The cytoplasm is a complex, crowded, actively-driven environment whose biophysical characteristics modulate critical cellular processes such as cytoskeletal dynamics, phase separation, and stem-cell fate. Little is known about the variance in these cytoplasmic properties. Here, we employed particle-tracking nano-rheology on genetically encoded multimeric 40-nm nanoparticles (GEMs) to measure diffusion within the cytoplasm of the fission yeast Schizosaccharomyces pombe . We found that the apparent diffusion coefficients of individual GEM particles varied over a 400-fold range, while the average particle diffusivity for each individual cell spanned a 10-fold range. To determine the origin of this heterogeneity, we developed a Doppelgänger Simulation approach that uses stochastic simulations of GEM diffusion that replicate the experimental statistics on a particle-by-particle basis, such that each experimental track and cell had a one-to-one correspondence with their simulated counterpart. These simulations showed that the large intra- and inter-cellular variations in diffusivity could not be explained by experimental variability but could only be reproduced with stochastic models that assume an equally wide intra- and inter-cellular variation in cytoplasmic viscosity. To probe the origin of this variation, we found that the variance in GEM diffusivity was largely independent of factors such as temperature, cytoskeletal effects, cell cycle stage and spatial locations, but was magnified by hyperosmotic shocks. Taken together, our results provide a striking demonstration that the cytoplasm is not “well-mixed” but represents a highly heterogeneous environment in which subcellular components at the 40-nm size-scale experience dramatically different effective viscosities within an individual cell, as well as in different cells in the population. These findings carry significant implications for the origins and regulation of biological noise at cellular and subcellular levels.