What is GPT Image 2
GPT Image 2 is an OpenAI model with the API identifier gpt-image-2 for image generation and editing. The official documentation describes text and image inputs, flexible image sizes, and high-fidelity handling of input images. The Images API supports generation through /v1/images/generations and edits through /v1/images/edits.
What the official material confirms
Generation and edits
The same API surface supports creating a new image and editing an input asset.
High-fidelity input
The model is designed to preserve important details and identity when working from input images.
Flexible sizes
The documentation supports several formats and sizes; the exact set depends on the current API version.
Meaning and structure
The practical advantage appears in layouts, lettering, composition, and prompts that require relationships between objects.
This does not mean error-free output. Official pages describe supported capabilities and API parameters, not identical results for every prompt, language, typeface, or source image. Every final asset still needs visual review and a rights check for its inputs.
Pricing, access, and API
The OpenAI pricing page lists tokenized rates for gpt-image-2: USD 4 per 1M image-input tokens, USD 1 per 1M cached input tokens, and USD 15 per 1M image-output tokens; text input is priced separately at USD 2.50 per 1M tokens or USD 0.625 for cached input. The OpenAI guide calculator gives approximate square 1024x1024 costs of USD 0.006 / 0.053 / 0.211 for low / medium / high, and about USD 0.005 / 0.041 / 0.165 for 1024x1536 and 1536x1024. These estimates exclude input text and image tokens, so check the live API response and pricing page before a production run.
Check the model
Use the exact identifier gpt-image-2 and verify current limits and account availability in your API workspace.
Choose quality
Start with medium for iterations and reserve high for a final asset after the composition is approved.
Count the inputs
Reference images add input tokens to the generation estimate and can change the final cost of a batch.
What independent comparisons show
Independent numbers depend on date, prompt mix, quality mode, and voting method. Read them as dated methodological snapshots, not permanent rankings.
Artificial Analysis — 11 Oct 2026
On the current text-to-image leaderboard, GPT Image 2 (high) is ranked third with 1172±8 Elo and 15,601 samples; GPT Image 2.5 Sunburst and Flare are above it. This is blind preference data, not a universal score for every workflow.
Tom Guide — 29 Apr 2026
In a seven-prompt head-to-head against Nano Banana 2, GPT Images 2.0 won 4:3. It was stronger on text and layout, spatial relationships, and conceptual scenes; the competitor won individual tests for physics, hands, and narrative staging.
TechRadar — 28 Apr 2026
In a practical comparison of portrait, lighting, product, and seasonal edits, the author gave ChatGPT Images 2.0 the overall edge, while describing a draw in one test and a difference between speed, realism, and strict prompt following.
Where it is especially useful
Editorial covers
When one frame must combine the subject, a readable headline, visual hierarchy, and a safe zone for different card crops.
Concepts and storyboards
When you need to compare directions while preserving a character, object, lighting setup, or compositional logic.
Source-image editing
When changing an environment, season, light, or mood matters more than creating a scene from scratch.
Information graphics
When an image must explain a process, relationships, or spatial structure rather than only create atmosphere.
Limitations worth stating plainly
The model can make mistakes in small text, rare names, complex anatomy, exact geometry, batch consistency, and strict adherence to every detail in a long prompt. Results also depend on source images, prompt language, quality setting, API queue, and available limits.
Do not trust lettering without review
Zoom in and check every letter, number, URL, brand name, and legally meaningful element.
Do not upload third-party material casually
Faces, logos, photographs, and commercial assets require your own rights or a valid permission basis.
Do not treat a beautiful frame as evidence
Generation creates a visual interpretation; it does not prove that an event, person, or product exists in that exact form.
Practice: a prompt for an editorial cover
A strong request starts with the editorial job, then fixes composition and constraints. For a website cover, say in advance that the main object and title must stay in the central safe zone and that random small lettering is forbidden.
Professional editorial cover for a technology article about GPT Image 2. Dark navy background, restrained gold accents, cinematic light. In the central safe zone, render exactly and without errors: GPT Image 2. Show layers of image generation and editing, several connected visual frames, and a subtle bridge motif as a metaphor for connecting an idea to a result. The composition must work as a wide banner, a card, and a square crop. Add no other words, logos, watermarks, tiny captions, or random letters.
A short API workflow
Define the job
Describe the audience, format, main subject, and what the viewer should understand in one second.
Fix the composition
Name the safe zone, aspect ratio, hierarchy, palette, and forbidden elements.
Iterate deliberately
Change one parameter at a time, preserve the successful prompt, and inspect the asset at the real card size.
Run editorial review
Check facts, in-image text, input rights, alt text, and consistency across RU and EN localizations.