What Is GPT Image 2.5? Sunburst, Flare, Features and First Look
Learn what GPT Image 2.5 is, how Sunburst and Flare differ, which image generation and editing features are confirmed, and how to choose a model.
GPT Image 2.5 is OpenAI's newest image-generation family, released through two models rather than one: GPT Image 2.5 Sunburst and GPT Image 2.5 Flare. Both accept text and image inputs and return images. The meaningful difference is workflow priority. OpenAI positions Sunburst as its most capable model for generation and editing, especially where editing precision matters, while Flare is optimized for fast, high-quality everyday generation.
That distinction is more useful than a generic “best model” label. A creative team rarely needs maximum fidelity for every draft. It needs quick exploration at one stage and controlled final work at another. GPT Image 2.5 is therefore best understood as a two-model production system: use Flare to move quickly when its output passes your standard, and use Sunburst where the extra quality or editing accuracy is necessary.
This guide separates confirmed capabilities from assumptions, explains the parameters that matter, and shows how to evaluate GPT Image 2.5 on real work.
GPT Image 2.5 at a Glance
The official model IDs are:
gpt-image-2.5-sunburstgpt-image-2.5-flare
OpenAI also lists dated snapshots ending in 2026-09-08. An undated model ID is convenient when you want the current version, while a dated snapshot is more appropriate when reproducible behavior matters.
Both models support image generation, image editing and transparent backgrounds. Available quality values are auto, low, medium, high, xhigh and max. Output dimensions can be selected automatically or supplied as custom sizes, subject to documented limits. The Image API handles direct generations and edits; the Responses API supports image generation inside conversational or multi-step flows.
What GPT Image 2.5 does not provide is equally important. The image models do not output audio or video, and their model pages do not list function calling, structured outputs or fine-tuning. Use them as specialized visual models rather than general-purpose agents.
Sunburst and Flare Are Built for Different Decisions
GPT Image 2.5 Sunburst
Sunburst is the quality-first choice. OpenAI recommends it when demanding requirements are the priority or when GPT Image 2 has not met a complex workflow's standard. Typical candidates include precise product edits, identity-sensitive reference work, intricate visual layouts and final campaign assets.
This does not mean every Sunburst request will automatically beat every Flare result. Prompt, reference quality, dimensions and quality setting all affect the outcome. The responsible approach is to define an acceptance test and compare results.
GPT Image 2.5 Flare
Flare is the speed-first model. OpenAI describes it as the small model and says its image quality is comparable to GPT Image 2. It is a practical starting point for thumbnails, ideation, composition tests, variation generation and high-volume everyday work.
Flare should not be treated as a disposable draft model. If it passes the same production standard as Sunburst for a particular task, its faster response can make it the better operational choice.
The recommended evaluation pattern
If an existing GPT Image 2 workflow already meets quality requirements, OpenAI suggests testing Flare first for a potential latency improvement. If GPT Image 2 falls short on a difficult use case, test Sunburst first. Once Sunburst passes, compare Flare against exactly the same prompt, references, dimensions and quality level.
This is a stronger method than generating one appealing image with each model. Measure repeated results, unwanted changes, identity preservation, text accuracy, latency, retries and cost per accepted image.
Confirmed GPT Image 2.5 Features
Text-to-image generation
Both models can generate an image from a written prompt. The official prompting guidance recommends defining the intended result, subject, composition, visual medium and constraints. That order forces the prompt to describe a deliverable, not merely a mood.
Image editing
GPT Image 2.5 can modify an existing image using a new instruction. For controlled edits, identify exactly what should change and list what must remain unchanged. This applies to wardrobe changes, background replacement, object removal, product cleanup and compositing references.
No generative edit should be assumed pixel-perfect. OpenAI warns that repeated edits can still alter details intended to remain fixed. If a region must be pixel-identical, use traditional compositing for that region rather than relying only on prompting.
Reference-image workflows
Multiple inputs can be assigned clear roles—for example, subject reference, clothing reference, style reference and background reference. A good prompt identifies inputs by number and explains what to take from each. “Combine these” is ambiguous; “preserve the person from image 1, use the coat from image 2 and place both in the lighting of image 3” is testable.
Transparent backgrounds
Both models support transparent output. In the API, request background: "transparent" and use PNG or WebP. A checkerboard drawn into an opaque image is not actual transparency, so verify the alpha channel in the returned file.
