GPT Image 1.5 creates images from written instructions and edits existing visuals with reference images and an optional mask. Describe the subject, scene, layout and exact words to develop a comic page, a documentary scene or an educational illustration. For editing, identify the object or lettering to change, or assign separate references to a person and destination. Choose square, portrait or landscape pixel dimensions; text generation offers one to four requested images, while the documented editing workflow produces one image per request.
GPT Image 1.5 is developed by OpenAI. Vidgo provides access to the model. Model names and trademarks belong to their respective owners.Choose Text to Image when the prompt should establish the complete scene, panel sequence or labeled illustration. Choose Image Editing when references supply an identity, environment or object to retain. Describe the intended change and fixed details, add a mask when the edit should follow a selected area, then choose the output dimensions.
Generate high-fidelity visuals from text prompts with GPT Image 1.5 Text to Image, featuring precise instruction adherence, in-image typography, and flexible aspect ratios at 2 credits ($0.010) per image.
Perform precision image editing and inpainting with GPT Image 1.5 Edit using reference images and text instructions, preserving subject identity and lighting at 2 credits ($0.010) per image.
Text generation uses prompt, size and n. Editing also requires image_urls containing at least one public image address and can include mask_url for a selected region. Assign each reference a clear role and explain the subject, placement, lighting and wording to preserve. Use n set to 1 for the documented single-output editing workflow.
| Model / task | Input and workflow |
|---|---|
| GPT Image 1.5 Text to Image | Generate high-fidelity visuals from text prompts with GPT Image 1.5 Text to Image, featuring precise instruction adherence, in-image typography, and flexible aspect ratios at 2 credits ($0.010) per image. |
| GPT Image 1.5 Edit | Perform precision image editing and inpainting with GPT Image 1.5 Edit using reference images and text instructions, preserving subject identity and lighting at 2 credits ($0.010) per image. |
The existing records show a four-panel snail story, a radio-studio photograph, a labeled beaver lodge, a changed trail sign and a drawing reinterpreted as a photographed creature. Review the actual outputs to understand how each brief identifies the content, sequence and details to retain.
Write the grid, recurring character, each action and the exact speech bubbles. The snail record asks for a 2×2 page moving from an express letter to a winter delivery. Inspect the character, panel order and bubble spelling before using a result as an illustrated story draft.
Specify the words, surfaces and scene lighting. The radio record includes an ON AIR sign, a four-line index card and dated cassette labels. Inspect each text element with the host, microphone and warm-versus-cool studio light when developing a narrative still.
Write the structure and the exact labels with their intended destinations. The beaver-lodge record asks for five labels, a dry chamber, an underwater entrance and a food cache. Check anatomy, the waterline and label connections before adapting the image for learning material.
Supply the trail-sign photograph and identify the lower plank. The edit replaces SUMMIT 5 KM with SUMMIT CLOSED - SNOW, retains the upper sign and forest, and adds fresh snow to the edges. Compare source and result for spelling, brush texture and the localized weather change.
Provide the drawing and list the design features that define the creature. The edit retains the purple body, orange spots, six legs, antennae and one eye, then requests soft skin and garden contact shadows. Inspect the design and new material treatment against the source.
The first case uses a trail-sign photograph to revise one plank’s wording and add snow while retaining the surrounding scene. The second combines a woman’s identity reference with a hillside apiary, then asks for an inspection pose, preserved hive colors and matching golden-hour light. Read each full prompt and compare all source images with the corresponding result.
Edit only the text on the lower plank: replace "SUMMIT 5 KM" with "SUMMIT CLOSED - SNOW" in the same cream-white hand-brushed capital letters, with the same letter height, stroke texture and slight unevenness, fitted neatly inside the same plank. Leave the upper plank "LAKE TRAIL 2 KM", both arrow shapes, the post, the wood grain, the camera angle and the forest exactly as they are. Then add only a thin layer of fresh snow resting on the top edges of both planks and on top of the post, with a few light snowflakes in the air. No other changes, no extra text, no logos, no watermark.
