This study evaluates the extent to which fully automated street network modelling can reproduce the analytical outcomes of a hybrid (automated–manual) approach for centrality and accessibility analysis across four morphologically distinct cities: Nicosia, London, Gothenburg, and Madrid. Results show that the geometry-preserving segmentation logic of the automated workflow systematically increases network granularity relative to the continuity-merging rules applied in the hybrid model, producing higher node densities and shorter street segments, particularly in cities with irregular, historically layered street patterns. Despite these geometric differences, angular integration exhibits strong to near-perfect rank correlation between hybrid and automated models across all cases, with agreement increasing at larger spatial radii. This indicates that global configurational structure is robust in automated simplification. Angular betweenness displays lower, though still moderate to high, correspondence, especially at smaller radii, reflecting its sensitivity to over-segmentation and local geometric variation. Spatial autocorrelation analysis reveals that ranking differences are strongly clustered in footpaths in open green spaces and detailed paths in residential areas. However, the differences are more locally confined and diminish at larger radii. Accessibility results further demonstrate scale-dependent divergence: short-range attraction reach and attraction distance estimates align closely between models, whereas discrepancies increase at larger thresholds due to the cumulative effects of finer-grained connectivity and additional pedestrian paths in automated networks. Overall, the findings indicate that automated street network models reliably capture large-scale integration and accessibility patterns across diverse urban contexts, while local, flow-sensitive measures remain more affected by network granularity and path representation. These results support the use of automated workflows for scalable and comparative urban analysis, with targeted manual refinement reserved for contexts requiring high local precision.