GPT Image 2.5 Models: How to Choose Base, Flare, and Sunburst
GPT Image 2.5 introduces the base gpt-image-2.5 listing plus GPT-Image-2.5 Flare and Sunburst. Learn the key parameters, differences, and model choices.
GPT Image 2.5 arrived on September 8, 2026 as part of the ChatGPT Images 2.5 release, and many users are now looking for a practical gpt image 2.5 entry point and workflow. In official API documentation, the product family is represented by two model names: GPT-Image-2.5 Flare and GPT-Image-2.5 Sunburst. Access platforms commonly also list a plain gpt-image-2.5 entry as a lower-cost base option.
When searching for gpt image 2.5 official guidance, it is worth separating OpenAI’s own materials from third-party integration pages. This article does not promise prices or availability on any platform. Instead, it reads the public parameters and practical trade-offs so you can decide when the base entry, Flare, or Sunburst is the better fit.
Overview: one release, three common entry points
GPT Image 2.5 shifts more attention to editing precision and multi-turn consistency. OpenAI positions Flare as the fastest high-quality everyday image model for most applications, while Sunburst is positioned as the most capable image generation and editing model for workflows where fine editing matters most. Both accept text and image inputs and produce images.
Alongside the two official API names, many access platforms also list a plain gpt-image-2.5 entry that is usually limited to 1K output and offered at lower cost. This base option is best understood as a platform listing rather than a third official API model name. It is useful for quick prompt tests and low-cost drafts, but it does not define the full capability of the family.
Three entry points compared
The table below compares the three entries across the dimensions that matter most in practice. The base column refers to the plain gpt-image-2.5 listing that platforms commonly expose at 1K only. Flare and Sunburst are the official API model names from OpenAI. Available resolutions, quality tiers, and costs vary by platform.
| Dimension | Base gpt-image-2.5 | GPT-Image-2.5 Flare | GPT-Image-2.5 Sunburst |
|---|---|---|---|
| Official positioning | Platforms typically treat this as a low-cost entry for quick tests and drafts, not as a third official API model name. | OpenAI describes Flare as the fastest high-quality everyday image model and the default choice for most applications. | OpenAI describes Sunburst as the most capable generation and editing model, with editing precision taking priority even when generation may take longer. |
| Speed | The base entry usually corresponds to a default or lower-tier platform channel, so latency depends heavily on the integration. | Flare is positioned for speed and works well for high-volume daily image generation. | Sunburst may take longer because it prioritizes editing quality and consistency over raw generation speed. |
| Editing depth | Basic generation is typically available, but deeper editing depends on whether the platform exposes the full feature set. | Flare handles everyday edits quickly, but complex edit depth is not its main focus compared with Sunburst. | Sunburst emphasizes fine edits, preserving untouched areas, transparent backgrounds, and stronger multi-turn consistency. |
| Quality tier availability | Platforms may expose only part of the quality range, and a base entry is often limited to 1K output. | Flare can be used with quality settings including low, medium, high, xhigh, max, and auto where the platform supports them. | Sunburst is aimed at demanding work and is more likely to be used with higher resolution and quality tiers when available. |
| Best fit | Low-cost prompt testing, draft exploration, and non-critical batch generation. | Fast production of technical visuals, cool-toned assets, high-throughput daily content, and rapid validation. | Scene-rich warm visuals, precise edits, multi-turn revision work, and detailed production assets. |
Key parameters at a glance
These are the parameter dimensions most often discussed in public guidance. Actual options depend on the platform, API channel, and model entry, especially for the base listing, which may expose only a subset of capabilities.
| Parameter / capability | Values | What to know |
|---|---|---|
| Quality | low, medium, high, xhigh, max, auto | xhigh and max are new in Images 2.5; Images 2 previously offered low, medium, and high. |
| Resolution | Commonly 1K, 2K, and 4K | The base entry is usually 1K only; Flare and Sunburst availability depends on the platform and channel. |
| Aspect ratio | 16:9, 9:16, 3:2, 2:3, 4:3, 3:4, 21:9, 1:1, and others; image-to-image can use auto | Text-to-image workflows commonly use fixed ratios, while image-to-image can follow the source image automatically. |
| Input type | Text and image | The models accept text and image inputs and output images; some platforms support multiple reference images, up to 16. |
| Output format | No output_format parameter in the API | Because the Images 2.5 API does not expose output_format, some platforms do not offer a format selector. |
Four key updates in this release
The official launch material moves the focus from raw generation toward editing stability. The four changes below explain the main differences from the previous generation; they are based on OpenAI’s published materials rather than independent testing.
