GPT Image 2.5 is more than a routine image-quality upgrade. OpenAI has divided the new generation into two distinct API models: gpt-image-2.5-flare prioritizes speed and volume, while gpt-image-2.5-sunburst prioritizes quality and precise editing.
Here is the short verdict: choose Flare for everyday creation, rapid iteration, and high-volume generation. Choose Sunburst for campaign assets, product imagery, identity preservation, and multi-step editing. For mixed workloads, the most efficient approach is to generate candidates with Flare and send only the strongest images to Sunburst for refinement.
You can begin by testing compositions and prompt ideas in the PhotoArtify AI image generator, then move selected results into an editing workflow.
Review methodology: This article reflects information available on September 9, 2026. It combines OpenAI's official model documentation, image-generation guide, and API pricing with the identical-prompt comparisons published by Intelligent Living. The ratings below are an editorial assessment of the documented capabilities and public samples, not a PhotoArtify laboratory benchmark.
What is new in GPT Image 2.5?
The most important change is not a single benchmark score. GPT Image 2.5 brings generation, editing, and production controls into a more complete image workflow.
- Two model tiers: Flare is designed for fast, high-quality everyday generation. Sunburst is OpenAI's most capable model for image generation and editing.
- Six quality settings: Both models support
low,medium,high,xhigh,max, andauto, giving developers more control over quality, latency, and cost. - Flexible output sizes: In addition to common square, portrait, and landscape presets, GPT Image 2.5 supports custom resolutions within documented constraints, with edges up to 3,840 pixels.
- Native transparent backgrounds: PNG and WebP outputs can include transparency, which is useful for product cutouts, stickers, icons, and design assets.
- Multi-turn image editing: The Responses API can keep an image in context and apply a sequence of prompted changes.
- The same core image-token rates: Flare and Sunburst use the same standard image input, cached input, and output token prices. The final job cost depends on resolution, quality, reference inputs, and the number of attempts.
Together, these changes make GPT Image 2.5 feel less like a one-shot image generator and more like an image-production system that can be embedded in creative products.
GPT Image 2.5 Flare vs Sunburst
| Category | GPT Image 2.5 Flare | GPT Image 2.5 Sunburst |
|---|---|---|
| Official position | Fast, high-quality everyday image generation | Most capable image generation and editing model |
| Speed | Faster and better suited to volume | Slower, with quality taking priority |
| Complex scenes | Strong enough for most content tasks | Better suited to fine materials, complex layouts, and final assets |
| Editing | Supported | Better fit for precision-focused editing workflows |
| Best uses | Social graphics, thumbnails, concepts, previews | Product images, advertising, identity-sensitive edits, final creative |
| Cost strategy | Reduces the cost of exploration through faster iteration | Can reduce expensive rework on high-value assets |
Flare review: faster iteration is the real advantage
Flare's value is not limited to saving time on one image. A faster model lets a creator test more compositions, visual directions, and prompt variations within the same production window.
That matters for blog covers, social posts, mood boards, thumbnails, and early campaign concepts. The first generation is rarely the final answer in these workflows. The ability to explore ten useful directions can be more valuable than spending maximum resources on the first one.
Start at medium or high rather than selecting max automatically. Find a useful structure in the AI prompt gallery, then change one variable at a time, such as the camera position, lighting, background, or palette. Controlled revisions make it easier to identify which instruction actually improved the result.
Flare rating: 8.8/10
- Speed and throughput: 9.5
- Everyday image quality: 8.7
- Editing control: 8.2
- Cost control: 9.0
- Final-asset capability: 8.4
Sunburst review: precision matters more than raw generation
Sunburst is the stronger choice once the visual direction is established and the job shifts from exploration to execution. Its most important advantage is precision editing: replace a background without changing the subject, adjust clothing while preserving the face, or revise product lighting without rebuilding the package.
Generating another image that looks approximately right is easy compared with changing only the requested detail. Sunburst's quality-focused position makes it the better match for the PhotoArtify AI image editor, especially when an image must survive several rounds of feedback. The prompt-based image editing guide explains how to separate the requested change from the details that must remain fixed.
Sunburst rating: 9.1/10
- Speed and throughput: 7.8
- Everyday image quality: 9.2
- Editing control: 9.6
- Cost control: 8.3
- Final-asset capability: 9.5
Public sample results: visual quality is not data accuracy
The referenced comparison tested chart graphics, infographics, and photorealistic people with identical prompts. The examples show where GPT Image 2.5 is convincing and where a polished result can still be misleading.
Photorealistic people and commercial images look more mature
GPT Image 2.5 performs well when a prompt depends on materials, lighting, faces, and commercial composition. Sunburst is the more appropriate option for natural skin texture, stable facial features, and complex lighting. Flare can produce visually strong candidates with less waiting, which makes it useful during selection.
Human review is still necessary. Check eyes, teeth, fingers, jewelry connections, fabric edges, and background figures before delivery. As the overall image becomes more convincing, isolated defects can become easier to miss.
Text layout is useful, but final copy still needs verification
GPT Image models are capable of interpreting image prompts that contain text and layout instructions. Version 2.5 is useful for poster concepts, menu designs, covers, packaging drafts, and infographic layouts.
