Imagen 4
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Organisation type
- Industry
- Country
- United States of America
- Published
- 20 May 2025
- Authors
- Gabriel Barcik, Jakob Bauer, Dana Berman, Nicole Brichtova, Lluis Castrejon, Matan Cohen, Sander Dieleman, Yuqing Du, Praneet Dutta, Jess Gallegos, Yilin Gao, Evgeny Gladchenko, Susan Hao, Ruba Haroun, Ed Hirst, Tobenna Peter Igwe, Xuhui Jia, Siavash Khodadadeh, Pavel Khrushkov, Karol Langner, Rory Lawton, Yinxiao Li, Yandong Li, Shixin Luo, Michael Mathieu, Soňa Mokrá, Aäron van den Oord, Lily Pa…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation, Text-to-image
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- API access
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Significant use
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Imagen 4 Model Card
- Last updated
- 28 November 2025
What the numbers mean
About this model
Imagen 4 was published by Google, in the country recorded as United States of America, during May 2025. The publishing organisation is categorised as industry.
It works in the domain of Image generation, and is recorded as performing the task of image generation, Text-to-image.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The reason it appears in this catalogue at all: significant use.
Answers
Imagen 4 — common questions
Imagen 4— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Imagen 4— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Imagen 4— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Imagen 4— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
Imagen 4— when was it released?
It was published in May 2025.
Imagen 4— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image generation, Text-to-image. These are the areas it was designed around; they describe intent rather than a hard boundary.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.