Available Endpoints
Start building with the GPT Image 2 API
Multiple endpoints for text-to-video, image-to-video, fast preview flows, and async job retrieval. This section is laid out more like a product catalog than raw docs so users can scan what to use first.
Endpoint
Text-to-Image
fal-ai/gpt-image-2
Generate a new image from a prompt with explicit size, quality, format, and image count settings.
Best for: Best for net-new visuals, infographics, product shots, UI mockups, posters, and marketing creative.
Endpoint
Image Edit
openai/gpt-image-2/edit
Edit one or more source images with change-preserve instructions, optional masks, and auto or custom output sizing.
Best for: Use this for relighting, object replacement, cleanup, packaging revisions, background changes, or virtual try-on style workflows.
Endpoint
Queue Status
https://queue.fal.run/{model_id}/requests/{request_id}/status
Check whether a request is in queue, in progress, or completed, with optional logs in queue-based workflows.
Best for: Needed when you submit requests asynchronously and want to reflect progress in your backend or product UI.
Endpoint
Fetch Result
https://queue.fal.run/{model_id}/requests/{request_id}
Retrieve the finished output payload once the generation or edit request has completed.
Best for: Use this after status polling or webhook confirmation to read the final image URLs and usage metadata.
Get started today
Ready to integrate GPT Image 2?
Try the API directly in the console, or reach out to the team for onboarding, pricing, and enterprise setup.
API Documentation
How to get access to GPT Image 2 API
The official fal.ai client supports both direct subscribe-style workflows and queue-aware production patterns. Start with generation, then add edit mode and explicit polling when you need more control.
import { fal } from "@fal-ai/client";
fal.config({
credentials: process.env.FAL_KEY
});
const result = await fal.subscribe("fal-ai/gpt-image-2", {
input: {
prompt: "A premium bottled matcha drink ad with clean typography, mint green palette, condensation on the bottle, soft studio reflections, and a headline that reads 'ENERGY THAT FEELS CLEAN'",
image_size: "landscape_4_3",
quality: "high",
num_images: 1,
output_format: "png"
},
logs: true,
onQueueUpdate(update) {
if (update.status === "IN_PROGRESS") {
update.logs?.forEach((log) => console.log(log.message));
}
}
});
console.log(result.data.images[0].url);Async flow
- 1
Choose the generation or edit endpoint based on whether you are creating from scratch or modifying an existing image.
- 2
Submit the request with prompt, size, quality, and output format settings, then store the returned request id if you are using the queue API.
- 3
Poll the status endpoint or rely on the client subscribe helper until the request reaches COMPLETED.
- 4
Read the final output URLs and usage metadata, then pass the finished assets into your own review, storage, or publishing workflow.
What Makes It Different
What makes the GPT Image 2 API different
This section is laid out to read more like a product narrative than a feature list. Each row shows a capability, why it matters, and what that looks like in a real workflow.
Pixel-perfect typography
That makes it more useful for infographics, packaging mockups, landing page banners, UI screenshots, posters, and ad creative where readable text matters.
Capability
Pixel-perfect typography
GPT Image 2 is unusually strong at dense text, small labels, and text-forward layouts compared with typical image generation models.
That makes it more useful for infographics, packaging mockups, landing page banners, UI screenshots, posters, and ad creative where readable text matters.
Example scenario
A product marketing team generates launch visuals with exact headline copy, pricing callouts, and feature bullets that remain legible in the first pass.
Capability
Photoreal image quality
The model is positioned by fal.ai as a quality-first image model with strong realism, material rendering, and lighting fidelity.
Developers can use it for customer-facing product imagery, campaign stills, editorial scenes, or polished concept visuals without defaulting to stylized output.
Example scenario
An ecommerce workflow produces higher-fidelity product hero shots for PDP experiments without scheduling a fresh studio shoot.
Photoreal image quality
Developers can use it for customer-facing product imagery, campaign stills, editorial scenes, or polished concept visuals without defaulting to stylized output.
