Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability

Ning Li; Jingran Zhang; Justin Cui

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arXiv · 2504.08003

Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability

Ning Li·Jingran Zhang·Justin Cui

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Abstract

OpenAI's multimodal GPT-4o has demonstrated remarkable capabilities in image generation and editing, yet its ability to achieve world knowledge-informed semantic synthesis--seamlessly integrating domain knowledge, contextual reasoning, and instruction adherence--remains unproven. In this study, we systematically evaluate these capabilities across three critical dimensions: (1) Global Instruction Adherence, (2) Fine-Grained Editing Precision, and (3) Post-Generation Reasoning. While existing benchmarks highlight GPT-4o's strong capabilities in image generation and editing, our evaluation reveals GPT-4o's persistent limitations: the model frequently defaults to literal interpretations of instructions, inconsistently applies knowledge constraints, and struggles with conditional reasoning tasks. These findings challenge prevailing assumptions about GPT-4o's unified understanding and generation capabilities, exposing significant gaps in its dynamic knowledge integration. Our study calls for the development of more robust benchmarks and training strategies that go beyond surface-level alignment, emphasizing context-aware and reasoning-grounded multimodal generation.

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Ning Li, Jingran Zhang, Justin Cui. 2025-04-09. Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability. https://arxiv.org/abs/2504.08003

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