OpenAI GPT Image 2 demonstrates strong capability with multi-element compositions, maintaining coherence across foreground subjects, mid-ground details, and background environments. The model excels when you structure prompts hierarchically: primary subject first, supporting elements second, environmental context third, then style and lighting. For example, 'elderly craftsman carving wood, workshop interior with tools on walls, afternoon sunlight through dusty windows, documentary photography style' guides the model to prioritize elements appropriately. Complex scenes benefit from the high quality setting, which allocates more computational resources to detail preservation across all composition layers. The model generally maintains consistent lighting and perspective across multiple subjects, though occasional coherence issues may arise with more than 4-5 distinct elements. For scenes requiring precise spatial relationships or architectural accuracy,
Recraft V4 Pro Text to Image offers different optimization strategies that may handle geometric complexity more reliably. Test complex prompts across quality tiers to determine where detail preservation justifies increased credit costs for your specific project needs.