Introducing ChatGPT Images 2.5

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Sharper details, faster generation, more precise editing, and better tools for creating and sharing.

Every week, people create more than 3 billion images across ChatGPT Images and the GPT‑Image models in the API. Today, we’re expanding what you can create with ChatGPT Images 2.5—our new state-of-the-art image model that brings sharper details, more precise editing, and faster generation to your creative workflows.

Images 2.5 produces more natural lighting and richer textures, is better at preserving the subjects in your reference photos, and follows editing instructions more reliably across multiple turns. We’ve also reduced image generation latency by up to 50% compared with Images 2.0, so you can generate images and refine your concepts more quickly.

In ChatGPT, we’re introducing product features that give you more control over the creative process. Sketch⁠ is a new feature that lets you draw directly in ChatGPT as a reference for your final image. Templates make it easier to start creating across some of the most popular image formats, like flyers and product photos.

You can also now place comments directly on images for more focused editing, and you can share prompts you’ve used to let others try your ideas with their own photos and details.

Images 2.5 is available to all ChatGPT, ChatGPT Work, and Codex users across desktop, mobile, and web.

For developers, we’re introducing two new models in the API. GPT‑Image‑2.5 Flare brings the same improvements in quality, editing, and speed, and GPT‑Image‑2.5 Sunburst offers an extra level of precision for detailed creative work with longer generation times.

The most meaningful images feel grounded in the real-life people, places, and memories they’re based on. Images 2.5 is better at working from reference photos to transform familiar subjects across new settings, visual styles, and compositions. Image subjects look more recognizable, lighting and textures feel more natural, and distinctive features are more likely to carry through.

For teams building with the API, this same fidelity makes reference-led workflows more reliable, so variations can stay anchored to the original source.

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