Editing and mask support
This is especially useful for background swaps, cleanup, product variation tests, ad localization, and iterative asset refinement.
Capability
Editing and mask support
The edit endpoint supports image URLs, optional masks, and preserve/change instructions for finer-grained revisions.
This is especially useful for background swaps, cleanup, product variation tests, ad localization, and iterative asset refinement.
Example scenario
A design ops tool lets a team preserve a bottle label while changing only the background, lighting mood, and prop styling.
Capability
Flexible production sizing
fal.ai documents preset image sizes plus custom dimensions up to 4K-class outputs, with width/height rules for production-safe rendering.
Teams can use one model family across square catalog assets, portrait ads, landscape landing pages, and larger campaign exports.
Example scenario
A growth team generates portrait social ads, square app store artwork, and landscape hero banners from one shared prompt system.
Flexible production sizing
Teams can use one model family across square catalog assets, portrait ads, landscape landing pages, and larger campaign exports.
Unified API Platform
Two API tiers for different use cases
Pick the right balance of quality, speed, and cost for your workflow. The section stays data-driven, but the presentation is closer to a clean product comparison table.
| Feature | Low | MediumRecommended | High |
|---|---|---|---|
| Best for | Fast prompt iteration and idea finding | Balanced quality and cost | Best photorealism and typography |
| Speed | Fastest | Moderate | Slowest |
| Quality | Best for rough creative loops | Good for many product-facing assets | Highest fidelity |
| Cost | Lowest | Mid-tier | Highest |
| Recommended use | Prompt tuning, early mockups, and workflows where you plan to rerender the winners | Editorial images, marketing drafts, and regular production use when you want better fidelity without the highest cost | Final campaign exports, text-heavy layouts, premium product stills, and assets where details must hold up under review |
| API endpoints | fal-ai/gpt-image-2, openai/gpt-image-2/edit | fal-ai/gpt-image-2, openai/gpt-image-2/edit | fal-ai/gpt-image-2, openai/gpt-image-2/edit |
Use Cases
Industries using the GPT Image 2 API
This section keeps the same reusable data model, but the presentation is closer to a grid of industry cards instead of long narrative boxes.
Content teams, publishers, and marketing designers
Infographics and educational visuals
Generate diagrams, explainers, posters, and information-dense layouts with readable text and deliberate hierarchy.
GPT Image 2 is a better fit than many image models when text is part of the artifact instead of an afterthought.
Merchandising teams and product marketers
Ecommerce product photography
Produce clean packshots, styled product scenes, and label-accurate launch visuals for catalog and campaign workflows.
The model is positioned for photoreal quality and brand-consistent product imagery, which matters for commercial assets.
Design systems teams and product designers
UI mockups and design comps
Create interface mockups, dashboard concepts, and marketing screenshots with clearer layout and text behavior.
Readable buttons, chips, headings, and interface labels make the model much more usable for product-oriented design ideation.
Creative ops teams and asset production pipelines
Image editing and cleanup workflows
Update backgrounds, remove clutter, localize copy, preserve products, or change only selected regions with masks.
The edit endpoint gives you preserve/change control instead of forcing every asset through a full regeneration pass.
Paid media teams and performance marketers
Ad creative and localization
Generate variant campaigns with exact headlines, offer text, and different aspect ratios for channels and regions.
Text rendering plus flexible sizing reduces the number of manual resize and redesign steps after generation.
Brand teams, agencies, and packaging designers
Brand packaging and label visualization
Prototype product labels, bottle art, box fronts, and shelf-ready concepts with readable brand and ingredient text.
This is one of the clearest cases where typography quality and photoreal rendering need to work together in one output.
Examples
GPT Image 2 API examples
This section supports future video or image previews, but it also renders cleanly from prompt-only data. That keeps the template reusable even when a page launches before media assets are ready.
Scientific infographic
Text-heavy generation
Use a structured prompt when you need diagrams, labels, and compact copy to survive the first render without broken typography.
