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OpenAI
gpt-image-2
Documentation
Documentation
gpt-image-2 is OpenAI's April 2026 image model: agentic "thinking" that plans composition before rendering, native 2K output, multilingual in-image text (including CJK), and mask-based editing with up to 16 reference images. Route it through GPTProto with the standard OpenAI request shape.

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GPT Image 2 API

Call OpenAI's gpt-image-2 through one GPTProto key at $6.4/$24 per 1M tokens — 20% under OpenAI's list. Same model ID, no organization verification, one balance shared across 200+ models.

Reference-Guided Consistency

Pass up to 16 reference images in a single call to hold subject identity, style, and product details across a set — for sequential art, catalog shots, and brand-consistent campaigns.

Create a cinematic character design board for a high-budget drama film. A beautiful female lead with soft expressive eyes, flawless but natural skin texture, elegant silk gown, subtle jewelry. Include full-body turnaround, expressive head studies, cinematic portrait, fabric flow breakdown, makeup detail studies, annotation notes, height scale. Mood: soft studio lighting, golden Hollywood glamour aesthetic.

Prompt
arrow
Reference-Guided Consistency
After

Reference-Guided Consistency

Pass up to 16 reference images in a single call to hold subject identity, style, and product details across a set — for sequential art, catalog shots, and brand-consistent campaigns.

arrow

Create a cinematic character design board for a high-budget drama film. A beautiful female lead with soft expressive eyes, flawless but natural skin texture, elegant silk gown, subtle jewelry. Include full-body turnaround, expressive head studies, cinematic portrait, fabric flow breakdown, makeup detail studies, annotation notes, height scale. Mood: soft studio lighting, golden Hollywood glamour aesthetic.

Prompt
Reference-Guided Consistency
After

In-Image Text Rendering

Renders small labels, UI copy, and long passages in Latin and CJK scripts with layout accuracy — usable in client work without a separate typesetting pass. A core reason to reach for the gpt image 2 api over older generators.

Avant-garde Tokyo fashion zine poster with a refined neo-Y2K editorial aesthetic, inspired by underground Japanese street magazines and luxury urban campaigns. Layered collage composition featuring weathered paper textures, fragmented magazine clippings, faded xerox marks, distressed ink smears, scratched film overlays, and contemporary Harajuku-inspired graphic design. Primary visual: a dominant cinematic beauty portrait occupying the upper half of the poster, intense direct gaze with razor-sharp eye detail, naturally textured skin, softly glossy lips, loosely pinned messy hair strands, no eyewear, subtle moody rim lighting, calm yet powerful expression, photographed like a luxury street-fashion campaign with ultra-realistic DSLR depth and authentic facial detail. Secondary visuals: exactly two smaller ripped-frame portraits near the lower section, each showing different moods and camera perspectives, arranged asymmetrically like taped instant-film snapshots layered over torn paper pieces. Graphic styling: oversized experimental Japanese typography integrated into the composition, minimal condensed English captions, faded metro signage fragments, barcode labels, editorial stamps, folded newspaper textures, masking tape strips, rough brush marks, grainy analog imperfections, layered cut-paper shadows, and sophisticated magazine-inspired spacing. Overall mood: clean but rebellious, premium Japanese street-editorial energy, cinematic contrast, muted neutral palette with charcoal, ivory, faded silver, and washed earth tones, subtle flash photography feel, raw fashion photography realism, modern visual culture poster design, highly detailed luxury collage artwork, sharp focus, authentic print imperfections, ultra high resolution, 8K aesthetic, absolutely no kawaii elements, no pastel tones, no cartoon styling.

