GPT Image 2 API Python, Node.js, and curl Examples for Developers

If you want to add image generation to your app quickly, the GPT Image 2 API Python examples below will help you get moving with WisGate’s image generation API. You can also try the model interactively in WisGate AI Studio before wiring it into your code.

Overview of GPT Image 2 API

The GPT Image 2 API gives developers a direct way to generate images from text prompts through WisGate’s unified API. The model name you will use is gpt-image-2, and the request format is simple enough to plug into existing backends, scripts, or server-side jobs. If you are building product features such as marketing graphics, app mockups, concept art, or social preview images, this image generation API fits well because it uses a consistent request shape across tools and languages.

WisGate’s platform is designed as one API for access to multiple AI models, so you do not need to rewrite your whole integration when you move from one model to another. That matters for teams that want to ship quickly and keep their code small. In practice, the GPT Image 2 code example approach is straightforward: send a prompt, choose a size, and ask for one or more images. For developers, that means fewer moving parts and less setup time.

Model Specifications and Use Cases

The model ID is exactly gpt-image-2. For a common starting point, use the size 1024x1024 and set n to 1 when you only need one output image. Those defaults are easy to test and help you keep requests predictable while you validate prompt quality. If you need multiple variations from the same prompt, you can increase n, but that also increases usage per request because you are asking the API to return more generated images.

Typical use cases include hero images for landing pages, concept visuals for product planning, editorial illustrations, ad creatives, and prototype assets for internal tools. The model works best when your prompt is specific about subject, style, colors, composition, and any text that should appear in the image. A short prompt will work, but a more explicit one usually gives you a better starting point. That is especially useful when you want repeatable output for a production feature rather than an ad hoc experiment.

Authentication and API Endpoint Setup

To make a request, you need an API key and a Bearer token in the Authorization header. The required endpoint for image generation is https://api.wisgate.ai/v1/images/generations. The request must also include Content-Type: application/json.

Here is the basic shape to remember:

1. Send your HTTP request to https://api.wisgate.ai/v1/images/generations
2. Include Authorization: Bearer <API_KEY>
3. Set Content-Type: application/json
4. Pass a JSON body with the model and prompt fields, plus n and size when needed

That is all you need for a first call. If you are testing locally, keep your API key out of source control and read it from an environment variable instead. That makes it easier to move the same code between development, staging, and production. The endpoint and headers stay consistent across the examples below, so you can copy the same request logic into your own stack with small changes only.

GPT Image 2 API Examples by Language

The examples below use the same endpoint, headers, and payload structure so you can compare them side by side. The only major difference is the HTTP client syntax in each language. If your team already uses Python for automation, Node.js for server apps, or curl for quick testing, you can adapt the same request body with almost no friction.

The example payload uses model set to gpt-image-2, prompt set to A beautiful sunset, n set to 1, and size set to 1024x1024. That mirrors the background example and gives you a clean baseline to verify that your setup is working before you move to more detailed prompts.

Python Example

Python is a good choice when you want a readable request script or when your app already uses requests for API calls in Python. The snippet below posts JSON to the WisGate endpoint and prints the response so you can inspect the returned image data or URL structure, depending on the API response format.

import os
import requests

url = "https://api.wisgate.ai/v1/images/generations"
api_key = os.environ.get("WISGATE_API_KEY")

headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {api_key}"
}

payload = {
    "model": "gpt-image-2",
    "prompt": "A beautiful sunset",
    "n": 1,
    "size": "1024x1024"
}

response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()

print(response.json())

A few details matter here. First, the Content-Type header tells the API that you are sending JSON. Second, the Authorization header uses the Bearer token format, which is the same structure you will use in every language example. Third, the payload includes all of the fields you need for a basic image generation request. If you want to experiment, change the prompt string first, then compare the output response. That is usually the fastest way to learn how the model reacts to wording changes.

