GPT Image 2.6 Prompt Generator
Generate optimized prompts for OpenAI's GPT Image 2.6, the November 2026 model behind ChatGPT Images 2.6 and the gpt-image-2.6-sunburst and gpt-image-2.6-flare API models. Write still images, looping animated GIFs and clean SVG vectors from the same form, with the five-part brief structure OpenAI recommends (Scene → Subject → Details → Use Case → Constraints), the new complexity and markup controls, and 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 2.6 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 2.6 Tips
- • Structure: Scene → Subject → Important Details → Use Case → Constraints
- • Anti-slop: replace "stunning/masterpiece/8K" with concrete facts ("overcast daylight," "50mm feel," "brushed aluminum")
- • GIFs: one subject, one small motion, a start pose it returns to, and say whether the camera moves
- • GIFs: lock what must not move — "background, colours and text stay identical in every frame"
- • SVG: flat fills, strong silhouette, 2-5 colours, minimal strokes, no gradients unless essential
- • SVG is generation-only with n=1: it cannot vectorize an existing logo or take an input image
- • Complexity Low for targeted edits and markup, High for open-ended posters and infographics
- • Text too small to read? Raise quality, not complexity
- • For edits, say "Change only X, keep everything else the same" and repeat the preserve list every turn
GPT Image 2.6 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.
Looping GIF Sticker
Animated GIFNew in 2.6: one subject, one small motion, a start pose it returns to.
Looping animated sticker in a flat illustration style. Subject: [CHARACTER, e.g. a round orange cat with a tiny backpack], centered with even padding on a flat [SOLID COLOR] background. Motion: the cat [SMALL MOTION, e.g. waves one paw twice] and returns to its starting pose, sitting upright facing the viewer. Keep the camera still. The outline, colors and background stay identical in every frame; only the paw moves. The motion loops smoothly. No text. API: output_format=gif, frame_count=9, frame_duration_ms=200
Ambient Product Loop
Animated GIFA still product shot with one subtle moving element, made for social and email.
Editorial product shot of [PRODUCT, e.g. a matte black ceramic coffee mug] on a [SURFACE] by a window, soft directional daylight from camera left, ~5200K, 50mm feel. Motion: only [AMBIENT ELEMENT, e.g. thin steam rising from the mug and drifting right] moves; it starts and ends in the same position so the loop is seamless. The mug, the surface, the light and the framing do not change between frames. Camera locked off. No text, no watermark. API: output_format=gif, frame_count=12, frame_duration_ms=120
Animated Logo Reveal
Animated GIFAnimate a still logo on the edits endpoint, with the logo kept exact.
Animate Image 1: the [BRAND] logo. Motion: a single soft light sweep travels left to right across the logo, then the frame settles back to the original still logo. Keep the logo shape, the letterforms "[BRAND NAME]", the colors and the [BACKGROUND] exactly as in Image 1 in every frame. Camera still. No added text, no extra elements. API: output_format=gif, frame_count=9, frame_duration_ms=100
SVG Logo Mark
SVG VectorNew in 2.6: editable vector output. Flat shapes and a short palette.
Flat vector logo mark for [BRAND, e.g. a neighborhood bakery called "Rise"]. A [SYMBOL, e.g. a wheat stalk forming the letter R] with a strong, simple silhouette, centered with even padding. Clean vector-like shapes, flat fills, minimal strokes, no gradients, no texture. Palette: [COLOR 1 HEX] as the main fill and [COLOR 2 HEX] as the accent, nothing else. Wordmark "[NAME]" below the symbol in a geometric sans serif, set once. Transparent background. API: output_format=svg, n=1, background=transparent
SVG Icon Set
SVG VectorA consistent row of line icons for a UI or a landing page.
Set of [N, e.g. 6] line icons for a [PRODUCT, e.g. personal finance app]: [ICON 1], [ICON 2], [ICON 3], [ICON 4], [ICON 5], [ICON 6]. Arranged in a single row, equal size, equal spacing. Same 2px-style stroke weight on every icon, rounded line caps and joins, consistent corner radius, no fills except [ACCENT HEX] on one small detail per icon. Strokes in [MAIN HEX]. No gradients, no shadows, no text. Transparent background. API: output_format=svg, n=1, background=transparent
SVG Spot Illustration
SVG VectorA flat illustration for a blog header or an empty state, as an editable file.
Flat vector spot illustration of [SCENE, e.g. a person watering a row of potted plants on a balcony], for a [USE, e.g. website empty state]. Simple geometric shapes, bold silhouettes, flat fills, no gradients, no texture, no tiny details. Palette limited to [3-5 HEX CODES], with [DOMINANT HEX] dominant. Wide composition with clear negative space on the right. No text. API: output_format=svg, n=1
Markup Edit
EditingNew in 2.6: circle what to change on a transparent overlay.
Use the red circle in the markup to locate the target: [WHAT IS CIRCLED, e.g. the coffee cup on the left side of the table]. Change: [EDIT, e.g. replace it with a tall glass of iced tea with lemon]. Match the existing lighting direction, shadows and depth of field. Everything outside the marked area stays exactly unchanged. Remove the markup circle completely from the final image.
