openai/gpt-image-2.5-flare-developer/text-to-image

text-to-image

DEV

GPT Image 2.5 Flare Developer Text-to-Image API by OpenAI

openai/gpt-image-2.5-flare-developer/text-to-image

Text-to-image

GPT Image 2.5 Flare Developer generates images from natural-language prompts at a flat per-image price by 1K / 2K / 4K resolution tier, with no token accounting to reason about. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Compare models

GPT Image 2.5 Flare Developer Text-to-Image is developed by OpenAI. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Size

Aspect ratio locked: changing one side scales the other proportionally. Click the lock to edit freely.

Background

Output format

Number

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost $0.03 per run. For $10 you can run this model approximately 333 times.This model is billed per image; generating a 1k image costs $0.03, a 2k image costs $0.05, and a 4k image costs $0.08.

Here's what you can do next:

Image-to-Video Image-to-Image

Parameters

Code Example
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "openai/gpt-image-2.5-flare-developer/text-to-image",  # Required. Model name. options: openai/gpt-image-2.5-flare-developer/text-to-image
    "prompt": "A neon-lit ramen stall on a rainy Tokyo street, cinematic wide shot.",  # Required. Text description of the desired image
    "size": "1024x1024",  # optional string or auto or 1024x1024 or 1536x1024 or 5 moreThe size ofthe generated images
    "background": "auto",  # Background of the generated image. options: auto | opaque | transparent
    "output_format": "png",  # Output image format. options: png | jpeg | webp
    "moderation": "auto",  # Content-moderation strictness. options: auto | low
    "n": 1,  # Number of images to generate, 1 to 10. (min: 1, max: 10)
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] == "completed":
            print("Generated image:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

image_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateImage

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "openai/gpt-image-2.5-flare-developer/text-to-image",
    "prompt": "A beautiful landscape with mountains and lake"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Response

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 7Required: 2Optional: 5

modelstringrequired

Model name.

Default: "openai/gpt-image-2.5-flare-developer/text-to-image"

openai/gpt-image-2.5-flare-developer/text-to-image

promptstringrequired

Text description of the desired image. Up to 32000 characters.

Default: "A neon-lit ramen stall on a rainy Tokyo street, cinematic wide shot."

sizestring

optional string or auto or 1024x1024 or 1536x1024 or 5 moreThe size ofthe generated images.For gpt-image-2.5 is supported as WIDTHxHEIGHT strings,for example 1536x864 . Width and height mustboth be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1.Resolutions above2560x1440 are experimental, and the maximum supported resolution is 3840x2160 .The requesteosize must also satisfy the model's current pixel and edge limits.The standard sizes 1024x1024.1536x1024 ,and 1024x1536 are supported by the GPT image models; auto is supported for modelsthat allow automatic sizing.For dall-e-2,use oneof 256x256,512x512,or 1024x1024.Fordall-e-3,use oneof 1024x1024,1792x1024,or 1024x1792.

Default: "1024x1024"

auto1024x10241024x768768x10241024x15361536x10242048x20482048x11521152x20482560x10881088x25602880x21602160x28803840x21602160x3840

backgroundstring

Background of the generated image. When using transparent, set the output format to png or webp.

Default: "auto"

autoopaquetransparent

output_formatstring

Output image format.

Default: "png"

pngjpegwebp

moderationstring

Content-moderation strictness. low applies less restrictive filtering.

Default: "auto"

autolow

ninteger

Number of images to generate, 1 to 10. Each image is billed separately.

Default: 1Min: 1Max: 10

Example Request Body

{
  "model": "openai/gpt-image-2.5-flare-developer/text-to-image",
  "prompt": "A neon-lit ramen stall on a rainy Tokyo street, cinematic wide shot.",
  "size": "1024x1024",
  "background": "auto",
  "output_format": "png",
  "moderation": "auto",
  "n": 1
}

Output Schema

The API returns a prediction response with the generated output URLs.

idstring

Unique identifier for the prediction.

modelstring

Model ID used for the prediction.

statusstring

Status of the task: created, processing, completed, or failed.

outputsarray

Array of URLs to the generated images. Empty when status is not completed.

created_atstring

ISO timestamp of when the request was created.

