GPT Image 2.5 Prompt Generator
Generate optimized prompts for OpenAI's GPT Image 2.5 — the September 2026 state-of-the-art image model behind ChatGPT Images 2.5 and the gpt-image-2.5-flare and gpt-image-2.5-sunburst API models. Built around the five-part brief structure OpenAI recommends (Scene → Subject → Details → Use Case → Constraints), with anti-slop rules, physically specific lighting language, and multi-turn edit discipline baked in.
Be specific: describe who/what is in the image with details
Quick Palettes
Generated Prompt
Fill in the form and click "Generate" to create an optimized GPT Image 2.5 prompt.
Tip: Be as specific as possible with your subject description. Instead of "a dog," try "a golden retriever puppy with a red bandana."
GPT Image 2.5 Tips
- • Structure: Scene → Subject → Important Details → Use Case → Constraints
- • Anti-slop: replace "stunning/masterpiece/8K" with concrete facts ("overcast daylight," "50mm feel," "brushed aluminum")
- • Lighting and texture are 2.5's biggest gain — name the light source, its direction, and its colour temperature
- • For edits, say "Change only X, keep everything else the same" and repeat the preserve list on every turn
- • Multi-turn beats compound: 2.5 holds quality across a chain of edits, so make one change per turn
- • Reference photos: state which image the identity comes from — subject preservation is much stronger in 2.5
- • Use xHigh or Max for dense typography, print-ready output, and campaign hero assets
GPT Image 2.5 Prompt Templates
Copy-ready image briefs built around what this model actually rewards. Swap the [BRACKETED] parts for your own subject and settings, then paste straight into the tool.
Product Hero Shot
ProductPlays to 2.5’s biggest gain — natural lighting and real material texture.
An editorial product photograph of [PRODUCT] on [SURFACE]. Lit by [LIGHT SOURCE, e.g. a single north-facing window] from camera [LEFT/RIGHT], soft directional falloff, roughly [COLOUR TEMPERATURE, e.g. 5200K]. The [MATERIAL, e.g. unglazed stoneware] reads as [SURFACE BEHAVIOUR, e.g. chalky matte with a faint speckle]. [FRAMING] at [LENS, e.g. 85mm] with shallow depth of field. Background: [BACKGROUND], softly defocused. Real photograph, honest texture, no retouching gloss, no watermark.
Text-Heavy Infographic
TypographyKeep word count low and hierarchy explicit — run this at xHigh or Max.
A vector infographic explaining [TOPIC]. Title "[TITLE]" across the top in bold [FONT STYLE]. Three numbered sections down the page, each a heading and one short line: "1. [HEADING] — [BODY]", "2. [HEADING] — [BODY]", "3. [HEADING] — [BODY]". Simple [ICON STYLE] icons beside each. [BACKGROUND COLOUR] background with [ACCENT] accents, thin rule lines, generous margins. Flat even lighting, every character sharp and correctly spelled, no duplicate text.
Poster / Flyer
DesignMatches the Poster template in ChatGPT Images 2.5.
A [STYLE] poster for [EVENT/BRAND], [SIZE/RATIO] format. Headline "[HEADLINE]" fills the upper third in [FONT STYLE], [COLOUR]. Subhead "[SUBHEAD]" beneath at roughly half the size. [IMAGE ELEMENT] occupies the middle. Detail line "[DATE / VENUE / URL]" along the bottom edge. Palette limited to [COLOURS], dominant colour [PRIMARY]. Clear hierarchy, generous margins, print-ready, no extra copy, no watermark.
Photorealistic Portrait
PeopleSpecify light source and skin texture rather than “beautiful lighting”.
A photorealistic portrait of [PERSON DESCRIPTION] wearing [WARDROBE]. [FRAMING, e.g. head and shoulders], body angled slightly camera left, eyes to lens, [EXPRESSION]. Lit by [LIGHTING SETUP] from [DIRECTION], [COLOUR TEMPERATURE]. Natural skin texture with visible pores, fine flyaway hair, no beauty retouching. Background: [BACKGROUND], softly defocused. 85mm feel, sharp focus on the near eye, true-to-life colour.