Prompt
arrow
In-Image Text Rendering
After

In-Image Text Rendering

Renders small labels, UI copy, and long passages in Latin and CJK scripts with layout accuracy — usable in client work without a separate typesetting pass. A core reason to reach for the gpt image 2 api over older generators.

arrow

Avant-garde Tokyo fashion zine poster with a refined neo-Y2K editorial aesthetic, inspired by underground Japanese street magazines and luxury urban campaigns. Layered collage composition featuring weathered paper textures, fragmented magazine clippings, faded xerox marks, distressed ink smears, scratched film overlays, and contemporary Harajuku-inspired graphic design. Primary visual: a dominant cinematic beauty portrait occupying the upper half of the poster, intense direct gaze with razor-sharp eye detail, naturally textured skin, softly glossy lips, loosely pinned messy hair strands, no eyewear, subtle moody rim lighting, calm yet powerful expression, photographed like a luxury street-fashion campaign with ultra-realistic DSLR depth and authentic facial detail. Secondary visuals: exactly two smaller ripped-frame portraits near the lower section, each showing different moods and camera perspectives, arranged asymmetrically like taped instant-film snapshots layered over torn paper pieces. Graphic styling: oversized experimental Japanese typography integrated into the composition, minimal condensed English captions, faded metro signage fragments, barcode labels, editorial stamps, folded newspaper textures, masking tape strips, rough brush marks, grainy analog imperfections, layered cut-paper shadows, and sophisticated magazine-inspired spacing. Overall mood: clean but rebellious, premium Japanese street-editorial energy, cinematic contrast, muted neutral palette with charcoal, ivory, faded silver, and washed earth tones, subtle flash photography feel, raw fashion photography realism, modern visual culture poster design, highly detailed luxury collage artwork, sharp focus, authentic print imperfections, ultra high resolution, 8K aesthetic, absolutely no kawaii elements, no pastel tones, no cartoon styling.

Prompt
In-Image Text Rendering
After

Agentic Thinking Mod

Before rendering, gpt-image-2 reasons through layout and constraints — the first OpenAI image model with O-series-style planning. It raises success rates on dense scenes like infographics, multi-panel layouts, and packaging.

Prompt : FORMAT: 4:5 vertical premium menswear SMM poster hyper-realistic fashion advertising luxury daytime editorial campaign Instagram billboard composition 8K ultra-detail CONCEPT: SNITCH adapts its own daytime luxury identity clean masculine confidence modern Indian street luxury minimal premium fashion storytelling SCENE: Young Indian man walking through luxury business district in Mumbai during bright afternoon cream architectural buildings hard sunlight shadows across pavement warm glass reflections wearing oversized beige coord-set white sneakers silver accessories luxury café atmosphere in background confident expression effortless rich-boy energy COMPOSITION: architectural leading lines model positioned lower-center large sky negative space for typography clean premium framing editorial composition balance PRODUCT FOCUS: SNITCH oversized coord-set hero styling visible linen and cotton fabric texture luxury tailoring folds premium sneaker detailing realistic garment stitching fashion-product-first framing UI ELEMENTS: minimal fashion UI adapted to SNITCH identity floating fabric-detail card “RELAXED FIT” collection strip system size selector dots wishlist micro icon editorial product labeling beige monochrome overlays luxury fashion micro typography TYPOGRAPHY: Huge bold headline: “OWN THE DAY.” subheading: “Luxury essentials for modern Indian men.” CTA: “SHOP SS26” GRAPHICS: minimal beige grid overlays architectural line graphics soft editorial framing micro-fashion markers premium whitespace balance LIGHTING: hard natural sunlight warm commercial reflections luxury skin tones high dynamic range realism editorial daylight exposure CAMERA: Sony A1 50mm lens f/2.0 luxury fashion photography STYLE: Jacquemus × Zara Studio × SNITCH premium Indian luxury streetwear Second : FORMAT: 4:5 vertical luxury athletic fashion campaign hyper-realistic commercial streetwear photography premium Gen-Z menswear advertising 8K ultra-detail CONCEPT: SNITCH adapts a luxury athletic identity sport-meets-streetwear energy minimal aggressive masculinity urban performance aesthetic SCENE: Young Indian male model standing beside upscale outdoor basketball court bright blue summer sky metal fencing reflections city skyline background wearing sleeveless black SNITCH coord set premium sneakers athletic sweat realism confident body posture youth luxury energy COMPOSITION: dynamic diagonal composition court lines creating motion model centered heroically clean fashion hierarchy street-performance framing PRODUCT FOCUS: SNITCH athletic coord-set hero styling high-detail fabric texture luxury sporty tailoring visible stitching realism premium performance silhouette UI ELEMENTS: sport-inspired SNITCH interface system drop countdown graphics collection code labels motion-speed typography overlays floating stitched-label graphics minimal black-orange UI accents performance feature tags TYPOGRAPHY: Huge bold headline: “MOVE DIFFERENT.” subheading: “Built for speed, style, and presence.” CTA: “EXPLORE DROP” GRAPHICS: kinetic motion streaks athletic grid overlays industrial sports graphics minimal energy textures performance-driven composition LIGHTING: harsh summer sunlight realistic athletic highlights high-detail shadow realism premium sports-commercial exposure CAMERA: Canon EOS R5 35mm sports-fashion lens f/1.8 commercial lifestyle photography STYLE: Nike lifestyle × Fear of God × SNITCH premium Indian sport-luxury fashion