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

export ATLASCLOUD_API_KEY="your-api-key-here"

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "GPT Image 2.5 Flare Developer text-to-image (OpenAI Images API)."
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    }
  },
  "components": {
    "schemas": {
      "Input": {
        "type": "object",
        "required": [
          "model",
          "prompt"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "openai/gpt-image-2.5-flare-developer/text-to-image",
            "enum": [
              "openai/gpt-image-2.5-flare-developer/text-to-image"
            ]
          },
          "prompt": {
            "type": "string",
            "maxLength": 32000,
            "default": "A neon-lit ramen stall on a rainy Tokyo street, cinematic wide shot.",
            "description": "Text description of the desired image. Up to 32000 characters."
          },
          "size": {
            "default": "1024x1024",
            "description": "optional string or auto or 1024x1024 or 1536x1024 or 5 moreThe size ofthe generated images.For gpt-image-2.5 is supported as WIDTHxHEIGHT strings,for example 1536x864 . Width and height mustboth be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1.Resolutions above2560x1440 are experimental, and the maximum supported resolution is 3840x2160 .The requesteosize must also satisfy the model's current pixel and edge limits.The standard sizes 1024x1024.1536x1024 ,and 1024x1536 are supported by the GPT image models; auto is supported for modelsthat allow automatic sizing.For dall-e-2,use oneof 256x256,512x512,or 1024x1024.Fordall-e-3,use oneof 1024x1024,1792x1024,or 1024x1792.",
            "enum": [
              "auto",
              "1024x1024",
              "1024x768",
              "768x1024",
              "1024x1536",
              "1536x1024",
              "2048x2048",
              "2048x1152",
              "1152x2048",
              "2560x1088",
              "1088x2560",
              "2880x2160",
              "2160x2880",
              "3840x2160",
              "2160x3840"
            ],
            "type": "string"
          },
          "background": {
            "type": "string",
            "default": "auto",
            "enum": [
              "auto",
              "opaque",
              "transparent"
            ],
            "description": "Background of the generated image. When using transparent, set the output format to png or webp.",
            "x-ui-component": "select"
          },
          "output_format": {
            "type": "string",
            "default": "png",
            "enum": [
              "png",
              "jpeg",
              "webp"
            ],
            "description": "Output image format.",
            "x-ui-component": "select"
          },
          "moderation": {
            "type": "string",
            "default": "auto",
            "enum": [
              "auto",
              "low"
            ],
            "description": "Content-moderation strictness. low applies less restrictive filtering.",
            "x-ui-component": "select"
          },
          "n": {
            "type": "integer",
            "minimum": 1,
            "maximum": 10,
            "default": 1,
            "description": "Number of images to generate, 1 to 10. Each image is billed separately."
          }
        },
        "x-order-properties": [
          "model",
          "prompt",
          "size",
          "background",
          "output_format",
          "n"
        ],
        "allOf": [
          {
            "if": {
              "properties": {
                "background": {
                  "const": "transparent"
                }
              },
              "required": [
                "background"
              ]
            },
            "then": {
              "properties": {
                "output_format": {
                  "enum": [
                    "png",
                    "webp"
                  ]
                }
              }
            }
          }
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "description": "Unique identifier for the prediction."
          },
          "urls": {
            "type": "object",
            "description": "Object containing related API endpoints."
          },
          "model": {
            "type": "string",
            "description": "Model ID used for the prediction."
          },
          "status": {
            "type": "string",
            "description": "Status of the task: created, processing, completed, or failed."
          },
          "outputs": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "description": "Array of URLs to the generated images. Empty when status is not completed."
          },
          "created_at": {
            "type": "string",
            "format": "date-time",
            "description": "ISO timestamp of when the request was created."
          }
        }
      }
    }
  }
}

