# GPT-Image 1.5 > GPT Image 1.5 generates high-fidelity images with strong prompt adherence, preserving composition, lighting, and fine-grained detail. ## Overview - **Endpoint**: `https://fal.run/fal-ai/gpt-image-1.5/edit` - **Model ID**: `fal-ai/gpt-image-1.5/edit` - **Category**: image-to-image - **Kind**: inference **Tags**: openai, gpt-image ## Pricing Your request will cost different amounts based on the number of images, quality, and size. - You will be charged $0.005 per 1,000 input text tokens. One word is roughly 4 tokens. - You will be charged $0.008 per 1,000 input image tokens. One 1024x1024 image is roughly 135 tokens in low fidelity mode, or 3,050 tokens in high fidelity mode. - You will be charged $0.010 per 1,000 output text tokens. The model will consume tokens reasoning about your prompt based on it's complexity. - For **low** quality, you will be charged $0.009 for 1024x1024 or $0.013 for any other size *per image*. - For **medium** quality, you will be charged $0.034 for 1024x1024, $0.051 for 1024x1536 and $0.050 for 1536x1024 *per image*. - For **high** quality, you will be charged $0.133 for 1024x1024, $0.200 for 1024x1536 or $0.199 for 1536x1024 *per image*. For more details, see [fal.ai pricing](https://fal.ai/pricing). ## 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: - **`prompt`** (`string`, _required_): The prompt for image generation - Examples: "Same workers, same beam, same lunch boxes - but they're all on their phones now. One is taking a selfie. One is on a call looking annoyed. Same danger, new priorities. A hard hat has AirPods." - **`image_urls`** (`list`, _required_): The URLs of the images to use as a reference for the generation. - Array of string - Examples: ["https://v3b.fal.media/files/b/0a8691af/9Se_1_VX1wzTjjTOpWbs9_bb39c2eb-1a41-4749-b1d0-cf134abc8bbf.png"] - **`image_size`** (`ImageSizeEnum`, _optional_): Aspect ratio for the generated image Default value: `"auto"` - Default: `"auto"` - Options: `"auto"`, `"1024x1024"`, `"1536x1024"`, `"1024x1536"` - **`background`** (`BackgroundEnum`, _optional_): Background for the generated image Default value: `"auto"` - Default: `"auto"` - Options: `"auto"`, `"transparent"`, `"opaque"` - **`quality`** (`QualityEnum`, _optional_): Quality for the generated image Default value: `"high"` - Default: `"high"` - Options: `"low"`, `"medium"`, `"high"` - **`input_fidelity`** (`InputFidelityEnum`, _optional_): Input fidelity for the generated image Default value: `"high"` - Default: `"high"` - Options: `"low"`, `"high"` - **`num_images`** (`integer`, _optional_): Number of images to generate Default value: `1` - Default: `1` - Range: `1` to `4` - Examples: 1 - **`output_format`** (`OutputFormatEnum`, _optional_): Output format for the images Default value: `"png"` - Default: `"png"` - Options: `"jpeg"`, `"png"`, `"webp"` - **`sync_mode`** (`boolean`, _optional_): If `True`, the media will be returned as a data URI and the output data won't be available in the request history. - Default: `false` - **`mask_image_url`** (`string`, _optional_): The URL of the mask image to use for the generation. This indicates what part of the image to edit. **Required Parameters Example**: ```json { "prompt": "Same workers, same beam, same lunch boxes - but they're all on their phones now. One is taking a selfie. One is on a call looking annoyed. Same danger, new priorities. A hard hat has AirPods.", "image_urls": [ "https://v3b.fal.media/files/b/0a8691af/9Se_1_VX1wzTjjTOpWbs9_bb39c2eb-1a41-4749-b1d0-cf134abc8bbf.png" ] } ``` **Full Example**: ```json { "prompt": "Same workers, same beam, same lunch boxes - but they're all on their phones now. One is taking a selfie. One is on a call looking annoyed. Same danger, new