Prompt
arrow
Agentic Thinking Mod
After

Agentic Thinking Mod

Before rendering, gpt-image-2 reasons through layout and constraints — the first OpenAI image model with O-series-style planning. It raises success rates on dense scenes like infographics, multi-panel layouts, and packaging.

arrow

Prompt : FORMAT: 4:5 vertical premium menswear SMM poster hyper-realistic fashion advertising luxury daytime editorial campaign Instagram billboard composition 8K ultra-detail CONCEPT: SNITCH adapts its own daytime luxury identity clean masculine confidence modern Indian street luxury minimal premium fashion storytelling SCENE: Young Indian man walking through luxury business district in Mumbai during bright afternoon cream architectural buildings hard sunlight shadows across pavement warm glass reflections wearing oversized beige coord-set white sneakers silver accessories luxury café atmosphere in background confident expression effortless rich-boy energy COMPOSITION: architectural leading lines model positioned lower-center large sky negative space for typography clean premium framing editorial composition balance PRODUCT FOCUS: SNITCH oversized coord-set hero styling visible linen and cotton fabric texture luxury tailoring folds premium sneaker detailing realistic garment stitching fashion-product-first framing UI ELEMENTS: minimal fashion UI adapted to SNITCH identity floating fabric-detail card “RELAXED FIT” collection strip system size selector dots wishlist micro icon editorial product labeling beige monochrome overlays luxury fashion micro typography TYPOGRAPHY: Huge bold headline: “OWN THE DAY.” subheading: “Luxury essentials for modern Indian men.” CTA: “SHOP SS26” GRAPHICS: minimal beige grid overlays architectural line graphics soft editorial framing micro-fashion markers premium whitespace balance LIGHTING: hard natural sunlight warm commercial reflections luxury skin tones high dynamic range realism editorial daylight exposure CAMERA: Sony A1 50mm lens f/2.0 luxury fashion photography STYLE: Jacquemus × Zara Studio × SNITCH premium Indian luxury streetwear Second : FORMAT: 4:5 vertical luxury athletic fashion campaign hyper-realistic commercial streetwear photography premium Gen-Z menswear advertising 8K ultra-detail CONCEPT: SNITCH adapts a luxury athletic identity sport-meets-streetwear energy minimal aggressive masculinity urban performance aesthetic SCENE: Young Indian male model standing beside upscale outdoor basketball court bright blue summer sky metal fencing reflections city skyline background wearing sleeveless black SNITCH coord set premium sneakers athletic sweat realism confident body posture youth luxury energy COMPOSITION: dynamic diagonal composition court lines creating motion model centered heroically clean fashion hierarchy street-performance framing PRODUCT FOCUS: SNITCH athletic coord-set hero styling high-detail fabric texture luxury sporty tailoring visible stitching realism premium performance silhouette UI ELEMENTS: sport-inspired SNITCH interface system drop countdown graphics collection code labels motion-speed typography overlays floating stitched-label graphics minimal black-orange UI accents performance feature tags TYPOGRAPHY: Huge bold headline: “MOVE DIFFERENT.” subheading: “Built for speed, style, and presence.” CTA: “EXPLORE DROP” GRAPHICS: kinetic motion streaks athletic grid overlays industrial sports graphics minimal energy textures performance-driven composition LIGHTING: harsh summer sunlight realistic athletic highlights high-detail shadow realism premium sports-commercial exposure CAMERA: Canon EOS R5 35mm sports-fashion lens f/1.8 commercial lifestyle photography STYLE: Nike lifestyle × Fear of God × SNITCH premium Indian sport-luxury fashion

Prompt
Agentic Thinking Mod
After

2K Output & Mask Editing

Native output up to 2K (2048px) with neutral, accurate color — the warm cast from gpt-image-1.5 is gone. Inpaint or outpaint precise regions via mask images while untouched pixels stay pixel-identical.

