FAQ
Answers about GPT Image 2 / gptimage 2 prompts, workflows, and this independent guide site.
- How does GPT Image 2 differ from DALL·E?
- GPT Image 2 generates images natively inside a modern multimodal stack, with thinking mode, strong text rendering, and multi-image batch runs — commonly used as a practical successor workflow to older DALL·E-style tools.
- What is GPT Image 2 thinking mode?
- With thinking mode on, the model plans composition, checks references, and verifies text and object counts before outputting multiple coherent images — useful for posters and series with less rework.
- How accurate is GPT Image 2 text rendering?
- Short English text is typically very strong; Chinese, Japanese, Korean and other non-Latin scripts are solid for posters, banners and UI mockups. Always verify critical copy.
- What resolutions and aspect ratios does GPT Image 2 support?
- Common outputs include 2K with higher tiers available; aspect ratios from about 3:1 to 1:3 cover banners, posters and vertical social formats.
- How do I start with GPTImage Prompts?
- Browse our prompt library or tutorials, copy a prompt, then open the gptimage App workbench to generate. For complex series, enable thinking mode.
- Does GPT Image 2 support transparent backgrounds?
- Transparent PNG output is often limited; for cutout-heavy workflows, pair with a dedicated cutout/FLUX-style step.
- What is a practical batch limit?
- Thinking-style runs often produce multiple coherent images per pass — great for poster series, storyboards and e-commerce variants. See our batch tutorial.
- Which commercial use cases suit gptimage 2 prompts?
- E-commerce heroes and detail shots, UI/UX mockups, multilingual ad posters, publishing art, comic storyboards and automated content pipelines.