGNeT which will be then used for later decoding steps. Lastly, we explore a hybrid version of MAGNeT, in which we fuse between autoregressive and non-autoregressive models to generate the first few seconds in an autoregressive manner while the rest of the sequence is being decoded in parallel. We demonstrate the efficiency of MAGNeT over the task of text-to-music generation and conduct extensive empirical evaluation, considering both automatic and human studies. We show the proposed approach is comparable to the evaluated baselines while being significantly faster (x7 faster than the autoregressive baseline). Through ablation studies and analysis, we shed light on the importance of each of the components comprising MAGNeT, together with pointing to the trade-offs between autoregressive and non-autoregressive considering latency, throughput, and generation quality. Samples are available as part of the supplemental material.