GPT Image 2 image generator

What GPT Image 2 is

GPT Image 2 is an image generation and editing model from OpenAI. OpenAI lists it as gpt-image-2 with the default snapshot gpt-image-2-2026-04-21 — a date that lines up with its 21 April 2026 launch post — and describes it as a state-of-the-art model for fast, high-quality image generation and editing that supports flexible image sizes and high-fidelity image inputs.

The same launch post introduced it to ChatGPT users as ChatGPT Images 2.0 under the heading “A new era of image generation”. This page covers the model, not the ChatGPT product around it: what it accepts, what the samples show it producing, what OpenAI charges for it, and where its published limits sit.

One thing matters before you pick it. GPT Image 2 is not the newest tier OpenAI ships. The official image generation guide now points new integrations at gpt-image-2.5-flare and gpt-image-2.5-sunburst, and keeps gpt-image-2 in a section headed “Earlier GPT Image models”. The model is still listed and still billable, but treating the April 2026 generation as the current state of the art would be wrong — see the comparison section below for what actually differs.

What the samples show it renders well

OpenAI captions every file in its launch gallery as generated with this model, so the gallery doubles as an honest summary of where the generation improved. Five areas stand out.

• Multi-script typography. Posters and packaging that mix Japanese, Korean, Arabic, Devanagari, Cyrillic, Bengali, Greek, Chinese and Latin letterforms in a single layout, with lettering treated as type rather than as texture.
• Photographic realism. Street, portrait and candid flash-photography looks with legible skin texture, believable hands and light that behaves like light.
• World knowledge. Period interiors, software surfaces and signage that follow the logic of the thing depicted instead of averaging it into something generic.
• Format range. From a wide traffic panorama to a phone-tall comic page, so one brief can cover a banner, a cover and a story frame.
• Panel-to-panel continuity. Comic and storyboard pages that hold characters, lettering and print texture together across several panels.

Tasks that fit this model

The samples suggest a fairly specific profile: work where the image has to carry text, carry a look, or carry a story across more than one frame. Briefs that fit it well tend to look like this.

It is a weaker fit for work that depends on pixel-exact placement of many small elements, or on a recurring brand character staying identical across dozens of separate generations — both are limits OpenAI states openly, and they are covered further down.

• Posters, covers and packaging where a short headline or product name has to be spelled correctly and placed deliberately.
• Localised campaign assets where the same composition has to work in several scripts without the layout collapsing.
• Editorial illustrations and infographic spreads that need real structure — columns, callouts, maps, captions — rather than decoration.
• Photoreal scenes for mockups, storyboards or concept boards that a client has to read as a photograph.
• Comic pages and character sheets where the same figure has to stay recognisable panel after panel.
• Edits that start from a reference you are allowed to use, where the surrounding detail should survive the change.

Prompt ideas you can copy

These are written for this page as starting points. They are not the instructions behind any image in the gallery, and no result is guaranteed — change one decision at a time between attempts so you can tell what caused the change.

Design a single poster about a city night market. Set the same short headline in three scripts stacked in one column, keep the grid identical across all three, use ink black, vermilion and newsprint cream, and leave an empty band at the bottom for a date to be typeset later.

Photograph a small bakery counter in early morning light. One window on the left, flour dust visible in the beam, warm interior tones, a shallow depth of field on the pastries, and no text anywhere in the frame. Describe it as if shot on 35mm film.

Draw a two-panel comic page about a courier waiting for a train. Keep the same coat, bag and hair across both panels, change only the camera: wide station platform first, close on the hands second. Lettering must stay in the upper third of each panel.

Using my uploaded reference photo, replace only the surface under the product with matte dark stone. Keep the product silhouette, its label text, the camera angle and the existing shadows exactly as they are. Change nothing else.

Sizes, quality and output options

OpenAI publishes the accepted inputs for this model in the image generation guide. The table below is that specification, not this site's control panel — the two are related but not identical.

The workspace above presents the same idea in this site's own controls: sixteen aspect ratios including auto, a resolution tier of 1K, 2K or 4K, a render quality of low, medium or high, and up to ten reference images. Those tiers are this site's shorthand rather than OpenAI's parameter names, and the live service decides the final options and the point cost for the settings you pick.

Only the controls shown for this model exist. A size or quality offered on another provider's page does not become available here by being advertised elsewhere.

How to generate with GPT Image 2 here

The workflow is the same one the other image models on this site use, so the habits transfer.

1. Write the brief in the workspace above: deliverable first, then the subject, the framing, the light and the constraints you actually care about.
2. Add reference images only if you are editing or matching something. State which reference does what, and what must stay unchanged.
3. Set the aspect ratio, resolution tier and render quality. Start lower for composition checks and raise the tier once the arrangement is right.
4. Read the quote and sign in when the workspace asks. The live service calculates the cost for the settings you selected.
5. Generate, inspect the result at full size, then download the version you want to keep. If a job fails, read its status before retrying so you do not submit a duplicate.

GPT Image 2 next to the newer 2.5 tiers

All three rows below come from the same two OpenAI pages — the pricing table and the image generation guide — so this is a comparison the sources themselves support rather than one assembled from other people's reviews.

Two conclusions follow from those rows. The 2.5 tiers share the same token prices as this model while adding two higher quality settings, so there is no cost argument for staying on the older generation. The Batch API is the one place where GPT Image 2 has a published price the 2.5 tiers do not, at roughly half the standard image rate.

For a head-to-head on a specific brief, run the same prompt and the same reference through each model here and compare the result rather than the marketing. Cross-provider comparison tables are easy to find online; almost none of them can point at a source that documents both sides.

Cost and the limits OpenAI publishes

GPT Image 2 is billed by token, not by image: the final cost is the input text tokens, plus input image tokens when you use reference images, plus the image output tokens. The output count grows with image size and quality, which is why a 4K high-quality request costs more than a 1K draft of the same prompt.

The limits list is worth reading before a deadline rather than after one. The two that bite most often are latency on dense layouts and consistency across a set of images — plan a proof-of-concept pass for the first, and reduce the number of separate generations for the second.

This page does not reproduce third-party quota claims. Figures that describe daily image allowances in a chat subscription, or a maximum resolution, are not part of OpenAI's model or pricing documentation, and where those numbers disagree with the official pages the official pages are the ones that describe this model.

• Latency. OpenAI states that complex prompts may take up to two minutes to process.
• Text rendering. Improved, but the model can still struggle with precise text placement and clarity.
• Consistency. Maintaining the same character or brand element across separate generations remains unreliable.
• Composition control. Placing elements precisely in structured, layout-sensitive compositions is still difficult.
• Moderation. Every prompt and result is filtered against the usage policies; the strictness is set by the moderation parameter.