GPT Image 2: Photorealistic Text-to-Image and Edit Model
What is GPT Image 2?
GPT Image 2 is OpenAI's next-generation image model, launched in April 2026 as the successor to gpt-image-1.5. It generates photorealistic images from text or edits existing images guided by a prompt, all through a single endpoint. The headline improvement is near-perfect in-image typography: over 95% accuracy across Latin, Japanese, Korean, Chinese, Hindi Devanagari, and Bengali scripts — the first image model practical for shipping UI labels, posters, and multilingual marketing assets without a manual redraw pass. A new single-pass architecture roughly doubles generation speed over the previous version, and built-in reasoning plans composition, counts items, and checks constraints before rendering.
Key Features
- •Text-to-image generation and guided image editing in one API
- •In-image text rendering at 95%+ accuracy, including non-Latin scripts
- •Output resolutions up to 2K across landscape (
1536x1024), portrait (1024x1536), and square (1024x1024) - •Transparent-background outputs for logos, stickers, and product cutouts
- •Output formats: PNG (sharpest text), WebP (smaller files), JPEG (universal)
- •Moderation controls:
autodefault,lowfor permitted use cases - •Native multi-constraint prompt adherence at ~98% accuracy
Best Use Cases
GPT Image 2 is the right choice anytime legible text is part of the image: magazine covers with headlines, product packaging mockups, storefront and signboard scenes, infographics and charts, storyboards and comic panels, multilingual ad creatives, UI screen mockups, and posters. In testing, it rendered a handwritten chalkboard easel combining English ("Mumbai Book Store", "Open Daily 9 am – 9 pm") and Hindi Devanagari ("मुंबई पुस्तक भंडार") cleanly on the first try. Edit mode (passing an image input) is ideal for relighting, background swaps, text changes on existing visuals, and brand-consistent variations.
Prompt Tips and Output Quality
Keep quality=high whenever typography matters — medium and low degrade fine lettering. Lead the prompt with subject, then typography in quotes, then style and lighting cues. For magazine-style layouts pick 1024x1536; for marketing banners and scenes, 1536x1024. Use background=transparent for product shots that will be composited downstream. Keep output_format=png and output_compression=100 when text crispness is non-negotiable.
FAQs
Does GPT Image 2 render text in Hindi, Japanese, and Chinese? Yes. Multilingual typography is the model's flagship capability — Devanagari, CJK, Korean, and Bengali all render cleanly enough to ship.
What is the difference between generation and edit mode?
Leaving the image parameter null generates from text alone. Passing an image URL switches the model into edit mode, where the prompt guides modifications to the input.
What output sizes are supported?
1024x1024, 1536x1024, 1024x1536, and auto. All run up to 2K resolution with high quality.
When should I use background=transparent?
For logos, stickers, icon sets, and product cutouts that will be composited against other backgrounds.
Is GPT Image 2 faster than gpt-image-1.5? Yes — roughly 2× faster thanks to a new single-pass architecture, with fewer artifacts on hands, faces, and material surfaces.
Where does GPT Image 2 fall short? Physical reasoning tasks (origami, angled reflections, Rubik's cubes) and highly dense repetitive detail (circuit diagrams, grains of sand) still challenge the model. Iterative edits beyond one or two passes tend to drift.