A scientific infographic explaining battery chemistry with labeled cell layers, molecular callouts, clean arrows, white background, precise hierarchy, readable small text, and a polished editorial style.
UI homepage mockup
UI generation
A strong fit for interface mockups when buttons, navigation labels, and repeated text elements need to look coherent.
A pixel-perfect video platform homepage mockup with left sidebar, top navigation, category chips, thumbnail grid, realistic labels, and clean spacing throughout the interface.
Brand product shot
Product photography
Good for premium ecommerce or launch creative where the packaging text and material finish both matter.
A premium product photo of a black perfume bottle on wet stone with silver label text, subtle mist, realistic reflections, controlled studio lighting, and a luxury campaign aesthetic.
Storefront cleanup edit
Preserve-and-change edit
This is the kind of scoped edit where the preserve list keeps the model from drifting outside the requested change.
Remove every advertising sign and poster from the storefront windows. Preserve the awning, brick facade, mullions, reflections, sidewalk, and all people exactly. Reconstruct the glass naturally with no ghosting or adhesive marks.
How To Use This API
How to use GPT Image 2 API
This quick-start walkthrough is written to rank for integration-style searches while staying concise enough for busy developers and operators.
- 1
Create your fal.ai account
Set up a fal.ai account so you can access the playground, model endpoints, and your API key from the dashboard.
- 2
Store your FAL_KEY securely
Set `FAL_KEY` in your server environment and avoid exposing it from client-side code or public browser sessions.
- 3
Choose generate or edit mode
Use the core GPT Image 2 endpoint for new images and the edit endpoint when your workflow starts from one or more reference images.
- 4
Set size, quality, and format
Pick a preset or custom size, choose low/medium/high quality based on budget and fidelity needs, and return the asset as JPEG, PNG, or WebP.
- 5
Use the queue for production flows
Submit requests to the queue, keep the returned request id, and poll status or results when you need durable asynchronous handling.
FAQ
Frequently asked questions about GPT Image 2 API
FAQs stay compact and skimmable here. The content is still data-driven for SEO, but the layout is cleaner and less visually heavy.
What is GPT Image 2 API?
It is fal.ai's hosted API surface for OpenAI's GPT Image 2 model family, covering text-to-image generation and image editing workflows.
What is GPT Image 2 especially good at?
fal.ai positions GPT Image 2 around three standout strengths: stronger text rendering, high-fidelity photorealism, and more reliable product photography with readable labels and packaging.
Does GPT Image 2 support image editing?
Yes. fal.ai documents a companion edit endpoint for image-to-image workflows, including reference images, auto sizing from the input, and optional mask-based changes.
What sizes are supported?
fal.ai documents both preset sizes and custom dimensions up to 4K-class outputs, with width and height constrained to multiples of 16 and aspect-ratio limits for valid requests.
What output formats can I request?
The documented output formats are JPEG, PNG, and WebP, so you can choose based on asset quality, transparency, or delivery constraints.
How do queue-based requests work?
You submit a request, receive a request id, then use the queue status and result endpoints to track progress and fetch the final image payload once the request is completed.
Can I use GPT Image 2 for commercial work?
The fal.ai model pages indicate commercial use is supported, but you should still verify your own legal, brand, and platform requirements before publishing assets at scale.
Where can I find prompting guidance?
fal.ai publishes a dedicated prompting guide for GPT Image 2 covering generation, editing, preserve/change logic, and structured prompt templates.
Model Directory
Browse the full model market before you choose your route.
Use the `/models` catalog to scan providers, modalities, reasoning support, context windows, and pricing metadata from a local OpenRouter snapshot. It is the fastest way to compare what exists before you decide which models should be prioritized on ImaRouter.
Get Started
Start with GPT Image 2 on fal.ai, then productionize the workflow you validate
Use the playground to test prompts, move into the text-to-image or edit API when the workflow is stable, and adopt queue polling when you need request ids, status visibility, and durable async handling.