A single finished epic fantasy adventure movie poster, one unified cinematic composition. A cloaked hero standing on a cliff overlooking a burning golden kingdom, dramatic storm light, sweeping epic scale, rich saturated colors, the figure in the lower third. IMPORTANT: this must be ONE complete movie poster only — NOT a character design sheet, NO turnaround views, NO multiple angles, NO head-study panels, NO annotation grids. At the top, the film title in large bold cinematic serif lettering: "EMBERFALL". Near the bottom a small tagline: "Every throne is built on ashes." Leave clean negative space for the text. 2:3 --ar 2:3

Prompt
arrow
2K Output & Mask Editing
After

2K Output & Mask Editing

Native output up to 2K (2048px) with neutral, accurate color — the warm cast from gpt-image-1.5 is gone. Inpaint or outpaint precise regions via mask images while untouched pixels stay pixel-identical.

arrow

A single finished epic fantasy adventure movie poster, one unified cinematic composition. A cloaked hero standing on a cliff overlooking a burning golden kingdom, dramatic storm light, sweeping epic scale, rich saturated colors, the figure in the lower third. IMPORTANT: this must be ONE complete movie poster only — NOT a character design sheet, NO turnaround views, NO multiple angles, NO head-study panels, NO annotation grids. At the top, the film title in large bold cinematic serif lettering: "EMBERFALL". Near the bottom a small tagline: "Every throne is built on ashes." Leave clean negative space for the text. 2:3 --ar 2:3

Prompt
2K Output & Mask Editing
After

What Is GPT Image 2?

gpt-image-2 is OpenAI's image generation and editing model, released April 21, 2026 as the successor to gpt-image-1 (April 2025) and gpt-image-1.5 (December 2025). It is natively multimodal — image generation is part of the core model rather than a diffusion model bolted onto a language model — which is why it follows long, multi-part prompts and renders readable in-image text more reliably than DALL-E-era generators.

Two things set the model apart at the API level. First, an agentic "thinking" pass: for complex prompts it plans composition and reasons through constraints before generating, which lifts success rates on infographics, multi-panel layouts, and text-heavy marketing assets. Second, an editing endpoint that accepts mask images for precise inpainting and outpainting, plus up to 16 reference images per call for identity and style consistency. Output is PNG at up to 2K native resolution, with neutral color that fixes the warm cast in gpt-image-1.5.

On GPTProto you call the same gpt-image-2 model ID through the standard OpenAI-compatible request shape — no separate OpenAI account, no organization verification step, and one balance that also covers Nano Banana Pro, Seedream, Flux, and 200+ other models.

GPT Image 2 Specifications

Spec GPT Image 2
Model ID gpt-image-2
Released April 21, 2026
Type Text-to-image + image editing (inpaint / outpaint)
Output format PNG (raster)
Max resolution Native 2K (2048px); high-res variants up to ~4K
In-image text Latin + CJK (Chinese / Japanese / Korean), dense layouts
Reasoning Agentic "thinking mode" (plans before rendering)
Reference images Up to 16 per call
Editing Mask-based inpaint / outpaint; unedited pixels preserved
Input modality Text (+ reference images)
OpenAI list price $8 / $30 per 1M tokens (input / output)
GPTProto price $6.4 / $24 per 1M tokens (20% under list)
Access One GPTProto key, no OpenAI org verification

GPT Image 2 vs Nano Banana Pro (and gpt-image-1.5)