Flexible sizes and formats
Common dimensions include square, portrait and landscape formats, as well as 2K and 4K examples. Custom width and height values must use multiples of 16; neither edge can exceed 3,840 pixels; aspect ratio must stay between 1:3 and 3:1; and total pixel count must remain within the documented range. Outputs above 2560×1440 are currently described as experimental.
The Image API returns base64-encoded image data. PNG is the default, with JPEG and WebP also available. JPEG may be useful when latency and file size matter; PNG or WebP is required for transparency.
What Has Improved—and What Still Needs Review
OpenAI says both GPT Image 2.5 models improve precise editing and subject preservation. Sunburst is positioned above GPT Image 2 for image quality, while Flare is positioned near GPT Image 2 quality with a speed focus.
Those statements do not remove the need for visual QA. Official documentation still lists possible limitations involving latency, exact text placement, recurring-character consistency and structured composition. Complex prompts can take substantial time. Small typography can remain unreliable. A character may drift across independent generations, and elements may not land at exact coordinates.
For production work, review the whole image rather than only the requested change. A successful background edit is not acceptable if a product label, face or hand changed unexpectedly.
How to Write a Useful First Prompt
Use a specification with five blocks:
Purpose: Vertical key art for an original fantasy animation.
Subject: A young courier holding a cracked blue compass.
Composition: Waist-up, character on the right third, empty space at upper left.
Style and light: Hand-painted anime illustration, rainy dusk, cool ambient light and warm shop windows.
Constraints: One character, compass visible, no text, no logo, no watermark, no extra fingers.
Choose Flare for the first composition pass if speed matters. Once the composition is approved, compare Sunburst only if the result does not meet the required facial, object or texture standard. Changing the model, prompt and quality simultaneously makes the comparison meaningless.
Where GPT Image 2.5 Fits in a Creative Pipeline
GPT Image 2.5 produces and edits still images. A larger project may use those images as character concepts, style frames, product assets, storyboard references or animation keyframes. Keep the handoff explicit: save the prompt, model, quality, size, reference inputs and approved file.
If your goal is an animated story rather than a standalone image, a platform such as Elser AI can handle downstream steps including character-led scenes, storyboarding, video, audio and editing. At the time of this article, Elser publicly documents GPT Image 2 access, but this article does not claim native GPT Image 2.5 availability inside Elser. Export the rights-cleared still and verify the current Elser model list before planning a direct integration.
A Practical First-Look Test
Create a ten-prompt benchmark from your actual work:
- Two straightforward generations.
- Two difficult compositions.
- Two identity-preserving edits.
- One product-label edit.
- One exact-text poster.
- One transparent cutout.
- One repeated character across three scenes.
Score instruction following, subject preservation, typography, composition, unwanted changes, latency and accepted-image cost. Run more than one sample for each difficult case. The result will tell you whether Sunburst's quality advantage matters for your workflow and where Flare can save time.
FAQ
Is GPT Image 2.5 one model?
No. It is a family containing Sunburst and Flare. Sunburst prioritizes demanding quality and editing precision; Flare prioritizes fast, high-quality everyday generation.
Can GPT Image 2.5 edit existing images?
Yes. Both models accept image inputs and support editing. Clearly separate the requested change from details that must remain fixed, and inspect the entire result.
Does GPT Image 2.5 generate video?
No. Its documented output modality is image. Use a separate video or animation workflow for motion.
Which quality setting should beginners use?
Start with auto or an explicit medium setting for a controlled baseline. Use low for rough drafts and test higher settings only when they solve a defined quality problem.
Is GPT Image 2.5 available in Elser AI?
Elser's public pages currently verify GPT Image 2, not GPT Image 2.5. Check Elser's live model selector before claiming native availability. You can still use rights-cleared generated assets in an external downstream workflow where uploads are supported.
Conclusion
GPT Image 2.5 is best understood as a choice between Sunburst for demanding visual accuracy and Flare for faster everyday production. Both support generation, editing, references, transparency, custom sizes and expanded quality controls. Neither removes the need for precise prompts or visual review.
Start with a representative benchmark, keep parameters fixed, and promote a model only when it passes your real acceptance criteria. That produces a more useful “first look” than declaring a winner from a few attractive examples.
