The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-text compression (VTC), exemplified by frameworks like DeepSeek-OCR and Glyph, which convert long texts into dense 2D visual representations, thereby achieving token compression ratios of 2x–10x. However, the impact of this high information density on the core long-context capabilities of vision-language models (VLMs) remains under-investigated. To address this gap, we introduce the first benchmark for VTC and systematically assess the performance of VLMs across three long-context understanding settings: VTC-Retrieval, VTC-Reasoning, and VTC-Memory. We further introduce VTCBench-Wild to evaluate robustness across diverse rendering configurations. We comprehensively evaluate leading open-weight and proprietary models on our benchmarks. The results indicate that several VLMs can retrieve information from rendered text in short-to-moderate contexts, but this ability degrades at longer lengths, and the degradation is especially pronounced on tasks requiring binding and integrating dispersed evidence. This study provides diagnostic evidence of VTC and serves as a foundation for designing more efficient and scalable VLMs.