Node.js Example

Node.js works well when your backend already uses JavaScript or TypeScript. The example below uses fetch, which keeps the request compact and easy to read. If your environment uses axios instead, the payload and headers remain the same; only the transport call changes.

const apiKey = process.env.WISGATE_API_KEY;

async function generateImage() {
  const response = await fetch("https://api.wisgate.ai/v1/images/generations", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${apiKey}`
    },
    body: JSON.stringify({
      model: "gpt-image-2",
      prompt: "A beautiful sunset",
      n: 1,
      size: "1024x1024"
    })
  });

  if (!response.ok) {
    throw new Error(`Request failed: ${response.status}`);
  }

  const data = await response.json();
  console.log(data);
}

generateImage().catch(console.error);

This version is useful for web services, job workers, and API routes. You can wrap it in a function, add your own prompt-building logic, and return the generated image data to the frontend. If you are building a product feature that lets users describe an image in plain language, this pattern fits naturally into a form submission or server action. The request is still the same; you are just placing it inside a Node.js flow.

curl Example

curl is the fastest way to verify that your credentials and endpoint are correct. It also makes the request shape very clear, which helps when you are debugging a Python or Node.js implementation. The command below matches the background example exactly and is a reliable starting point for manual testing.

curl -X POST https://api.wisgate.ai/v1/images/generations \
  -H "Authorization: Bearer $WISDOM_GATE_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "A futuristic city skyline at dusk with neon reflections on rain-slicked streets",
    "n": 1,
    "size": "1024x1024",
    "quality": "high"
  }'

When you test with curl, you can swap the prompt and keep the rest of the payload unchanged. That makes it easy to compare results across different prompt versions. If curl returns an authentication error, check the Bearer token value first. If the request reaches the API but fails validation, inspect the JSON body for missing keys or formatting problems.

Understanding Key Request Parameters

The GPT Image 2 API expects a small set of request parameters, and understanding them helps you control both output and usage. The model field selects the image model, so set it to gpt-image-2 exactly. The prompt field contains the description of the image you want. That is where you can be specific about subject matter, style, lighting, composition, background, or mood.

The n field controls the number of images returned. For most development work, n=1 is the easiest default because it keeps the response simple and lowers the amount of generation work per request. If you need several variations from the same prompt, increase n carefully and compare the results before using that pattern in production. The size field sets the image dimensions, such as 1024x1024. Larger or more detailed outputs can affect response time and cost, so start with a smaller or standard size while you iterate.

A practical prompt structure is: subject + style + composition + key details. For example, instead of only saying “a sunset,” try “A beautiful sunset over a calm ocean, cinematic lighting, soft clouds, warm orange and purple tones.” That gives the model more context and usually leads to more predictable results. This is one of the simplest ways to improve image prompt parameters without changing your integration code.

Pricing and Usage Considerations for GPT Image 2 API

WisGate positions its API platform as an affordable routing platform for accessing multiple AI models through one API, which helps teams keep implementation overhead low. The exact pricing figures for GPT Image 2 are not provided here, so the right way to think about cost is through request shape and usage patterns. In other words, the number of images you request and the size you choose both matter.

If you are building a feature that will be used often, start with n=1 and 1024x1024 while you validate the workflow. That gives you a controlled baseline for both performance and cost awareness. Once you have a good prompt and know what quality level you need, you can decide whether higher resolution or multiple outputs are worth the extra usage. That is especially important if your application lets end users generate images on demand.

WisGate AI Studio at https://wisgate.ai/studio/image is a useful place to experiment before you bake the call into a product flow. Testing prompts there can save time because you can refine the wording first, then move the same prompt into your Python, Node.js, or curl request. If you also want to review available model options later, https://wisgate.ai/models is a good reference point for the broader platform. This approach helps you build faster while keeping your API usage more deliberate.

Troubleshooting and Best Practices

The most common issue is an authorization error. When that happens, confirm that your header uses Authorization: Bearer <API_KEY> and that the key is valid. If the server returns a JSON parsing or validation error, check that your request body includes model, prompt, n, and size with the right data types. Strings should be quoted, and n should be a number rather than a string.

Prompt quality is another area where small changes matter. Short prompts can work, but they often leave too much open to interpretation. Add specific details when you want more control. For example, mention color palette, camera angle, aspect feel, or illustration style if those properties matter to the result. If the image comes back but does not match your expectation, change the prompt before changing the code.

A few practical habits also help:

1. Store your API key in an environment variable.
2. Test with curl before moving to application code.
3. Start with n=1 and 1024x1024.
4. Log request IDs or errors when your API wrapper supports it.
5. Keep prompts short at first, then add detail in small steps.

Those habits make debugging easier and keep your integration cleaner. When you are ready to move from testing to implementation, open WisGate for the platform overview, try WisGate AI Studio for hands-on prompt testing, and sign up to get an API key for your GPT Image 2 API Python, Node.js, or curl workflow."