However, text that looks plausible is not necessarily correct. Brand names, dates, prices, percentages, disclaimers, and small labels must be verified individually. For commercial work, the safer process is to let the model produce the composition, background, and visual hierarchy, then add final copy in a design application.
Do not treat generated charts as accurate data visualizations
In the referenced chart test, GPT Image 2.5 produced professional-looking graphics, but bar positions, scale lines, and stated values were not consistently aligned. Moving from medium to max improved visual fidelity rather than the mathematical relationship between the data points.
Charts based on real data should be rendered with code or a dedicated charting tool. Use an image model for decorative backgrounds, conceptual illustrations, or non-critical visual elements. A chart that looks credible is not evidence that its data is correct.
Which quality setting should you use?
| Quality | Recommended use | Practical guidance |
|---|---|---|
low |
Thumbnails and bulk previews | Judge only the subject and composition |
medium |
Everyday content and first drafts | The best default starting point for most tests |
high |
Products, portraits, and text-heavy images | Use when visible detail has clear business value |
xhigh |
Fine materials, detailed illustration, priority assets | Confirm that it produces a measurable improvement over high |
max |
Premium final assets and extreme-detail tests | Use when quality matters more than latency and cost |
auto |
General product experiences | Convenient, but less suitable for controlled comparisons |
A common mistake is treating max as the most accurate setting. Quality controls how many generation resources the model can use. It does not guarantee correct composition, facts, spelling, or numerical relationships.
When the model misunderstands the request, rewriting the prompt is usually more effective than increasing the quality setting.
GPT Image 2.5 API pricing
OpenAI's standard pricing page lists the same rates for Flare and Sunburst: image input costs $8 per million tokens, cached image input costs $2 per million tokens, and image output costs $30 per million tokens. Text input is $5 per million tokens, with cached text input at $1.25 per million tokens.
There is no single fixed cost per image. The final amount changes with output size, quality, reference-image inputs, and retries. A useful production cost report should record:
- the number of candidates generated for each task;
- the percentage of outputs that can be used without regeneration;
- the average number of editing rounds;
- the total cost per deliverable image;
- the time from the first prompt to final approval.
Flare will often be more economical for high-volume content. Sunburst can still have a lower cost per usable result for important product or campaign images if its editing precision prevents multiple complete regenerations. Review the PhotoArtify pricing plans before choosing an allowance for a recurring workflow.
Recommended workflow: explore with Flare, refine with Sunburst
- Use the AI prompt gallery to define the subject, composition, lighting, camera, and visual style.
- Generate a set of candidates with the AI text-to-image generator, evaluating subject clarity and composition before small details.
- Move selected images into the AI image editor. State the requested change and separately list the elements that must remain unchanged.
- Inspect people, text, product geometry, transparent edges, and factual details before approval.
- If the image is correct but too small, use the AI image upscaler rather than regenerating the entire composition only for resolution.
This workflow separates exploration from delivery. Flare creates a broader choice set; Sunburst provides tighter control over the selected direction.
Strengths and limitations
Strengths
- Clear model tiers make it easier to allocate generation resources by task value.
- Generation and editing belong to one continuous API workflow.
- Custom dimensions, transparent backgrounds, and multi-turn editing address real production needs.
- Six quality levels provide more control over speed, quality, and budget.
- Flare and Sunburst are more useful together than as isolated alternatives.
Limitations
- Six quality settings can increase costs without a disciplined testing strategy.
- Charts, numbers, and small text still require human verification.
- Sunburst is unnecessary for many high-volume, low-value images.
- Transparent output still needs inspection around hair, glass, shadows, and soft edges.
- Official capability descriptions cannot replace testing with your own products, characters, brand rules, and editing requirements.
Final verdict
GPT Image 2.5 is compelling because it offers more production control, not simply because the outputs look better. A fast model handles exploration, a quality-focused model handles refinement, and the same platform supports generation, editing, transparency, custom sizes, and multi-turn feedback.
Flare is sufficient for occasional social images, blog graphics, and concept exploration. Sunburst is worth the additional processing time for ecommerce, advertising, recurring characters, and images that will pass through multiple revision rounds.
For teams and product developers, the best implementation is automatic routing: use Flare by default, then escalate to Sunburst for complex edits, high-value deliverables, or jobs that fail a quality check.
Overall rating: 9.0/10. GPT Image 2.5 is a meaningful production-oriented upgrade, but human review remains essential for charts, exact text, product accuracy, and factual visualizations.
Frequently asked questions
Is GPT Image 2.5 Flare or Sunburst better?
Neither model is universally better. Flare is the better choice for speed, volume, and experimentation. Sunburst is the better choice for final quality, complex scenes, and precision editing. Choose according to the cost of failure, not only the model name.
Does GPT Image 2.5 support transparent backgrounds?
Yes. Use PNG or WebP output and inspect the alpha channel around hair, glass, shadows, and translucent materials. JPEG does not support transparency.
Is max quality always better than high?
No. max can provide more rendering detail, but it does not automatically fix a weak prompt, incorrect numbers, or a misunderstood composition. Start with medium or high, then increase quality only when detail is the remaining problem.
Can GPT Image 2.5 generate accurate charts?
It can generate images that look like polished charts, but the values, scales, and positions may be inaccurate. Use a data-driven charting tool for final visualizations.
