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GPT Image 2 Available Now

GPT Image 2: The AI Image Model That Plans Before It Draws.

GPT Image 2is OpenAI's first image model built with a reasoning pipeline — it researches, plans, and self-checks a prompt before generating a single pixel. The result is reliable text rendering across scripts, accurate multi-element compositions, and structured output that other models approximate but rarely get right.

What Makes GPT Image 2 Different?

GPT Image 2 is the first image model where the generation step is preceded by a reasoning step. Before rendering, the model interprets the prompt semantically — understanding layout intent, text content, compositional constraints, and real-world context — and builds a plan. This is why it handles the kinds of prompts that trip up diffusion models: dense typography, precise multi-element layouts, diagrams, and anything where accuracy matters more than approximation.

The practical effect is that you don't need to simplify your prompt to be understood. Write what you actually need — with all the layout specifics, text content, and compositional detail — and the model treats that as an instruction set rather than a loose suggestion. On Image Arena, it ranks #1 across all leaderboards, with a 1,512 score in Text-to-Image and a +242 point lead over the next model.

GPT IMAGE 2 CAPABILITIES

Four Capabilities Worth Understanding

Each one addresses a limitation that makes other AI image models unreliable for production work.

Text Rendering

Multi-line headlines, dense fine print, signage, labels, and CJK characters render as readable text — not visual noise. The model understands what the text says, not just what letters look like.

Reasoning Pipeline

GPT Image 2 plans before it generates. Complex prompts with multiple elements, layout requirements, and compositional constraints get interpreted as a structured instruction set — not a probabilistic best guess.

Mask-Based Editing

Inpainting and outpainting via a dedicated edit endpoint. Supply a plain-language instruction with or without a mask — the model handles segmentation, relighting, and compositing internally. Multi-pass edits don't accumulate artifacts.

Structured Generation

Diagrams, infographics, charts, posters, and comics — content where compositional accuracy matters — are where GPT Image 2 pulls away from other models. Structure is understood, not approximated.

GPT Image 2 Technical Specifications

The exact parameters available when you run GPT Image 2 inside the VidTool AI workspace.

Resolutions
1K / 2K / 4K (max edge 3840px)
Aspect ratios
1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
Quality
Low / Medium / High
Output formats
PNG (default) / JPEG / WebP
Transparent background
Not supported
Editing
Inpainting + outpainting via dedicated edit endpoint — mask optional
Knowledge cutoff
December 2025
Benchmark
#1 on Image Arena across all leaderboards · 1,512 score in Text-to-Image · +242 point lead

How to Generate Images with GPT Image 2

From prompt to finished image — or from existing image to refined output — in four steps.

1

Choose your mode

Start fresh with text-to-image generation, or upload an existing image to edit it using inpainting, outpainting, or a plain-language instruction.

2

Write a detailed prompt

GPT Image 2's reasoning pipeline handles complexity — don't simplify your prompt to avoid confusion. Specify layout, text content, colors, and composition directly. The model plans before it renders.

3

Select resolution, aspect ratio & quality

Choose from 1K, 2K, or 4K output across ten aspect ratios. Use Low quality for fast iteration, High quality for final production output.

4

Refine with edits & export

Use the edit mode to adjust specific regions, extend the canvas, or replace elements with a plain-language instruction. Export as PNG, JPEG, or WebP when done.

Generated with GPT Image 2

Real outputs from the model — no post-processing, no cherry-picking of settings.

GPT Image 2 — Van Gogh style double exposure portrait with swirling starry sky and sunflowers

Van Gogh style portrait

Double exposure · post-impressionism · detailed artistic direction

GPT Image 2 — realistic livestream screenshot with accurate UI elements and text

Live stream screenshot

Accurate UI rendering · readable on-screen text · realistic lighting

GPT Image 2 — European girl group poster with consistent multi-subject composition

Group poster

Multi-subject composition · consistent style · poster layout

FAQ

Frequently Asked Questions about GPT Image 2

Technical questions about OpenAI's GPT Image 2, answered plainly.

What is GPT Image 2 and how does it differ from DALL-E 3?

GPT Image 2 is OpenAI's image generation model built directly into the GPT architecture, replacing DALL-E 3 as OpenAI's primary image model. The fundamental difference is the generation pipeline: GPT Image 2 reasons about a prompt before producing any pixels, which makes it significantly more reliable on complex, multi-element compositions, precise layouts, and prompts that require real-world knowledge. DALL-E 3 used a diffusion approach and had no reasoning step.

Why does GPT Image 2 render text so much better than other image models?

Most AI image models generate text as visual texture — they approximate what letters look like without understanding what they say. GPT Image 2's reasoning pipeline processes the prompt semantically first, so it treats text in an image the way a typesetter would: understanding content, layout constraints, and readability before rendering. The result is that multi-line headlines, signage, labels, and CJK characters hold together correctly rather than drifting into visual noise.

How does the reasoning pipeline work in practice?

Before generating a single pixel, GPT Image 2 researches, plans, and self-checks the prompt. For a complex request — say, a product ad with multiple text elements, a specific layout, and a branded color scheme — it builds a plan for the composition and verifies it against the constraints in the prompt. This is why dense, multi-requirement prompts produce better results than they would with a diffusion model: you don't need to simplify your prompt to avoid confusion.

How does the edit endpoint work?

The edit endpoint accepts an existing image, an optional mask, and a plain-language instruction. White areas in the mask are modified; black areas are preserved. You can use it for inpainting (replacing a specific region), outpainting (extending the image beyond its current edges), or global edits (removing an object, replacing a background, changing lighting) without a mask. Multi-pass edits on the same image generally don't accumulate artifacts.

What output formats does GPT Image 2 support?

GPT Image 2 outputs PNG, JPEG, and WebP. PNG is the default and preserves maximum quality. JPEG reduces file size with minimal perceptual loss and is faster to transfer when latency matters. WebP offers a balance of quality and compression. Note: transparent backgrounds are not supported — requests that include transparency will fail.

What resolutions and aspect ratios are available?

VidTool AI offers 1K, 2K, and 4K output at ten aspect ratios: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, and 21:9. The underlying API supports any custom dimension where both edges are multiples of 16, no single edge exceeds 3840px, the aspect ratio stays under 3:1, and the total pixel count falls between 655,360 and 8,294,400.

What types of images is GPT Image 2 best suited for?

GPT Image 2 performs strongest on work that rewards accuracy over texture: structured layouts (diagrams, infographics, charts, posters, comics), images with readable text (signage, labels, multilingual content), product photography, UI mockups, and anything requiring precise instruction-following across multiple elements. For highly artistic or painterly output where looseness is a feature, other models may suit specific aesthetics better.

Does GPT Image 2 have a knowledge cutoff?

Yes — GPT Image 2's knowledge cutoff is December 2025. This means it has built-in awareness of brands, products, cultural references, and visual conventions up to that date. For prompts that depend on recognizing real-world context, logos, or styles, this grounding improves generation accuracy over models with earlier cutoffs.

Learn more from the official OpenAI GPT Image 2 announcement →

Last updated: June 6, 2026

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