Patterns of variability in diversity (alpha and beta), abundance, and community structure of soft-bottom macrobenthic assemblages were investigated across an inshore/offshore environmental gradient in the central Red Sea. A total of three distinct soft-substrate biotopes were identified through multivariate techniques: seagrass meadows, nearshore, and offshore. While the seagrass biotope was associated with higher organic matter content, the two coastal biotopes presented higher redox potential in the sediments and dissolved oxygen in the water. Depth and medium sand increased toward the offshore, while the percentage of fine particles was a determinant of nearshore communities. Regardless of the prevailing environmental conditions, the three biotopes were characterized by high numbers of exclusive taxa, most of which were singletons. Changes in species richness were not related to depth or organic matter, peaking at intermediate depths (nearshore). However, the number of taxa increased exponentially with abundance. On the other hand, density decreased logarithmically with depth and organic matter in sediments, probably linked to a reduced availability of food. One of the most conspicuous features of the macrobenthic assemblages inhabiting soft substrates in the central oligotrophic Red Sea is the low level of dominance resulting from a high species richness: abundance ratio. Despite the differences observed for alpha-diversity across the three biotopes, beta-diversity patterns were rather consistent. These findings suggest that mechanisms driving biodiversity are similar across the depth gradient. The partitioning of beta-diversity also show that assemblages are mainly driven by the substitution of species (turnover or replacement), most likely as a result of environmental filtering. The heterogeneity of the seafloor in shallow waters of the Red Sea promoted by the co-existence of coral reefs inter-spaced by sedimentary habitats may increase the regional pool of colonizers and potentiate the stochasticity of the distribution patterns.