Introducing 4o Image Generation

Introducing 4o Image Generation | OpenAI

March 25, 2025

Introducing 4o Image Generation

Unlocking useful and valuable image generation with a natively multimodal model capable of precise, accurate, photorealistic outputs.

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At OpenAI, we have long believed image generation should be a primary capability of our language models. That’s why we’ve built our most advanced image generator yet into GPT‑4o. The result—image generation that is not only beautiful, but useful.

A wide image taken with a phone of a glass whiteboard, in a room overlooking the Bay Bridge. The field of view shows a woman writing, sporting a tshirt wiith a large OpenAI logo. The handwriting looks natural and a bit messy, and we see the photographer's reflection.The text reads:(left)"Transfer between Modalities:Suppose we directly model p(text, pixels, sound) [equation]with one big autoregressive transformer.Pros:* image generation augmented with vast world knowledge* next-level text rendering* native in-context learning* unified post-training stackCons:* varying bit-rate across modalities* compute not adaptive"(Right)"Fixes:* model compressed representations* compose autoregressive prior with a powerful decoder"On the bottom right of the board, she draws a diagram:"tokens -> [transformer] -> [diffusion] -> pixels"

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selfie view of the photographer, as she turns around to high five him

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Useful image generation

From the first cave paintings to modern infographics, humans have used visual imagery to communicate, persuade, and analyze—not just to decorate. Today's generative models can conjure surreal, breathtaking scenes, but struggle with the workhorse imagery people use to share and create information. From logos to diagrams, images can convey precise meaning when augmented with symbols that refer to shared language and experience.

GPT‑4o image generation excels at accurately rendering text, precisely following prompts, and leveraging 4o’s inherent knowledge base and chat context—including transforming uploaded images or using them as visual inspiration. These capabilities make it easier to create exactly the image you envision, helping you communicate more effectively through visuals and advancing image generation into a practical tool with precision and power.

Improved capabilities

We trained our models on the joint distribution of online images and text, learning not just how images relate to language, but how they relate to each other. Combined with aggressive post-training, the resulting model has surprising visual fluency, capable of generating images that are useful, consistent, and context-aware.

Text rendering

A picture is worth a thousand words, but sometimes generating a few words in the right place can elevate the meaning of an image. 4o’s ability to blend precise symbols with imagery turns image generation into a tool for visual communication.

Create a photorealistic image of two witches in their 20s (one ash balayage, one with long wavy auburn hair) reading a street sign. Context: a city street in a random street in Williamsburg, NY with a pole covered entirely by numerous detailed street signs (e.g., street sweeping hours, parking permits required, vehicle classifications, towing rules), including few ridiculous signs at the middle: (paraphrase it to make these legitimate street signs)"Broom Parking for Witches Not Permitted in Zone C" and "Magic Carpet Loading and Unloading Only (15-Minute Limit)" and "Reindeer Parking by Permit Only (Dec 24–25) Violators will be placed on Naughty List." The signpost is on the right of a street. Do not repeat signs. Signs must be realistic.Characters:one witch is holding a broom and the other has a rolled-up magic carpet. They are in the foreground, back slightly turned towards the camera and head slightly tilted as they scrutinize the signs.Composition from background to foreground:streets + parked cars + buildings -> street sign -> witches. Characters must be closest to the camera taking the shot

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Multi-turn generation

Because image generation is now native to GPT‑4o, you can refine images through natural conversation. GPT‑4o can build upon images and text in chat context, ensuring consistency throughout. For example, if you’re designing a video game character, the character’s appearance remains coherent across multiple iterations as you refine and experiment.

Give this cat a detective hat and a monocle

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turn this into a triple A video games made with a 4k game engine and add some User interface as overlay from a mystery RPG where we can see a health bar and a minimap at the top as well as spells at the bottom with consistent and iconography

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update to a landscape image 16:9 ratio, add more spells in the UI, and unzoom the visual so that we see the cat in a third person view walking through a steampunk manhattan creating beautiful contrast and lighting like in the best triple A game, with cool-toned colors

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create the interface when the player opens the menu and we see the cat's character profile with his equipment and another page showing active quests (and it should make sense in relationship with the universe worldbuilding we are describing in the image)

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credit creator: Manuel Sainsily

Instruction following

GPT‑4o’s image generation follows detailed prompts with attention to detail. While other systems struggle with ~5-8 objects, GPT‑4o can handle up to 10-20 different objects. The tighter binding of objects to their traits and relations allows for better control.

A square image containing a 4 row by 4 column grid containing 16 objects on a white background. Go from left to right, top to bottom. Here's the list:1. a blue star2. red triangle3. green square4. pink circle5. orange hourglass6. purple infinity sign7. black and white polka dot bowtie8. tiedye "42"9. an orange cat wearing a black baseball cap10. a map with a treasure chest11. a pair of googly eyes12. a thumbs up emoji13. a pair of scissors14. a blue and white giraffe15. the word "OpenAI" written in cursive16. a rainbow-colored lightning bolt

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In-context learning

GPT‑4o can analyze and learn from user-uploaded images, seamlessly integrating their details into its context to inform image generation.

• draw a design for a vehicle with triangular wheels, using these images as reference.
• label the front wheel, the back…