LLM-Friendly Prompt Template

# openai/gpt-image-2.5-flare-developer/text-to-image

> GPT Image 2.5 Flare Developer generates images from natural-language prompts at a flat per-image price by 1K / 2K / 4K resolution tier, with no token accounting to reason about. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `openai/gpt-image-2.5-flare-developer/text-to-image`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  Model name.
  - Default: `"openai/gpt-image-2.5-flare-developer/text-to-image"`
  - Options: "openai/gpt-image-2.5-flare-developer/text-to-image"

- **`prompt`** (`string`, _required_):
  Text description of the desired image. Up to 32000 characters.
  - Default: `"A neon-lit ramen stall on a rainy Tokyo street, cinematic wide shot."`

- **`size`** (`string`, _optional_):
  optional string or auto or 1024x1024 or 1536x1024 or 5 moreThe size ofthe generated images.For gpt-image-2.5 is supported as WIDTHxHEIGHT strings,for example 1536x864 . Width and height mustboth be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1.Resolutions above2560x1440 are experimental, and the maximum supported resolution is 3840x2160 .The requesteosize must also satisfy the model's current pixel and edge limits.The standard sizes 1024x1024.1536x1024 ,and 1024x1536 are supported by the GPT image models; auto is supported for modelsthat allow automatic sizing.For dall-e-2,use oneof 256x256,512x512,or 1024x1024.Fordall-e-3,use oneof 1024x1024,1792x1024,or 1024x1792.
  - Default: `"1024x1024"`
  - Options: "auto", "1024x1024", "1024x768", "768x1024", "1024x1536", "1536x1024", "2048x2048", "2048x1152", "1152x2048", "2560x1088", "1088x2560", "2880x2160", "2160x2880", "3840x2160", "2160x3840"

- **`background`** (`string`, _optional_):
  Background of the generated image. When using transparent, set the output format to png or webp.
  - Default: `"auto"`
  - Options: "auto", "opaque", "transparent"

- **`output_format`** (`string`, _optional_):
  Output image format.
  - Default: `"png"`
  - Options: "png", "jpeg", "webp"

- **`n`** (`integer`, _optional_):
  Number of images to generate, 1 to 10. Each image is billed separately.
  - Default: `1`
  - Min: 1
  - Max: 10



**Required Parameters Example**:

```json
{
  "model": "openai/gpt-image-2.5-flare-developer/text-to-image",
  "prompt": "A neon-lit ramen stall on a rainy Tokyo street, cinematic wide shot."
}
```


**Full Example**:

```json
{
  "model": "openai/gpt-image-2.5-flare-developer/text-to-image",
  "prompt": "A neon-lit ramen stall on a rainy Tokyo street, cinematic wide shot.",
  "size": "1024x1024",
  "background": "auto",
  "output_format": "png",
  "n": 1
}
```


### Output Schema

The API returns the following output format:


- **`id`** (`string`, _optional_):
  Unique identifier for the prediction.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated images. Empty when status is not completed.

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.



**Example Response**:

```json
{
  "id": "",
  "urls": {},
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "created_at": ""
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "openai/gpt-image-2.5-flare-developer/text-to-image",
  "prompt": "A neon-lit ramen stall on a rainy Tokyo street, cinematic wide shot.",
  "size": "1024x1024",
  "background": "auto",
  "output_format": "png",
  "n": 1
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/openai/gpt-image-2.5-flare-developer/text-to-image)

A futuristic megacity emerging from the darkness at sunrise, enormous geometric skyscrapers covered in glass and vegetation, thousands of tiny people beginning their morning journey below, warm sunlight slowly illuminating the buildings from the horizon upward, flying vehicles moving quietly through the sky, hopeful vision of tomorrow, cinematic science fiction, monumental architecture, atmospheric depth, photorealistic, 70mm cinematography, 8K.