Recognizable reference subjects
The model places more emphasis on keeping a reference subject identifiable during generation and editing. Previously, people, products, or characters could drift in appearance across outputs. The current workflow is designed to preserve the key features of the original reference more consistently, reducing rework later.
Edits that leave the rest alone
Editing should change only the requested element while keeping other parts of the image as close to the original as possible. This is especially useful for background replacement, outfit changes, and local touch-ups, where a focused change matters more than a full re-render of the scene.
Complex layouts and transparent backgrounds
Complex layouts and transparent backgrounds have been strengthened. Posters with text, packaging layouts, UI elements, and multi-object compositions should hold their structure more reliably, with less overlap and distortion, which is helpful for design-oriented work.
Stronger multi-turn editing consistency
Multi-turn editing consistency is another headline change. Repeated edits should keep earlier modifications intact instead of drifting with each generation. For series assets and batch visuals, this matters more than single-image performance.
GPT Image 2.5 tutorial: five steps to your first generation
This gpt image 2.5 tutorial is for users who are new to the model and do not assume prior experience with APIs or a specific platform. Button labels may differ between interfaces, but the general flow should carry over.
1. Confirm the entry and model tier
First check whether you are on an official OpenAI page or a third-party platform. If it is an independent platform, look at whether it lists plain gpt-image-2.5, Flare, or Sunburst.
- Do not judge model capability from the page title alone
- Check available resolution and quality options before starting
2. Prepare the prompt and reference images
Write out the subject, composition, style, lighting, and elements that must be preserved. For image-to-image or editing work, prepare reference images where the main subject is clear and unobstructed.
- Break requirements into short clauses instead of long vague paragraphs
- When changing one area, state clearly that only that area should be modified
3. Choose the right quality tier
Match the quality tier to the purpose. Use a lower tier during draft exploration to save time, then move to a higher tier when approaching final delivery. A base entry limited to 1K is not the right tool for a finished 4K asset.
- Run composition tests at a lower tier before increasing quality
- Avoid using the highest tier for every experiment
4. Inspect the result item by item
Do not judge the image only by its overall look. Zoom in to check text, object counts, light and shadow, proportions, and edge details. Text needs to be checked character by character, not just by whether it looks roughly correct.
- Check text, counts, reflections, and contact shadows
- Check file dimensions and aspect ratio against the delivery spec
5. Fix one variable and retry
If only one element fails, change only the description related to that failure and keep everything else constant. Changing multiple variables at once makes it difficult to know which adjustment helped.
- Adjust one variable at a time before regenerating
- Save effective prompts for consistent batch reuse
Prompt template
The template below is useful for image generation tasks that need a clear, repeatable structure. Replace the bracketed content and decide whether to keep the image-to-image instructions for your workflow.
Subject: [main subject or product]; reference subject: [person, object, or character that must stay recognizable]; composition and ratio: [16:9, 1:1, or another ratio]; environment: [background, location, and spatial relationships]; lighting: [time of day, direction, and warm or cool tone]; style: [photorealistic, illustration, 3D, product shot, or poster]; must preserve: [hair color, logo, shape, color, etc.]; only modify: [specific element to change]; avoid: [extra text, watermark, unwanted objects]; output: [final delivery size and use].
- Write any Chinese text character by character in the prompt; do not just say “write in Chinese.”
- State transparent background explicitly when you need it, and confirm that the platform supports it.
- In multi-turn editing, keep the key constraints from earlier prompts to avoid style drift.
- If the result is close but a detail is wrong, try rephrasing that detail instead of rewriting the entire scene.
