GPT Image 1.5

Overview

GPT-Image-1.5 is OpenAI’s latest text-to-image and image editing model, designed to generate high-quality visuals with strong prompt understanding, reliable text rendering, and improved real-world grounding. It is built to handle both creative image generation and precise visual edits, making it suitable for a wide range of product, design, and content workflows.

Compared to earlier OpenAI image models, GPT-Image-1.5 places a stronger emphasis on prompt fidelity, readable text inside images, and controlled composition. It is particularly effective for generating images that include labels, UI-like layouts, posters, diagrams, and other structured visuals where accuracy matters as much as aesthetics.

How it Works

GPT-Image-1.5 uses a multimodal architecture that tightly integrates language understanding with visual generation and editing capabilities.

Prompt Interpretation

The model processes natural language prompts to understand subject matter, layout, stylistic intent, and textual content. It performs well with longer, more explicit prompts that describe structure, positioning, and visual hierarchy.

Image Generation

GPT-Image-1.5 generates images with clean composition, consistent proportions, and improved handling of fine details. It supports a wide range of visual styles, from photorealistic imagery to illustrations and graphic-style outputs.

Image Editing & Transformation

The model supports image-to-image workflows, including inpainting and targeted edits. This allows users to modify specific regions of an image, adjust content, or introduce new elements while preserving the surrounding context.

Text-Aware Rendering

A key strength of GPT-Image-1.5 is its ability to generate readable, well-placed text inside images. This makes it well suited for visuals that combine imagery and typography, such as posters, product mockups, diagrams, and UI-style compositions.

Key Features

• Strong Prompt Adherence
  Accurately follows detailed instructions around layout, composition, and visual constraints.

• Improved Text Rendering
  Generates legible text inside images with better placement and consistency than earlier models.

• Image Editing Support
  Enables controlled edits through inpainting and image-to-image transformations.

• Versatile Visual Styles
  Handles photorealistic, illustrative, and graphic styles within a single model.

• Grounded Visual Outputs
  Produces images that can reflect real-world entities and concepts when described clearly in the prompt.

Technical Specifications

• Model Name: GPT-Image-1.5
• Model Type: Text-to-image and image editing
• Input: Text prompt with optional input image
• Editing Capabilities: Inpainting and image-to-image transformations
• Text Rendering: Optimised for readable, structured text within images
• Use Cases: Generation, editing, layout-driven visuals, mixed text-image content

How to Use

1. Write a prompt describing the visual content, layout, and any text that should appear in the image.
2. Optionally provide an input image if you want to edit or transform existing visuals.
3. Submit the request using the GPT-Image-1.5 model.
4. Review the generated image and iterate on the prompt or inputs as needed.

Example prompt:
Design a clean product announcement poster featuring a modern smartphone on a neutral background. Include the product name at the top in bold text, a short tagline beneath it, and three small feature labels arranged neatly below the image. Use soft lighting and a minimal colour palette.

Tips for Better Results

• Be explicit about text placement, font style, and hierarchy when generating images with typography.
• Describe layout and alignment clearly to reduce ambiguity.
• Use image editing features for incremental refinements rather than regenerating from scratch.
• Keep prompts structured when combining multiple visual requirements.

Notes & Limitations

• Complex layouts benefit from clear, step-by-step prompt descriptions.
• While text rendering is improved, very dense or small typography may still require iteration.
• Editing accuracy depends on the clarity of the input image and the defined edit region.

Documentation

You can find full usage details, parameters, and examples here: https://runware.ai/docs/en/providers/openai