Reference Photo Composite
EditingSubject preservation from reference photos is a headline 2.5 upgrade.
Image 1: the base scene — preserve its composition, lighting direction and colour grade exactly. Image 2: the subject — [WHO/WHAT], this is the identity source. Place the subject from Image 2 into Image 1 at [POSITION], scaled so [SCALE CUE, e.g. their head reaches the window sill]. Match Image 1’s lighting direction, colour temperature and grain. Preserve: face, identity, outfit, proportions. Constraints: no extra objects, no added text, no halos or fringing.
Single Localised Edit
Editing2.5 changes only what you ask — name the change and the preserve list.
Change: [SPECIFIC ELEMENT] becomes [NEW STATE]. Preserve: composition, framing, lighting, shadows, colour grade, every other object, and all existing text exactly as they are. Constraints: no redesign, no extra objects, no added text, no watermark. Blend the edited area so its lighting, grain and focus falloff match the rest of the frame.
Multi-Turn Edit Chain
EditingQuality holds across turns — make one change per turn instead of one compound edit.
Turn 1 — Change only [FIRST CHANGE]; everything else stays exactly the same. Turn 2 — Now change only [SECOND CHANGE]; same person, same outfit, same framing as the previous image. Turn 3 — Now change only [THIRD CHANGE]; keep the colour grade and lighting from the previous image. On every turn preserve: [FACE / POSE / LAYOUT / BRAND TEXT]. Never add objects or text that were not requested.
Masked Inpaint
EditingFor the images/edits endpoint with an alpha-channel mask.
Fill the masked region only with [NEW CONTENT]. Match the surrounding [GRAIN / NOISE LEVEL], focus falloff, lighting direction and colour temperature so the seam is invisible. Do not alter a single pixel outside the mask. Constraints: no halos or fringing at the mask edge, no added text, no extra objects.
Transparent-Background Cutout
ProductTransparency improved in 2.5 — requires PNG or WebP output.
A [PRODUCT/SUBJECT] rendered as a clean cutout, [ANGLE, e.g. three-quarter view], lit by [LIGHTING] so the form reads clearly without a backdrop. Material: [MATERIAL] with [SURFACE FINISH]. Background: fully transparent (alpha channel), subject cleanly cut out, crisp edges on [FINE DETAIL, e.g. hair / wire / glass rim], no halos, no fringing, no drop shadow baked in.
Merch / Apparel Graphic
DesignMatches the Merch template; keep it flat and print-ready.
A [STYLE] graphic for [GARMENT], centred composition. Main motif: [MOTIF]. Lockup text reads "[TEXT]" in [FONT STYLE], [COLOUR], positioned [PLACEMENT]. Limited to [NUMBER] flat colours: [COLOURS]. Consistent stroke weight, no gradients, no drop shadows, clean vector edges, transparent background, print-ready at large scale.
Sketch-Guided Render
ScenesFor the @Sketch tool — say what the drawing governs and what it does not.
Use the supplied sketch for layout and proportion only — the final image is a [TARGET MEDIUM, e.g. photorealistic interior photograph], not a drawing. Scene: [SCENE]. Subject: [SUBJECT] positioned as sketched. Render in [STYLE] with [LIGHTING] and [MATERIALS]. Keep the sketched spatial relationships and scale; ignore the sketch’s line quality, colour and shading.
Multilingual Signage
TypographyPreserve typographic hierarchy across scripts; keep total word count low.
A [SIGN TYPE] displaying "[TEXT IN TARGET LANGUAGE]" in [SCRIPT, e.g. Japanese kanji and kana] set in [FONT STYLE], with "[SECONDARY TEXT]" beneath in smaller English at roughly half the size. Mounted on [SURFACE] in [SETTING]. [LIGHTING]. Straight-on framing, high contrast, text rendered crisply and correctly in both scripts, no duplicate or garbled characters.
Character Anchor (Series Work)
PeopleLock one specification, then reuse it verbatim across every panel or page.