Product Hero Shot
ProductPlays to GPT Image’s strength in 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.
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
EditingNumber every reference and say which one the identity comes from.
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
EditingUse complexity low and name both 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
ProductTransparent backgrounds need PNG, WebP or SVG 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 2.6
OpenAI shipped Images 2.6 in November 2026, two months after 2.5. The big news is new output formats: images can now move and can come back as vectors. Here is what actually changes how you write prompts.
Animated GIFs
Set output_format to gif and get a 2–16 frame loop back from one prompt, or animate a still reference on the edits endpoint. Describe one subject, one small motion and a start pose to return to.
SVG vector output
Logos, icons and flat illustrations come back as editable SVG paths. Write for flat fills, a strong silhouette and a short palette. Generation-only, one image per request.
Complexity control
A new planning dial, separate from quality. Low keeps targeted edits tight and literal; high lets the model plan open-ended posters and infographics.
Markup edits
Circle or scribble on a transparent overlay to point at what should change. Ask for the marks to be removed and everything outside them kept.
Saved references in ChatGPT
Save a photo of yourself, a pet or a character once and reuse it across chats, which helps with the model's known weak spot: consistency across generations.
Faster generation
OpenAI reports faster generation than 2.5, and Flare is the pick when speed matters most. Note the trade-off: 2.6 runs on the Images API only, with no Batch or Responses support.
Flare vs Sunburst: Which Model Should You Use?
Both API models share the same per-token pricing. OpenAI's advice: start with Flare when speed matters most, or Sunburst when detail and edit accuracy are demanding. If Sunburst works, try Flare with the same inputs and compare.
GPT Image 2.6 Flare
gpt-image-2.6-flareThe smaller 2.6 model, for workflows where speed matters most. Same features, including GIF and SVG output.
- • Creator and social content
- • Product experiences and visual search
- • Quick GIF stickers and reaction loops
- • High-volume generation
GPT Image 2.6 Sunburst
gpt-image-2.6-sunburstThe base 2.6 model: more detail and more precise edits, in exchange for longer generation times.
- • Production-ready campaign creative
- • Polished product imagery
- • Multi-step edits that must not drift
- • Detailed SVG logos and brand marks
GPT Image 2.6 Specifications
| Released | November 2026 (Images 2.6 in ChatGPT and Codex) |
|---|---|
| API model IDs | gpt-image-2.6-sunburst · gpt-image-2.6-flare |
| Endpoints | v1/images/generations · v1/images/edits (no Responses, Batch, Chat Completions or Realtime) |
| Output formats | PNG (default) · JPEG · WebP · GIF (new) · SVG (new) |
| GIF settings | frame_count 2–16 (default 9) · frame_duration_ms 20–2000 (default 200) · generations and edits |
| SVG settings | Generations only · n=1 · static paths, no embedded rasters · transparent background supported |
| Complexity | low · medium · high (default) — planning depth, no extra cost |
| Quality levels | low · medium · high · xhigh · max |
| Recommended sizes | 1024×1024 · 1536×1024 · 1024×1536 |
| Custom sizes | Multiples of 16, 1:3–3:1, max 3840px/edge (>2560×1440 experimental) |
| Editing | Mask inpainting, markup overlays (new), multi-image references |
| API pricing | $5/1M text in · $8/1M image in · $30/1M image out (same as 2.5) |
How to Use the Prompt Generator
Pick Mode, Model & Describe Your Vision
Choose a mode (Generate, Edit, Multi-Turn, Combine, Inpaint or Markup) and an output format: still image, animated GIF or SVG. For GIFs, set the motion, frame count and frame duration. Then pick Sunburst or Flare, a quality level and a complexity 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 2.6 or the OpenAI Images API with model “gpt-image-2.6-sunburst” or “gpt-image-2.6-flare”. GIF and SVG prompts end with an “API:” line telling you which output_format and frame settings to send (leave that line out if you paste into ChatGPT). 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 2.6?
GPT Image 2.6 is OpenAI's image generation and editing model, released in November 2026. In ChatGPT and Codex it ships as "Images 2.6"; in the API it is two model IDs, gpt-image-2.6-sunburst (the base model, most detail and most precise edits) and gpt-image-2.6-flare (the smaller, faster model). The headline additions are animated GIF output, SVG vector output, a new complexity parameter that controls how much the model plans a composition, and markup edits where you circle the part of an image you want changed.
Can GPT Image 2.6 make animated GIFs?
Yes. Set output_format to "gif" on the generations or edits endpoint. You choose frame_count (2 to 16, default 9) and frame_duration_ms (20 to 2000 ms, default 200), so the default loop runs about 1.8 seconds. On the edits endpoint, still reference images guide the animation. Prompt it with one subject, one small motion and a start pose the subject returns to, and say whether the camera moves. Frame count times frame size has to fit OpenAI's pixel budget, so large frames and 16 frames together will be rejected. GIF output has no streaming, partial images or compression setting.
Can GPT Image 2.6 create SVG files?