  GPT Image 2 Nano Banana Pro GPT Image 1.5
Model ID gpt-image-2 gemini-3-pro-image-preview gpt-image-1.5
Vendor OpenAI Google OpenAI
Released Apr 2026 Nov 2025 Dec 2025
Max resolution 2K native (~4K variants ) up to 4K (4096px) 2K
In-image text Latin + CJK multilingual, long passages improved vs gpt-image-1
Reasoning agentic thinking Gemini 3 reasoning + Search grounding none
Reference inputs up to 16 images multi-image, ~5-subject identity fewer
OpenAI/Google list $8 / $30 per 1M $2 / $12 per 1M $8 / $32 per 1M
GPTProto price $6.4 / $24 per 1M $ 0.0804/ per time

$ 5.6 / $ 22.4 per 1M

On GPTProto ✓ ✓ ✓

Which to pick (honest):  Nano Banana Pro has the lower token rate, native 4K, and Search-grounded generation — reach for it when cost, 4K infographics, or real-world-grounded visuals matter most. GPT Image 2 leads on agentic layout planning, up to 16 reference images, and tight drop-in compatibility with the OpenAI SDK — reach for it for reference-heavy product/packaging work already wired to OpenAI. Because both run on one GPTProto balance, you can benchmark them against your own prompts without opening a second account.

Switching from the Official OpenAI API

If you already call gpt-image-2 on OpenAI, moving to GPTProto is a base-URL swap — the model ID and request shape stay the same:

  • Keep model: "gpt-image-2" and your existing images.generate / edit request body.
  • Point the client at GPTProto's OpenAI-compatible base URL  https://gptproto.com/v1 and use your GPTProto key.
  • Skip OpenAI's Organization Verification gate that fronts the GPT Image family, and skip a second billing account — the same balance covers 200+ models.

How to Get a gpt-image-2 API Key

Getting a gpt-image-2 API key takes four steps and a few minutes. Create a free GPTProto account, add credits, generate your key, and make your first call — at $6.4 / $24 it's a cheaper gpt-image-2 API key than going direct, and one key works across every model on the platform. Full gpt-image-2 Documentation is in the docs.

Sign up

Sign up

Create your free GPT Proto account to begin. You can set up an organization for your team at any time.

Top up

Top up

Your balance can be used across all models on the platform, including gpt-image-2, giving you the flexibility to experiment and scale as needed.

Generate your API key

Generate your API key

In your dashboard, create an API key — you'll need it to authenticate when making requests to gpt-image-2.

Make your first API call

Make your first API call

Use your API key with our sample code to send a request to gpt-image-2 via GPT Proto and see instant AI-powered results.

Get API Key

gpt image 2 api: Common Questions

Find technical details and usage tips for the gpt image 2 api to enhance your creative workflow and image quality.

How much does the GPT Image 2 API cost?

On GPTProto, gpt-image-2 is $6.4 / $24 per 1M input / output tokens — 20% under OpenAI's $8 / $30 list. Billing draws from one shared balance, so the same key also runs Nano Banana Pro, Seedream, and 200+ other models.

How do I get a GPT Image 2 API key?

Create a free GPTProto account, add credits, and generate a key in the dashboard — it authenticates every model on the platform, including gpt-image-2. No OpenAI organization verification required.

GPT Image 2 vs Nano Banana Pro — which is better?

Nano Banana Pro (Gemini 3 Pro Image) is cheaper per token, does native 4K, and grounds images in Search; gpt-image-2 leads on agentic layout planning and up to 16 reference images. Both run on one GPTProto key, so you can A/B them directly.

Does the GPT Image 2 API support image editing and references?

Yes. The editing endpoint takes mask images for precise inpaint / outpaint, and you can pass up to 16 reference images per call to hold subject and style consistency.

How do I improve gpt image 2 api lighting?

The gpt image 2 api has a significantly better understanding of lighting by default. To maximize this, use descriptive prompts regarding light sources. While some competitors handle flash instructions differently, the gpt api excels at reacting to environmental light within the generated scene automatically. By specifying 'soft morning light' or 'harsh neon', you guide the gpt image 2 engine to render realistic gpt shadows.

Is prompting different for gpt image 2 api?

Effective gpt image 2 api usage relies on specificity. Instead of broad terms, describe the scene in detail—such as a residential bathroom under construction. For cleaner results, especially in nature, use negative prompts to block speckling or grain patterns, ensuring the gpt image output remains sharp. The gpt image 2 api responds best to prompts around 20 words that focus on gpt material and gpt environmental details.

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