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DNA methylation-based tumor classification has become an essential tool in neuropathological diagnostics, yet a subset of central nervous system (CNS) tumors remains unclassifiable using current approaches. The performance of machine learning-based classifiers across varying case complexity is not fully defined. We evaluated the Bethesda Classifier v3 (Bv3) in both straightforward and diagnostically challenging CNS tumor specimens. We first applied Bv3 to 195 CNS tumor samples that were successfully classified using the Heidelberg Classifier v12.8 (Hv12.8; n = 195). We then compared Bv3 classifications in a retrospective cohort of diagnostically challenging, unclassifiable cases by Hv12.8 (n = 321). Both cohorts have been re-evaluated to identify the integrated histomolecular diagnoses according to the WHO CNS5 (2021) classification, hereafter referred to as the final WHO2021 diagnosis. Misclassification was defined as cases with Bv3 scores ≥ 0.9 that did not match the final WHO2021 diagnosis. In the straightforward cohort, Bv3 correctly classified 189/195 cases (97%). In the challenging cohort, Bv3 classified 180/321 previously unclassifiable cases (56%). 13/321 (4%) cases were misclassified by Bv3, of which 7/13 (54%) were labeled as ganglioglioma. Among glioblastomas, 79% were correctly identified, including tumors with lower DNA concentration and lower tumor purity. Bv3 assigned HGAP (High-Grade Astrocytoma with Piloid features) with a score ≥ 0.9 in three cases, which were not concordant with the final WHO2021 diagnosis. The Bethesda Classifier v3 demonstrates robust performance across both straightforward and diagnostically challenging CNS tumor specimens and represents a valuable complementary machine learning tool in routine neuropathological diagnostics.