Why teams look for one image generation API
Image generation usually starts with a single model. A product team tests one prompt, wires one SDK, and ships a small prototype. The problem shows up later, when the product needs more than one output style: fast drafts for internal review, readable text for posters, Chinese text rendering for local campaigns, or photorealistic assets for a product page.
If every model has a different API key, request shape, billing account, and callback pattern, model testing becomes infrastructure work. A simpler pattern is to keep authentication stable and move model selection into the request body.
HiAPI's AI Image APIs page is the main comparison page for this workflow. Use it to decide which image model belongs in the product, then use each model page for exact parameters and examples.
Use one server-side API key
The API key should live in your backend, not in browser code. Your frontend can collect a prompt, an image, or generation settings, but the backend should create the HiAPI task.
Create or rotate the key from API Keys. In production, keep it in your server environment and attach it as an Authorization header:
Authorization: Bearer YOUR_API_KEY
This gives you one place to rotate credentials, one place to add rate limits, and one billing surface to inspect when a generation workflow gets more traffic.
Route by model id, not by provider client
The key implementation detail is to keep the endpoint stable and treat the model id as a routing parameter. Your app can expose presets such as "draft", "poster", "product image", or "photorealistic", then map those presets to a model id on the backend.
For example, a product might start with Nano Banana for fast creative exploration, use GPT Image 2 when the image needs readable text or a layout-heavy prompt, route Chinese social graphics to Qwen Image 2.0, and use FLUX 1.1 Pro for commercial-looking product visuals.
The application code does not need four auth flows to support that. It needs one task creation path and a small routing layer.
Minimal request pattern
Here is a compact request for GPT Image 2 through the unified task endpoint:
curl -X POST https://api.hiapi.ai/v1/tasks \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-image-2/text-to-image",
"input": {
"prompt": "A premium ecommerce hero image for a smart lamp, clean studio lighting",
"aspect_ratio": "16:9",
"resolution": "1K"
}
}'
For exact GPT Image 2 fields, use the GPT Image 2 API docs. For model selection, start from the AI Image APIs comparison.
Production flow with callbacks
Polling is convenient during local testing, but production traffic usually benefits from callbacks. When your backend creates a task, pass a callback URL and treat the callback as the source of terminal status. You can still keep task polling as a fallback for reconciliation.
The practical flow is:
- Your frontend sends prompt and generation settings to your backend.
- Your backend validates the request, chooses a model id, and creates a HiAPI task.
- HiAPI returns a task id immediately.
- Your backend stores the task id and waits for the final callback.
- Your app displays the output URL after the task reaches a terminal state.
This keeps user-facing pages responsive and avoids aggressive polling loops that can become noisy as generation volume grows.
Pricing workflow without hard-coded prices
Do not copy static price tables into application code or old internal docs. The safer workflow is to route users and operators to live pricing, then track usage inside your own product.
For cost-aware image generation, use a lower-cost model while prompts are changing. Increase resolution only after the prompt is stable. Move final brand or product assets to a stronger model when the output needs cleaner text, stricter layout, or more commercial polish.
That pattern keeps experimentation cheap while still giving your product a high-quality final path.
Internal links to keep the SEO path clear
This article is meant to support the image API hub, not replace it. The canonical comparison page is Best AI Image Generation APIs. The model-specific implementation page is GPT Image 2 API. The conversion pages are GPT Image 2 on HiAPI, Pricing, and API Keys.
For adjacent workflows, read Text-to-Video vs Image-to-Video API Workflow and GPT Image 2 Pricing Drivers and Production Examples.
FAQ
Can one API key call multiple image models?
Yes. On HiAPI, the same API key can create tasks for multiple image models. Change the model id in the request body to route to a different image model.
Should I expose the API key in a browser app?
No. Keep the key server-side. Browser code should call your backend, and your backend should create the HiAPI task.
Which image model should I start with?
Start with the model that matches the job. Nano Banana is useful for fast ideation, GPT Image 2 is a strong default for long prompts and readable text, Qwen Image 2.0 is useful for Chinese text rendering, and FLUX 1.1 Pro is better for photorealistic product visuals.
Where should pricing be checked?
Use the HiAPI Pricing page for current live pricing. Avoid copying fixed prices into articles or product code unless you have a maintenance process for updating them.