Typical scenarios and recommended entry
These recommendations are based on the public positioning of the three entries and common platform constraints, not absolute rules. If your platform only exposes the base entry, use it first to validate prompts.
| Scenario | Recommended entry | Reason |
|---|---|---|
| Low-cost prompt tests and drafts | Base gpt-image-2.5 | It is usually the cheaper way to verify composition and text legibility, but it is not intended for final high-resolution output. |
| High-volume daily output and technical visuals | GPT-Image-2.5 Flare | OpenAI positions this model for speed, making it a practical option for cool-toned tech graphics, UI material, and everyday content. |
| Fine editing and strong scene direction | GPT-Image-2.5 Sunburst | Editing precision and multi-turn consistency are stronger, which suits warm, scene-rich work that requires careful subject preservation. |
| Series assets and repeated revisions | GPT-Image-2.5 Sunburst | Repeated edits should hold earlier changes more reliably, which matters for character sets, packaging, and campaign visuals. |
| Platform only lists the base entry | Base gpt-image-2.5 | If the interface does not show Flare or Sunburst sub-selectors, generate within the available platform options and avoid promising unavailable capabilities. |
Current limits and boundaries
These are publicly confirmable limits, especially the common blind spots in third-party use. Do not treat a missing platform option as a model limitation, and do not promise that an option will be available before checking the current interface.
- xhigh and max quality tiers are new in Images 2.5, but they are not available on every platform or through every base entry.
- The plain gpt-image-2.5 entry is usually limited to 1K output; if you need higher resolution, check whether the platform exposes Flare, Sunburst, or 2K/4K tiers.
- The API does not expose an output_format parameter, so some platforms have no format selector; this does not mean the model is broken.
- OpenAI uses C2PA metadata and SynthID watermarking, but page display, download flow, and platform implementation can affect whether these markers are visible or complete.
- nanobananapro.hk is an independent platform, not the OpenAI official website. Its GPT Image 2.5 model page has a default cost of 3 platform points per generation and currently does not offer Flare or Sunburst sub-selectors, a 1K/2K/4K picker, or xhigh/max quality options.
Frequently asked questions
Where can I use GPT Image 2.5 in a local interface?
A common question is where to find a stable localized interface rather than a separate language-specific model. The actual entry may be the official OpenAI API or a third-party platform with a Chinese or English UI; the page mentioned in this article is an independent integration, not the OpenAI official site.
What is the official gpt image 2.5 website?
OpenAI’s official release and documentation should be checked on openai.com and developers.openai.com. Other platforms may list the model name, but they are access providers and should not be treated as the official website.
Are base, Flare, and Sunburst three official models?
No. The official API names are GPT-Image-2.5 Flare and GPT-Image-2.5 Sunburst. The plain gpt-image-2.5 entry is a lower-cost base listing that many platforms expose, usually limited to 1K output.
How should I choose between Flare and Sunburst?
Most everyday generation work should start with Flare because it is built for speed and higher throughput. Move to Sunburst when editing precision, complex layouts, transparent backgrounds, or multi-turn consistency are the main requirement. If you are only testing prompts, a base entry can be the cheaper first step.
Why is my platform missing 2K/4K or xhigh/max?
Available resolution and quality tiers vary by platform and channel. A base entry may only expose 1K, and some platforms do not offer an output format selector because the API does not expose output_format. Check the options shown in your current interface rather than assuming every tier is available.
Conclusion: choose by workflow, not by the newest name
GPT Image 2.5 is best approached by defining the job before choosing an entry. The plain gpt-image-2.5 listing is useful for low-cost trial and error, Flare matches high-volume daily production, and Sunburst is the stronger choice for fine edits and stable multi-turn revisions. Do not treat the base entry as the family’s ceiling, and do not assume every platform exposes every option.
The practical next step is to confirm the model name, resolution, quality tiers, and cost shown in your current interface before switching. If only the base entry is available, use it to refine prompts; when editing precision becomes the bottleneck, consider a channel that supports Flare or Sunburst.
To try the model on the current platform, visit the GPT Image 2.5 model page on nanobananapro.hk. It is an independent integration with a default cost of 3 platform points per generation, and the available options are shown on the page.
Official sources
The links below are OpenAI’s official release or developer documentation, used to check model descriptions, parameters, and image generation guidance.