Combine the two reference images. Place the woman from image 1 into the hillside apiary from image 2. She stands beside the second beehive, holding a honeycomb frame she has just lifted out with both gloved hands and inspecting it closely while a few bees fly around. Keep her face, age, silver hair bun, hazel eyes and white beekeeping jacket exactly as in image 1. Keep the painted hive colors, lavender, stone wall, olive trees and hills from image 2. Relight the whole scene to warm golden-hour sunlight coming from the right, with matching shadows on the ground and a soft rim light on her hair, so she looks naturally photographed there. Realistic documentary photograph, three-quarter view, no text, no logos, no watermark.
Prepare a complete written brief or source images, explain the sequence, exact words, desired changes and preserved details, then choose a suitable output size. Reviewed images can support comic concepts, documentary-style scenes, educational drafts, sign revisions, character development and person-to-location composition. Check factual content, lettering and identity before sharing the finished work.
Write a panel sequence, recurring character and exact dialogue. Generate a page, review the order and lettering, and use the checked image as a draft for a short illustrated story.
Describe the person, environment, light and scene text. Generate a photograph-style composition and inspect anatomy, object placement and wording for a film concept or editorial illustration.
Specify the object’s structure, internal relationships and exact labels. Generate a visual explanation, check factual connections and use the reviewed draft for a lesson or learning page.
Supply the existing image and name the exact text to replace and the surface to retain. Review the edited spelling, texture and layout for a scene update or visual revision proposal.
Provide a sketch and identify its defining shapes, colors and features. Request a new rendering medium or realistic environment and compare the result with the original design for character development.
Provide identity and destination references and describe pose, scale, lighting and contact. Review the combined scene for an illustrated narrative, scene proposal or creative planning draft.
The dynamic table shows the current per-image starting rate for text generation and editing. Text generation accepts n from 1 to 4, and its estimate increases with the requested image count. The editing documentation defines one output image and one-image billing for each request; use n set to 1 for that workflow. Select the intended pixel dimensions and review the task estimate before submitting.
| Model | Provider | Workflow | Input material | Output dimensions | Starting rate |
|---|---|---|---|---|---|
| GPT Image 1.5 Text to Image | OpenAI | Text to Image | Text prompt | 1024x1024 / 1024x1536 / 1536x1024 | $0.01 / image |
| GPT Image 1.5 Edit | OpenAI | Image Editing | 1+ reference images; optional mask | 1024x1024 / 1024x1536 / 1536x1024 | $0.01 / image |
Test your creative brief online, then bring the verified request into your application.
Enter the complete scene or change instructions, including exact words and retained details. Add editing references and an optional mask, choose size and use n=1 for an initial test. Review the estimate and compare the returned image with the brief and every source, including identity, wording and lighting.
Choose a task, inspect its settings and cost, and follow the result from an online test to an authenticated API request.
Choose Text to Image for a complete written composition, or Image Editing when a source defines what to retain or modify. Provide at least one editing image, add a mask if the change needs a selected area and choose 1024x1024, 1024x1536 or 1536x1024. Use the documented output count for the selected task.
Open the matching task, enter your brief and supply the required images. Review the generated result against subject appearance, composition and the requested changes before carrying the workflow into a project.
Use the current per-image rate and the text task’s requested output count to plan generation costs. For the documented single-image editing workflow, set n to 1 and review the displayed estimate. Combine the complete request amount with the planned number of tests for the project budget.
Read the actual prompt and inspect its source images and result together. Use those examples to understand how to assign input roles and describe the details to preserve or change in your own request.
Retain the task ID returned by the API to query status and retrieve completed file URLs. Use your online generation history to review earlier results and the creative direction you tested.
Use the documentation linked from each task for its exact model identifier, accepted inputs, authentication and response format. Integrate the same task you tested online so its media and settings match the request.
GPT Image 1.5 is an OpenAI image model available through text-generation and editing tasks on Vidgo. It accepts written instructions, reference images and optional editing masks, with three pixel dimensions for square, portrait or landscape results. This page explains those inputs, controls, examples and prices.
Explore model introductions, API guides and platform comparisons to understand input preparation, request integration and cost planning for your project.
