Input image of the urban location
AI Models & Assistants / Knowledge library
GPT-Image-1.5
Our previous image generation model
Why learn GPT-Image-1.5?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text / image inputs.
Where it came from
GPT-Image-1.5: Our previous image generation model Published aliases: gpt-image-1.5-2025-12-16. Input: text, image. Output: image, text. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what GPT-Image-1.5 does.
- Identify a use case for GPT-Image-1.5.
- Describe a limitation of GPT-Image-1.5.
- Name a compatible tool for GPT-Image-1.5.
- Compare GPT-Image-1.5 against an alternative.
Documentation and sources
- Primary source / model card (developers.openai.com)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Architectural Visualization for Urban Planning
Urban planning firms requiring rapid, high-fidelity visual representations of proposed building designs.
The business problem
Manual 3D rendering workflows are prohibitively slow, preventing architects from iterating on design concepts during client meetings.
How this topic solves it
Utilizing GPT-Image-1.5 to generate high-fidelity architectural renderings from descriptive text prompts and existing site photos, enabling real-time visual feedback.
- Prepare site context images and architectural design specifications as input data.
- Submit image-to-image requests to the v1/images/edits endpoint to apply design changes to existing site photos.
- Utilize the gpt-image-1.5-2025-12-16 snapshot to ensure consistent rendering styles across multiple design iterations.
- Review generated outputs for adherence to structural constraints and prompt instructions.
This diagram illustrates the transformation of site data into visual renderings using GPT-Image-1.5.
Model processing input via v1/images/edits
Textual instructions for architectural changes
Final visual representation
- Site Photo → provides context → GPT-Image-1.5
- Design Prompt → defines modifications → GPT-Image-1.5
- GPT-Image-1.5 → generates image → Rendered Output
Expected business outcome
Architects achieve a streamlined design loop that allows for immediate visual exploration of site-specific modifications without waiting for full 3D renders.
Limitations and checks
The model may struggle with precise geometric alignment in complex structural inpainting; verify results by overlaying the output on the original site blueprint to check for perspective drift.