  We perform a missing, reproducible evaluation of all publicly available GPT-4
family models concerning the Document Understanding field, where it is
frequently required to comprehend text spacial arrangement and visual clues in
addition to textual semantics. Benchmark results indicate that though it is
hard to achieve satisfactory results with text-only models, GPT-4 Vision Turbo
performs well when one provides both text recognized by an external OCR engine
and document images on the input. Evaluation is followed by analyses that
suggest possible contamination of textual GPT-4 models and indicate the
significant performance drop for lengthy documents.