priorities. A hard hat has AirPods.", "image_urls": [ "https://v3b.fal.media/files/b/0a8691af/9Se_1_VX1wzTjjTOpWbs9_bb39c2eb-1a41-4749-b1d0-cf134abc8bbf.png" ], "image_size": "auto", "background": "auto", "quality": "high", "input_fidelity": "high", "num_images": 1, "output_format": "png" } ``` ### Output Schema The API returns the following output format: - **`images`** (`list`, _required_): The generated images. - Array of ImageFile - Examples: [{"width":1024,"file_name":"yUt7tifLSbg1WzWWgfj2o.png","content_type":"image/png","url":"https://v3b.fal.media/files/b/0a8691b0/yUt7tifLSbg1WzWWgfj2o.png","height":1024}] **Example Response**: ```json { "images": [ { "width": 1024, "file_name": "yUt7tifLSbg1WzWWgfj2o.png", "content_type": "image/png", "url": "https://v3b.fal.media/files/b/0a8691b0/yUt7tifLSbg1WzWWgfj2o.png", "height": 1024 } ] } ``` ## Usage Examples ### cURL ```bash curl --request POST \ --url https://fal.run/fal-ai/gpt-image-1.5/edit \ --header "Authorization: Key $FAL_KEY" \ --header "Content-Type: application/json" \ --data '{ "prompt": "Same workers, same beam, same lunch boxes - but they're all on their phones now. One is taking a selfie. One is on a call looking annoyed. Same danger, new priorities. A hard hat has AirPods.", "image_urls": [ "https://v3b.fal.media/files/b/0a8691af/9Se_1_VX1wzTjjTOpWbs9_bb39c2eb-1a41-4749-b1d0-cf134abc8bbf.png" ] }' ``` ### Python Ensure you have the Python client installed: ```bash pip install fal-client ``` Then use the API client to make requests: ```python import fal_client def on_queue_update(update): if isinstance(update, fal_client.InProgress): for log in update.logs: print(log["message"]) result = fal_client.subscribe( "fal-ai/gpt-image-1.5/edit", arguments={ "prompt": "Same workers, same beam, same lunch boxes - but they're all on their phones now. One is taking a selfie. One is on a call looking annoyed. Same danger, new priorities. A hard hat has AirPods.", "image_urls": ["https://v3b.fal.media/files/b/0a8691af/9Se_1_VX1wzTjjTOpWbs9_bb39c2eb-1a41-4749-b1d0-cf134abc8bbf.png"] }, with_logs=True, on_queue_update=on_queue_update, ) print(result) ``` ### JavaScript Ensure you have the JavaScript client installed: ```bash npm install --save @fal-ai/client ``` Then use the API client to make requests: ```javascript import { fal } from "@fal-ai/client"; const result = await fal.subscribe("fal-ai/gpt-image-1.5/edit", { input: { prompt: "Same workers, same beam, same lunch boxes - but they're all on their phones now. One is taking a selfie. One is on a call looking annoyed. Same danger, new priorities. A hard hat has AirPods.", image_urls: ["https://v3b.fal.media/files/b/0a8691af/9Se_1_VX1wzTjjTOpWbs9_bb39c2eb-1a41-4749-b1d0-cf134abc8bbf.png"] }, logs: true, onQueueUpdate: (update) => { if (update.status === "IN_PROGRESS") { update.logs.map((log) => log.message).forEach(console.log); } }, }); console.log(result.data); console.log(result.requestId); ``` ## Additional Resources ### Documentation - [Model Playground](https://fal.ai/models/fal-ai/gpt-image-1.5/edit) - [API Documentation](https://fal.ai/models/fal-ai/gpt-image-1.5/edit/api) - [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/gpt-image-1.5/edit) ### fal.ai Platform - [Platform Documentation](https://fal.ai/docs/documentation) - [Python Client](https://fal.ai/docs/api-reference/client-libraries/python) - [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript) ### Other agent-readable surfaces This file covers one model. To find anything else: - [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs - [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page - [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined - Any other model: `https://fal.ai/models//llms.txt`