Marketing teams shipping text-heavy creative
Create social cards, ads, banners, and cover art with readable headlines ready for review.
Built for text-heavy image workflows
GPT Image API — GPT Image 2 generation and editing
GPT Image 2 on GPT Image API — quality tiers, custom sizes, and strong prompt adherence.
Try it in the playground.
Used by product, growth, and creative teams
1M+
Images generated
99.9%
Uptime
~300ms
Latency
What it is
GPT Image API at a glance
A GPT Image 2 generation and editing API with one key, flexible controls, and async workflows.
Product teams mocking up interfaces
App screens, onboarding flows, and dashboard hero shots with readable UI labels.
Agencies producing labeled assets
Packaging comps, menu boards, retail signage, and SKU-heavy creative benefit from reliable text rendering.
Localization teams reviewing CJK content
Build Chinese, Japanese, and Korean image workflows with concise copy and an explicit review step.
How teams ship with GPT Image API
A common production path from first test to repeatable workflow:
1
Pick a target asset class
Separate quick ideation, UI screenshot generation, packaging comps, and editorial hero art into different prompt templates.
2
Quote the exact text
Wrap literal copy in quotes and keep each region short enough that the model treats it as non-negotiable content.
3
Choose size and quality per render
Use smaller sizes for prompt testing, high quality for final assets.
4
Move long runs to async
Queues and webhook-based completion keep batch jobs from tying up synchronous workers or browser sessions.
One prompt — use GPT Image API from any Agent
Copy a single prompt to Codex, Cursor, or any coding agent so it understands GPT Image API's models and endpoint patterns before writing code.
GPT Image API — llms.txt prompt
Use GPT Image API to generate an image with the gpt-image-2 model — read https://gptimageapi.dev/llms-full.txt for the API details.From zero to first request in minutes
A familiar REST interface and predictable response format keep integration work small—even when the underlying model changes.
1
Create an API key
Open the developer console, create a scoped key, and keep it safely in your server environment.
2
Choose a model
Every image model shares the same generation endpoint — switch models with one request field.
3
Ship and scale
Monitor credits and requests from one account while GPT Image API handles provider routing and availability.
GPT Image API — demo.request
curl --request POST \
--url https://api.gptimageapi.dev/v1/images/generations \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-image-2",
"prompt": "A cinematic product shot",
"size": "1:1"
}'200 OK · task queued · pay only for successful output
Why GPT Image API
What you get over generic image generation.
Text-focused workflows
Create headlines, labels, menus, and UI concepts with stronger text rendering, then review critical copy before publishing.
Flexible output controls
Choose the size and quality setting that fits prompt testing, previews, or final production assets.
One API key
Use one account and API key across supported image generation and editing workflows.
Edit without regenerating
Update copy, packaging, or layout on brand-approved assets through edit routes instead of starting over.
Batch-ready workflow
Use asynchronous tasks and explicit review steps to organize higher-volume image work.
Production-friendly
Predictable task states, error handling, and monitoring keep pipelines easy to operate.
Product facts
Verifiable facts, not marketing numbers.
6 Image models in one catalog
6
Image models in one catalog
3 Quality tiers — low / medium / high
3
Quality tiers — low / medium / high
4 Localized languages
4
Localized languages
What teams say
Operational reasons teams switch image pipelines.
"Short, quoted headlines give our campaign team a clearer review workflow before publishing."
Ava Thompson
Marketing lead
"The editing route helps us revise label copy on existing packaging comps instead of regenerating every concept."
Lucas Meyer
Agency art director
"We can test UI labels in dashboard mockups and review the text before the design-to-development handoff."
Maya Patel
Product designer
"Menus and product cards are easier to review when prompts keep names and labels concise."
Jonas Weber
E-commerce founder
"We now have a defined workflow for testing Chinese and Japanese captions before each local release."
Hana Suzuki
Localization lead
"One API key, predictable JSON, and clear quality controls simplified our integration."
Diego Ramos
Indie developer
GPT Image API
GPT Image 2 generation and editing
GPT Image API combines stronger text rendering, flexible size and quality controls, prompt patterns, and async task handling in one developer workflow.
Create posters, menus, app mockups, packaging comps, and social cards with reviewable in-image copy.
Choose flexible size and quality settings for prompt testing, previews, and final production assets.
Use quoted-text prompting, seeded reruns, and image edit routes instead of regenerating every asset from scratch.
Move long runs to async queues with webhooks so batch jobs never tie up synchronous workers or browser sessions.
FAQ
Common questions before integrating GPT Image API.
Use it when readable copy, labels, UI text, menus, packaging, or multilingual captions are part of the deliverable and the output will be reviewed before publishing. If the job is purely visual, a simpler model may be enough.
Low is useful for prompt iteration, medium is the default for most product work, and high is the tier to reserve for public-facing assets where text fidelity and final polish matter.
Keep each text region short, wrap literal copy in quotes, specify the layout context clearly, and separate must-have text from descriptive styling instructions.
Yes. Keep Chinese, Japanese, and Korean copy concise, quote literal text in the prompt, and validate spelling, placement, and legibility with representative inputs before publishing.
Yes. In practice, edit routes are most valuable when you already have brand-approved assets and want to update copy, packaging, or layout instead of regenerating from scratch.
Queue the jobs asynchronously, store prompts and seeds alongside each request, validate outputs automatically where possible, and use webhooks or worker callbacks to collect finished renders.
No. The highest leverage approach is selective adoption: use GPT Image API where text fidelity or editability matters, and keep cheaper models for purely visual background assets.
Start building with GPT Image API
Open the playground, test a prompt, then integrate.