Snow on Antarctic sea ice strongly affects thermodynamic processes, sea ice mass balance, and microwave remote sensing, yet its spatial variability and characteristic length scales remain poorly quantified. The aim of this study is to provide a spatially extensive, layer-resolved characterization of Antarctic late-summer snow on sea ice and, for the first time, to quantify the variability of snow properties and their horizontal correlation length scales on first-year (FYI) and multi-year ice (MYI). We use a unique combination of manual snow pit observations and more than 900 SnowMicroPen (SMP) profiles collected along meter-scale transects during three expeditions in the Weddell Sea between 2018 and 2021. Snow stratigraphy and microstructural classes were derived from SMP force data using a supervised one-dimensional convolutional neural network trained on manually classified SMP profiles. Across both regimes, intrinsic properties of individual snow types, including density and specific surface area, were remarkably similar. Differences between FYI and MYI instead arise from contrasting snowpack structure, snow type fractions, and spatial coherence, with MYI characterized by a higher prevalence of dense melt-freeze layers and enhanced vertical heterogeneity. Spatial autocorrelation analyses reveal pronounced scale-dependent variability, with snow properties on FYI decorrelating over short distances, while MYI exhibits substantially higher spatial coherence. Individual ice floes capture only about 50 % of the variability characteristic of their respective ice regime, underscoring fundamental limits to the representativeness of point measurements. A hierarchy of variability emerges, in which snow type fractions and layer thickness dominate snowpack heterogeneity, while bulk snow density is comparatively homogeneous across spatial scales. These results demonstrate that Antarctic summer snow variability is governed primarily by stratigraphic composition and ice-regime-dependent snowpack evolution rather than bulk-integrated properties. These findings emphasize the need for spatially distributed observations and stratigraphy-aware parameterizations to improve the representation of snow on Antarctic sea ice in remote-sensing applications and sea ice and climate models.