OpenAIImageGPT Small
OpenAI's ImageGPT Small is a transformer decoder model pretrained on 14 million images from ImageNet ILSVRC 2012 at a resolution of 32x32 pixels, learning to predict the next pixel value given the previous ones. It is genuinely best at learning an inner representation of images for downstream tasks such as feature extraction and unconditional image generation.
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1K
Max Output
n/a
Parameters
-
Technical Specifications
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Frequently asked questions about ImageGPT Small
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
ImageGPT Small advertises a context window of 1,024 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. ImageGPT Small is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
ImageGPT Small accepts Image and produces Image and Embedding. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for ImageGPT Small is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; ImageGPT Small is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
OpenAI has not published a parameter count for ImageGPT Small, so the catalog cannot estimate its memory footprint. The capacity planner can size it from a parameter count you supply.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 4, 2026
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