Yes, through the API with output_format "svg" on the generations endpoint. It needs n=1, and it is generation-only: it cannot edit an image, and you cannot feed it an existing logo to vectorize. Under the hood the model renders an internal raster reference, then a separate authoring model writes clean, static SVG paths (no scripts, external resources or embedded rasters). It works best for logos, icons and flat illustrations. Ask for flat fills, a strong silhouette, a small palette, minimal strokes and no gradients unless essential. Transparent backgrounds are supported.
What does the new complexity parameter do?
Complexity (low, medium or high, default high) controls how much the model plans the composition before drawing. It is separate from quality and costs nothing extra, but higher values add latency and can introduce changes you did not ask for. Use low for targeted edits, markup and inpaint jobs, and high for open-ended designs such as posters and infographics. If your layout is right but small text is unreadable, raise quality rather than complexity.
What is a markup edit?
Markup editing launched alongside 2.6 (it also works on the 2.5 models). You pass a transparent PNG the same size as your first input image with circles, arrows or scribbles on it, and describe the change. The model uses the marks to find the target. Good prompts say what the mark points at, what to change, that everything outside stays the same, and that the marks themselves must be removed. Markup applies to the first image only and cannot be combined with a mask.
What is the difference between Sunburst and Flare?
Sunburst (gpt-image-2.6-sunburst) is the base model, built for detail and precise edits: campaign creative, polished product imagery, detailed SVG logos. Flare (gpt-image-2.6-flare) is the smaller model for workflows where speed matters most. OpenAI's advice: start with Flare when response time matters most and with Sunburst when detail, layout or edit accuracy is demanding; if Sunburst meets your needs, try Flare with the same prompts, references, size and quality. Both share the same per-token pricing, so the choice is about speed versus precision.
How does this prompt generator work?
Pick an output format (still image, animated GIF or SVG), a mode, model, quality and complexity, then describe your scene and subject. Our AI writes a brief in the five-part structure OpenAI recommends (Scene → Subject → Important Details → Use Case → Constraints). For GIFs it writes one clear looping motion with everything else locked between frames; for SVGs it writes flat vector shape language with a short palette. It ends GIF and SVG prompts with the matching API settings line.
What changed from GPT Image 2.5 to 2.6?
Four things are new: animated GIF output, SVG vector output, the complexity parameter, and markup edits (markup was also added to the 2.5 models). OpenAI also reports faster generation, and ChatGPT can now save reference images of you, your pets or your characters to reuse across chats. On the API side, the 2.6 models run on the Images API only: unlike 2.5, they are not available on the Responses API or the Batch API. Pricing per token is unchanged.
What resolutions and sizes does GPT Image 2.6 support?
The recommended sizes are 1024x1024, 1536x1024 and 1024x1536. Custom dimensions are WIDTHxHEIGHT in multiples of 16, with an aspect ratio between 1:3 and 3:1, no edge above 3840px, and a total pixel count between 655,360 and 8,294,400. Anything above 2560x1440 is still experimental. For GIFs, size applies to each frame.
How do the quality levels work?
GPT Image 2.6 supports low, medium, high, xhigh and max (OpenAI's docs list auto for the 2.5 models). Low is for quick drafts and high-volume exploration. Medium is the balanced production default. High is for dense text, labels, diagrams and identity-sensitive edits. xHigh and Max are for print-ready output, close-up portraits and hero assets, and take longer and cost more. For SVG, quality controls the internal reference image the vector is drawn from.
How much does the GPT Image 2.6 API cost?
Both Sunburst and Flare 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, the same as 2.5. SVG output is billed as the reference image plus the authoring model's SVG tokens. There is no Batch API discount because the 2.6 models do not support Batch.
How good is text rendering in GPT Image 2.6?
Good, but OpenAI still lists exact text placement as a known weak spot. Quote the exact copy, say how many times it should appear, give font style, weight, size, colour and placement, ask for no extra text, and spell tricky brand names letter by letter. Use high quality or above for dense labels. In a GIF, tell the model the text stays identical in every frame.
How should I structure a multi-turn edit?
Make one change per turn and restate the preserve list every time, because results drift. Write each turn as "Change only X, everything else stays exactly the same," and re-anchor identity every few turns. In the API, 2.6 has no Responses-API chaining, so send each turn as a new edits call with the previous output as the first image. In ChatGPT, saved reference images keep a person or character consistent across chats.
Does GPT Image 2.6 support inpainting and transparent backgrounds?
Yes. Inpainting uses the edits endpoint with a mask that has an alpha channel; it applies to the first image and is followed only roughly, so describe the masked region precisely. Transparent backgrounds work with PNG, WebP and SVG output. Transparency is not documented for GIFs, so for a GIF ask for a flat solid background that is easy to remove.
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 29,000+ premium prompts.
Where can I use the generated prompts?
The prompts are tuned for ChatGPT Images 2.6 and the OpenAI Images API (v1/images/generations and v1/images/edits) with model "gpt-image-2.6-sunburst" or "gpt-image-2.6-flare". Still-image prompts also work well on Midjourney, FLUX 3, Nano Banana 2.1 and Seedream 5 thanks to the structured, concrete language.
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