Learning motor control for muscle-driven musculoskeletal models has been hindered by the computational cost of biomechanically accurate simulation and the scarcity of validated, open full-body models. We present MuscleMimic, an open-source framework for scalable motion imitation learning with physiologically realistic, muscle-actuated humanoids. MuscleMimic provides two validated musculoskeletal embodiments, a fixed-root upper-body model (126 muscles) for bimanual manipulation and a full-body model (416 muscles) for locomotion, together with a retargeting pipeline that maps SMPL-format motion capture data onto musculoskeletal structures while preserving kinematic and dynamic consistency. Leveraging massively parallel GPU simulation, the framework achieves order-of-magnitude training speedups over prior CPU-based approaches, enabling a single generalist policy to be trained on hundreds of diverse motions within days. Biomechanical validation against experimental data demonstrates strong kinematic agreement for walking ($r{=}0.92$–$0.94$) and running ($r{=}0.79$), while muscle activation analysis reveals both the promise and fundamental challenges of achieving physiological fidelity through kinematic imitation. Code, musculoskeletal models, policy checkpoints, and retargeted datasets will be made publicly available.