Recent text-to-image (T2I) models achieve high visual fidelity and accurate explicit grounding from input prompts. However, image generation is fundamentally a process of semantic expansion. Beyond surface-level descriptions, generating authentic visual content requires supplementing prompts with unstated cues, a crucial step that current models frequently overlook. In response, we introduce WorldGenBench, a benchmark designed to evaluate how well T2I systems integrate factual grounding with contextual inference. Unlike prior work that isolates these abilities, WorldGenBench emphasizes their synergy, recognizing that true understanding requires both knowing and reasoning. To enable interpretable evaluation, we propose the Knowledge Checklist Score, which assesses whether generated images faithfully reflect key explicit and implicit semantic elements. Evaluating 24 models reveals persistent gaps: proprietary models like GPT-4o show strong integration, while most open-source systems lag, particularly in culturally diverse and scientific scenarios. Our analysis further identifies common failure modes, including semantic hallucination and the underrepresentation of non-Western knowledge, underscoring the limitations of current models in achieving real-world readiness. These findings highlight the need to guide future T2I models toward deeper, knowledge-aware generation. Project Page: \href{https://dwanzhang-ai.github.io/WorldGenBench/}{https://dwanzhang-ai.github.io/WorldGenBench/}

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