Character anchor — [NAME]: [AGE/BUILD], [HAIR], [SKIN], wearing [OUTFIT DETAIL], [DISTINGUISHING FEATURE]. Proportions: [PROPORTION NOTE, e.g. three-and-a-half heads tall]. Style: [ART STYLE], [LINE WEIGHT], [PALETTE]. Scene for this panel: [SCENE AND ACTION]. Preserve identity exactly: same face, same outfit, same proportions and same palette as the anchor above. No style drift between panels.
Brand Asset Set
DesignConsistent multi-element output in one generation.
A set of [NUMBER] [ASSET TYPE, e.g. app icons] for [BRAND], arranged in an evenly spaced grid. Each depicts: [ITEM 1], [ITEM 2], [ITEM 3], [ITEM 4]. Consistent stroke weight, corner radius and optical size across all of them. Palette limited to [COLOURS], dominant colour [PRIMARY]. Plain [BACKGROUND] background, flat even lighting, crisp edges, no text labels.
What's New in GPT Image 2.5
OpenAI shipped Images 2.5 on 8 September 2026 as the successor to Images 2.0, alongside two new API models. Here is what actually changes how you write prompts.
Natural lighting & richer texture
The single most visible change. Surfaces read as real materials under real light instead of the flat, over-polished look 2.0 defaulted to. Physically specific prompting — light source, direction, colour temperature, surface finish — now pays off much more.
Stronger subject preservation
Faces, pets, and products carried in from reference photos survive the generation far more reliably. Name which reference image the identity comes from and 2.5 will hold it.
Genuinely localised edits
Ask for one change and the rest of the frame stays intact — including complex subjects and busy backgrounds. This is where Sunburst pulls ahead of Flare.
Multi-turn edits without decay
Long edit chains no longer degrade generation over generation. Prefer several small single-purpose turns over one compound instruction.
Complex layouts & transparency
Denser compositions hold together, transparent backgrounds cut cleanly (PNG/WebP), and images containing real-world information render more accurately.
Up to 50% lower latency
Flare beats GPT Image 2 on quality while halving generation time, which changes what is practical for high-volume and interactive workloads.
Flare vs Sunburst: Which Model Should You Use?
Both API models share the same per-token pricing, so pick on latency and edit precision rather than cost.
GPT Image 2.5 Flare
gpt-image-2.5-flareFast, high-quality everyday image generation. Higher quality than GPT Image 2 at roughly 50% lower latency.
- • Creator and social content
- • Product experiences and visual search
- • Rapid prototyping and iteration
- • High-volume generation
GPT Image 2.5 Sunburst
gpt-image-2.5-sunburstOpenAI's most capable image model, tuned for editing precision. Longer generation times in exchange for tighter control.
- • Production-ready campaign creative
- • Polished product imagery
- • Multi-step edits that must not drift
- • Print-ready and hero assets
GPT Image 2.5 Specifications
| Released | 8 September 2026 |
|---|---|
| API model IDs | gpt-image-2.5-flare · gpt-image-2.5-sunburst |
| Default snapshots | gpt-image-2.5-flare-2026-09-08 · gpt-image-2.5-sunburst-2026-09-08 |
| Modalities | Text + image in → image out |
| Quality levels | low · medium · high · xhigh · max · auto |
| Recommended sizes | 1024×1024 · 1536×1024 · 1024×1536 |
| Custom sizes | Multiples of 16, 1:3–3:1, max 3840px/edge (>2560×1440 experimental) |
| Endpoints | v1/images/generations · v1/images/edits · Responses API |
| Editing | Inpainting with alpha-channel mask, multi-image references, multi-turn |
| Output formats | PNG (default) · JPEG · WebP, with transparent background support |
| Latency | Up to 50% lower than Images 2.0 |
| API pricing | $5/1M text in · $8/1M image in · $30/1M image out |
| Rate limits | Tier 1: 100K TPM / 5 IPM → Tier 5: 8M TPM / 250 IPM |
| Provenance | C2PA content credentials + SynthID watermarking |
How to Use the Prompt Generator
Pick Mode, Model & Describe Your Vision
Choose Generate, Edit, Multi-Turn Edit, Combine, or Inpaint, then pick Flare or Sunburst and a quality level. Pick a use case category (product photo, infographic, poster, merch graphic, UI mockup, manga panel), then describe your scene and subject. For edits, fill in the “Preserve” field so identity, layout, and brand elements stay locked.
