ExtremeTextBench: Evaluating Long-Form Text Rendering and Typeface Fidelity in Text-Rich Image Generation

Recent image-generation models have made rapid progress in text rendering. However, most existing benchmarks evaluate rendering accuracy under conventional settings, focusing primarily on short words, phrases, or moderately long text. As model capabilities continue to advance, these settings offer increasingly limited discriminative power and provide little insight into whether models can satisfy demanding design requirements, including long-form text rendering, small-scale text rendering, and precise typeface control. We introduce ExtremeTextBench, a bilingual benchmark that systematically evaluates two underexplored capabilities: rendering text under extreme length and size conditions, and adhering to explicit typeface constraints. ExtremeTextBench comprises 600 English and Chinese prompts, 3,695 annotated text segments, 78 typeface families, and 24 design categories drawn from realistic text-rich applications, including posters, presentation slides, e-commerce graphics, menus, and invitations. By analyzing performance across text-length and text-size ranges (with target lengths of up to 428 words in English and 1,584 characters in Chinese, and an observed small-text range of $\le 20$ pixels), ExtremeTextBench separately examines how rendering performance varies along these two dimensions, providing a more discriminative assessment of current state-of-the-art models. We also introduce a typeface-control evaluation method to assess whether generated text faithfully follows explicitly specified typefaces. Experiments on 16 state-of-the-art image-generation models reveal persistent limitations in rendering ultra-long text and text at small scales, while precise typeface control remains an open challenge. ExtremeTextBench fills an important gap in existing evaluation by providing a realistic and diagnostic testbed for the next generation of text-capable image-generation models.