Clip Vit Base Patch 16
OpenAI's Clip Vit Base Patch 16 model is a research-oriented AI model that utilizes a ViT-B/16 Transformer architecture as an image encoder and a masked self-attention Transformer as a text encoder, trained with a contrastive loss to maximize the similarity of image-text pairs. It is genuinely best at enabling researchers to explore zero-shot, arbitrary image classification and understand robustness, generalization, and other capabilities of computer vision models.
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Frequently asked questions about Clip Vit Base Patch 16
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Clip Vit Base Patch 16 advertises a context window of 77 tokens. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Clip Vit Base Patch 16 is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Clip Vit Base Patch 16 accepts Image and produces Text. 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 Clip Vit Base Patch 16 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; Clip Vit Base Patch 16 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 Clip Vit Base Patch 16, 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, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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