Generate Your Prompt
Click Generate. The tool assembles your inputs into the five-part structure, swaps vague praise for concrete lighting and material facts, formats text-in-image directives correctly, and adds a preserve list when you're editing — so the output drops straight into ChatGPT or the API.
Copy & Create
Paste into ChatGPT Images 2.5, the OpenAI Images API, or the Responses API with model “gpt-image-2.5-flare” or “gpt-image-2.5-sunburst”. For iterative work, use the prompt as a base and follow up with one small change per turn — repeat the preserve list each time to prevent drift.
Frequently Asked Questions
What is GPT Image 2.5?
GPT Image 2.5 is OpenAI's state-of-the-art image generation and editing model, released on 8 September 2026. In ChatGPT it ships as "ChatGPT Images 2.5"; in the API it is exposed as two model IDs — gpt-image-2.5-flare and gpt-image-2.5-sunburst. Compared with Images 2.0 it renders more natural lighting and richer textures, preserves subjects from reference photos far more reliably, follows editing instructions more consistently across multi-turn conversations, and cuts generation latency by up to 50%.
What is the difference between GPT Image 2.5 Flare and Sunburst?
Flare (gpt-image-2.5-flare) is the default: fast, high-quality everyday generation that beats GPT Image 2 on quality at roughly 50% lower latency. It suits creator and social content, product experiences, visual search, rapid prototyping, and high-volume generation. Sunburst (gpt-image-2.5-sunburst) is OpenAI's most capable image model, built for premium workflows that need tighter control across edits — production-ready campaign creative and polished product imagery — at the cost of longer generation times. Both currently share the same per-token API pricing, so the choice is about latency versus edit precision, not budget.
How does this prompt generator work?
You fill in structured inputs — scene, subject, style, composition, lighting, intended use, generation mode, model variant, quality, and constraints. Our AI assembles them into the five-part structure OpenAI recommends (Scene → Subject → Important Details → Use Case → Constraints) and applies anti-slop rules, physically specific lighting and material language, typography directives, and edit-mode discipline so the prompt is production-ready.
What changed from GPT Image 2 to GPT Image 2.5?
Six things matter most. (1) Lighting and texture are noticeably more natural. (2) Subject preservation from reference photos is much stronger. (3) Edits stay localised — the model changes what you asked for and leaves the rest of the frame intact, even with complex subjects and backgrounds. (4) Quality holds across long multi-turn edit chains instead of degrading. (5) Complex layouts and transparent backgrounds render better, and real-world information is more accurate. (6) Latency drops by up to 50%. On the API side the three quality levels became six (low, medium, high, xhigh, max, auto) and per-token pricing doubled.
What are Sketch, Templates, and image comments in ChatGPT Images 2.5?
Sketch lets you draw directly inside ChatGPT — type @Sketch — and use that drawing as a visual reference for the final image, which is the fastest way to lock layout and proportion. Templates give you a starting point for common formats such as posters, flyers, merch, and product photos, so you are not staring at a blank canvas. Image comments let you pin a note to a specific region of a generated image for a focused, local edit rather than re-prompting the whole thing. You can also share a prompt so other people can run it with their own photos and details.
What resolutions and sizes does GPT Image 2.5 support?
The recommended sizes are 1024x1024 (square), 1536x1024 (landscape), and 1024x1536 (portrait). Custom dimensions are accepted as WIDTHxHEIGHT where both values are multiples of 16, the aspect ratio sits between 1:3 and 3:1, no edge exceeds 3840px, and the total pixel count falls between 655,360 and 8,294,400. Anything above 2560x1440 is still flagged experimental by OpenAI.
How do the six quality levels work?
