GPT Image 2

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)
• •Output formats: PNG (sharpest text), WebP (smaller files), JPEG (universal)
• •Moderation controls: auto default, low for 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. Keep output_format=png and output_compression=100 when text crispness is non-negotiable.

How to Use GPT Image 2

GPT Image 2 handles both text-to-image generation and guided editing through a single endpoint. The way you configure its parameters decides whether output lands at magazine-ready quality or drifts into preview territory — especially when in-image typography is part of the brief.

Structuring the Prompt

Lead with the subject and scene, then the exact text you want rendered (in quotes), then style and lighting cues. Specify fonts, scripts, and hierarchy when shipping multilingual creatives. Example: "A chalkboard easel reads 'Open Daily 9 am – 9 pm' in clean white chalk, shallow depth of field, golden-hour lighting." The model handles Latin, Japanese, Korean, Chinese, Hindi Devanagari, and Bengali without script switching tricks.

Recommended Settings

Based on working test runs, these defaults produce production-ready output:

• •quality=high — the single most important setting. Drop it only for quick previews; medium and low visibly degrade lettering.
• •size — 1536x1024 for marketing banners and scene compositions; 1024x1536 for magazine covers, posters, and portrait social; 1024x1024 for in-feed social.
• •output_format=png with output_compression=100 whenever text crispness matters. Switch to webp for web delivery where file size is a priority.
• •moderation=auto is the safe default; switch to low only for permitted use cases.
• •background=opaque (the default) or auto — this model does not support transparent output. For cutouts that need an alpha channel, use gpt-image-1.5.

Text-to-Image vs Edit Mode

Leave the image parameter null for pure text-to-image generation. To edit an existing image — relighting, background swap, on-image text change, brand variations — pass the image URL and the model switches into edit mode automatically. The same prompt guidelines apply: be specific about what you want changed and what must be preserved.

Best Practices

Batch multi-constraint prompts (object counts, colors, text strings, positions) in a single pass — GPT Image 2 resolves around 98% of these without rerolls. Avoid iterative editing beyond one or two passes; quality plateaus and drift creeps in. For dense technical diagrams or physically constrained geometry (origami, reflections), validate outputs before shipping.

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.

Does GPT Image 2 support transparent backgrounds?

No. background accepts opaque (the default) or auto; requesting transparent returns a 400. Use gpt-image-1.5 when you need an alpha channel for logos, stickers, or product cutouts.

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.