Skip to main content

​
Common Params

LLMGrid supports OpenAI-compatible image generation and forwards provider-specific fields when applicable. Base URL: https://api.llmgrid.ai/v1
Endpoint: POST /images/generations
Auth: Authorization: Bearer <LLMGRID_API_KEY>

​
Usage

import os
from openai import OpenAI

# set env variables
os.environ["LLMGRID_API_KEY"] = "your-llmgrid-virtual-key"

client = OpenAI(
  api_key=os.environ["LLMGRID_API_KEY"],
  base_url="https://api.llmgrid.ai/v1"
)

response = client.images.generate(
  model="gpt-image-1",
  prompt="A modern retail storefront with colorful product displays, high detail, soft lighting",
  size="1024x1024",
  n=1,
  response_format="url"
)

print(response.data[0].url)
cURL 

curl -X POST "https://api.llmgrid.ai/v1/images/generations" \
  -H "Authorization: Bearer $LLMGRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-1",
    "prompt": "A modern retail storefront with colorful product displays, high detail, soft lighting",
    "n": 1,
    "size": "1024x1024",
    "response_format": "url"
  }'
Input Params 
  def image_generation(
    prompt: str,
    # Optional OpenAI-compatible params
    model: str | None = None,
    n: int | None = None,
    size: str | None = None,
    quality: str | None = None,
    style: str | None = None,
    response_format: str | None = None,  # "url" or "b64_json"
    user: str | None = None,
    timeout: int | float | None = None,
    # Optional gateway/provider passthrough
    **kwargs,
):
    ...

​
Required Fields

  • prompt: string — Text description of the image you want to generate.

​
Optional Fields

  • model: string (optional) — Image model to use (depends on your enabled providers/models).
  • n: integer (optional) — Number of images to generate (commonly 1–10, model-dependent).
  • size: string (optional) — Output dimensions (model-dependent).
    • Common values: 1024x1024, plus portrait/landscape variants if supported.
  • quality: string (optional) — Output quality (supported values vary by model/provider).
  • style: string (optional) — Style hint (if supported).
  • response_format: string (optional) — Output format:
    • "url" → returned data[].url
    • "b64_json" → returned data[].b64_json (base64-encoded image)
  • user: string (optional) — End-user identifier for tracking and governance.
  • timeout: number (optional) — Request timeout (seconds).
:::note Any non-standard parameters (fields not part of the OpenAI-compatible schema) are treated as provider-specific and forwarded in the request body as-is. :::

​
Response

A typical response includes a data array containing either url or b64_json depending on response_format.

​
URL Response Example

{
  "created": 1710000000,
  "data": [
    {
      "url": "https://..."
    }
  ]
}

​
Base64 Response Example (b64_json)

{
  "created": 1710000000,
  "data": [
    {
      "b64_json": "iVBORw0KGgoAAA..."
    }
  ]
}

​
Decode b64_json (Python)

import base64

b64 = response.data[0].b64_json
img_bytes = base64.b64decode(b64)

with open("output.png", "wb") as f:
    f.write(img_bytes)

​
Prompt Tips

  • Be explicit about subject + environment + style + lighting + composition
  • Add constraints like camera angle, color palette, branding, or background
  • If output needs to be consistent, specify same style and repeatable structure
Example prompt
Text
A high-resolution product hero shot of a premium tea box on a wooden table,
soft natural light from the left, shallow depth of field, clean background,
photorealistic, studio quality

​
Common Errors

  • 400 — Invalid payload / unsupported params for chosen model/provider
  • 401 — Missing/invalid Virtual Key
  • 403 — Key lacks access to the model/provider
  • 429 — Rate limit / quota exceeded (retry with backoff)
  • 5xx — Transient error (retry with backoff)