Digital pathology has seen the advent of a wealth of foundational models (FMs), yet to date their performance on cell phenotyping has not been benchmarked in a unified manner. We therefore propose PathoCellBench: A comprehensive benchmark for cell phenotyping on Hematoxylin and Eosin (H&E) stained histopathology images. We provide both PathoCell, a new H&E dataset featuring 14 cell types identified via multiplexed imaging, and ready-to-use fine-tuning and benchmarking code that allows the systematic evaluation of multiple prominent pathology FMs in terms of dense cell phenotype predictions in a range of generalization scenarios. We perform extensive benchmarking of existing FMs, providing insights into their generalization behavior under technical vs. medical domain shifts. Furthermore, while FMs achieve macro F1 scores > 0.70 on previously established benchmarks such as Lizard and PanNuke, on PathoCell, we observe scores as low as 0.20. This indicates a much more challenging task not captured by previous benchmarks, establishing PathoCell as a prime asset for future benchmarking of FMs and supervised models alike. Code and data are available on GitHub .