GPT Image 2.5 supports low, medium, high, xhigh, max, and auto (the default). Low is for quick drafts and high-volume exploration. Medium is the balanced production default for social and editorial work. High is for dense text, fine typography, and identity-sensitive edits. xHigh and Max are for print-ready output, close-up portraits, complex multi-element layouts, and campaign hero assets — they take longer and cost more per image, since billing is per token rather than a flat per-image rate.
How much does the GPT Image 2.5 API cost?
Both Flare and Sunburst are billed per token: $5 per 1M text input tokens ($1.25 cached), $8 per 1M image input tokens ($2 cached), and $30 per 1M image output tokens. There is no text output charge because the models only emit images. Those rates are double GPT Image 2's, so the real cost per image depends heavily on the size and quality level you pick — low quality at 1024x1024 stays fractions of a cent territory, while max quality at large sizes is materially more expensive.
What are the API rate limits?
Both models share the same default tiers: Tier 1 is 100K tokens per minute and 5 images per minute, Tier 2 is 250K TPM / 20 IPM, Tier 3 is 800K TPM / 50 IPM, Tier 4 is 3M TPM / 150 IPM, and Tier 5 is 8M TPM / 250 IPM. Complex prompts can take up to about two minutes to process, so set your client timeouts accordingly.
How good is text rendering in GPT Image 2.5?
Typography improved again in 2.5, and complex layouts hold together better than in 2.0 — but OpenAI still lists text rendering as one of the model's harder tasks, so treat it as a strength to work with rather than a solved problem. To get the best results: wrap literal text in quotes or ALL CAPS, specify font style, weight, size, and colour, state placement explicitly, keep total word count low, and spell tricky brand names letter-by-letter. Push quality to xHigh or Max for dense copy, small type, or multi-font layouts.
How should I structure a multi-turn edit?
Make one change per turn. Because 2.5 holds image quality across repeated edits without generational degradation, a chain of small single-purpose edits beats one compound instruction. Write each turn as "Change only X — everything else stays exactly the same," restate the preserve list (face, pose, lighting, background, layout, brand text), and re-anchor identity every few turns with "same person, same outfit, same framing as the previous image." In the API, the Responses endpoint supports this natively via previous_response_id.
Does GPT Image 2.5 support inpainting and transparent backgrounds?
Yes to both. Inpainting runs through the image edit endpoint with a mask — a PNG that matches your source image in format and size, carries an alpha channel, and stays under 50MB. Transparent backgrounds are set with the background parameter and improved in 2.5; they require PNG or WebP output. When you inpaint, tell the model to fill the masked region only and to match the surrounding grain, focus falloff, and lighting.
Why is "anti-slop" language important?
GPT Image 2.5 ignores vague praise like "stunning," "masterpiece," "ultra-detailed," or "8K." It responds far more reliably to concrete visual facts — "overcast daylight," "brushed aluminum," "50mm feel," "soft bounce light," "matte ceramic." Given how much lighting and texture fidelity improved in 2.5, physically specific language now pays off more than it did in earlier versions. Our generator strips out filler and substitutes specific camera, material, and lighting terms.
Are GPT Image 2.5 outputs watermarked?
Yes. Images carry C2PA content credentials in their metadata and Google DeepMind's SynthID watermarking. OpenAI also reports lower unsafe-output rates than the previous generation in its automated adversarial evaluations — 1.09% for Sunburst and 1.41% for Flare, against a 1.64% baseline.
Is this tool free to use?
Yes. You get 1 free prompt generation per day with no signup required. For unlimited access, sign up for a Promptslove membership which includes all AI tools and 20,000+ premium prompts.
Where can I use the generated prompts?
The prompts are tuned for ChatGPT Images 2.5 and the OpenAI API — the images generations and images edits endpoints, or the Responses API, with model "gpt-image-2.5-flare" or "gpt-image-2.5-sunburst". They also produce strong results on Midjourney, FLUX.2, Nano Banana 2, Seedream 5, and Stable Diffusion thanks to the structured, concrete language.
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