Model release

CLIP ViT-L/14

CLIP ViT-L/14 is an open-weight model release in the CLIP family from OpenAI3 1, version ViT-L/14, released Jan 2022. Its weights can be downloaded by the public2 6 under the MIT License (openai/CLIP code)5.

CLIP ViT-L/14 pairs a ViT-L/14 Vision Transformer image encoder with a masked self-attention Transformer text encoder, trained contrastively to match images with their captions. OpenAI released it in January 2022; a variant further trained at 336-pixel resolution (ViT-L/14@336px) followed in April 2022.1 3 9

Model hub: Model card (Hugging Face) (external site: huggingface.co)
Repository: openai/CLIP repository (external site: github.com)
License: LICENSE (MIT, code) (external site: github.com)
Paper: CLIP paper (arXiv 2103.00020) (external site: arxiv.org)

Last reviewedSep 29, 2026Entry updated Sep 29, 2026Documented release Jan 2022

Availability and license

Overall availability

Public

Downloadable without gating from Hugging Face, and fetched by the openai/CLIP package's clip.load("ViT-L/14"). No license for the weights is stated (see license notes).2 6

Availability fact review: not separately recorded

Availability is separate from permission: read the license before using or redistributing.

MIT License (openai/CLIP code) (external site: github.com)5
Applies to: Code · SPDX MIT · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the MIT License

The repository's MIT license covers "the Software" (the code). This review found no license statement for the model weights in the repository README, the model card, or the Hugging Face card, whose metadata carries no license field. Separately from licensing, the model card places all deployed uses out of scope.5 4 3 2

Component reuse rights

A readable or downloadable component is not automatically reusable. These indicators concern recorded license evidence, not system certification.

weights: Unknown — no complete fact-level rights review
code: Unknown — no complete fact-level rights review
data: Unknown — no complete fact-level rights review
documentation: Unknown — no complete fact-level rights review

No complete system-rights review is recorded for this release.

Model-disclosure tier

Computed from the checklist below using USASI rubric v0.2. An editorial category, not a certification.

Open-weight

The model parameters for this release can be downloaded by the public. License terms may still restrict use, redistribution, or commercial use.

No license for the weights is recorded in this catalog.

How tiers are computed

Public materials checklist

Items for a model under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.

Public materials checklist for CLIP ViT-L/14
Item	Status	Notes and evidence
WeightsCan the general public download the model parameters for this release?	Public	Published on Hugging Face without gating and downloadable through clip.load().2 6
Inference codeIs code for running the model published?	Public	The openai/CLIP package loads the model and encodes images and text; the Hugging Face card documents use with Transformers.4 1
Training codeIs the code used to train the model published?	Unknown	The repository provides loading, inference, and evaluation examples; this review found no published training code.4
Training-data informationDoes the information cover provenance, scope, acquisition, selection, labeling, processing, and where data or alternatives can be obtained? Access alone does not establish completeness.	Unknown	Not assessed.
Training-data accessCan the training data be obtained? This is independent of information completeness and reuse rights; original unshareable data need not be downloadable.	Unknown	Not assessed.
Complete training pipelineIs the complete base-training and preprocessing pipeline published, including configuration? Fine-tuning code or an inference SDK alone is insufficient.	Unknown	Not assessed.
Legacy data assessment (v0.1)Historical assessment combining download access and disclosure. Preserved for traceability; excluded from the v0.2 tier calculation. See the new separate assessments above.	Partial	The paper describes a dataset of 400 million image-text pairs gathered from public internet sources using 500,000 search queries. The model card says OpenAI will not release the dataset; only a list identifying a YFCC100M subset used in an ablation is published.9 3 8
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Partial	The paper describes training for 32 epochs with Adam, decoupled weight decay, a cosine schedule, and a 32,768 minibatch, and says the largest Vision Transformer took 12 days on 256 V100 GPUs. No training configuration files are published.9
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Public	The repository publishes the class names and prompt templates used for the paper's zero-shot results, an ImageNet prompt-engineering notebook, and a linear-probe example.7 4

What it is useful for

Zero-shot image classification and image-text similarity for research. The model card says any deployed use case, commercial or not, is currently out of scope, surveillance and facial recognition are always out of scope, and use should be limited to English.1 3

Run and use notes

Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.

The README documents installation with PyTorch 1.7.1 or later and torchvision, then loading a model by name with clip.load(); the Hugging Face card documents use through Transformers' CLIPModel and CLIPProcessor.4 1

Organization context

OpenAI

Other releases in the CLIP family

No other releases in this family have been assessed.

CLIP family overview

What this catalog does not know

Unknown means the sources reviewed for this record do not document it. It is not evidence that something does not exist.

Training code: unknown.
Training-data information: unknown.
Training-data access: unknown.
Complete training pipeline: unknown.

Have a primary source? How to report a correction.

U.S. eligibility

Project eligibility rests on documented governing or maintaining entities, not on contributors.

Eligible · basis: U.S. headquarters

The model card states that CLIP was developed by researchers at OpenAI, and the checkpoint is published by OpenAI in its openai/CLIP repository and Hugging Face account. OpenAI Group PBC lists its address as 1455 3rd Street, San Francisco, California, in a February 2026 agreement filed with the SEC.3 6 10

Assessed Sep 29, 2026

Sources

1.
openai/clip-vit-large-patch14 model card (external site: huggingface.co)
OpenAI (Hugging Face) · Model card · accessed Sep 29, 2026
2.
openai/clip-vit-large-patch14 (Hugging Face model metadata) (external site: huggingface.co)
OpenAI (Hugging Face) · Model card · accessed Sep 29, 2026
3.
Model Card: CLIP (external site: github.com)
OpenAI (GitHub) · Model card · accessed Sep 29, 2026
4.
openai/CLIP README (external site: github.com)
OpenAI (GitHub) · Repository · accessed Sep 29, 2026
5.
openai/CLIP LICENSE (external site: github.com)
OpenAI (GitHub) · License · accessed Sep 29, 2026
6.
openai/CLIP clip/clip.py (checkpoint download URLs) (external site: github.com)
OpenAI (GitHub) · Repository · accessed Sep 29, 2026
7.
openai/CLIP data/prompts.md (zero-shot class names and templates) (external site: github.com)
OpenAI (GitHub) · Documentation · accessed Sep 29, 2026
8.
openai/CLIP data/yfcc100m.md (YFCC100M subset) (external site: github.com)
OpenAI (GitHub) · Documentation · accessed Sep 29, 2026
9.
Learning Transferable Visual Models From Natural Language Supervision (arXiv 2103.00020, PDF) (external site: arxiv.org)
arXiv (OpenAI authors) · Paper · published Feb 26, 2021 · accessed Sep 29, 2026
10.
Exhibit 10.1: Equity commitment letter agreement between OpenAI Group PBC and Amazon (external site: sec.gov)
U.S. Securities and Exchange Commission (Amazon.com, Inc. filing) · Filing · published Feb 27, 2026 · accessed Sep 29, 2026

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APA style

USASI. (2026, September 29). CLIP ViT-L/14. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/clip-vit-large-patch14/

BibTeX

@misc{usasi_clip_vit_large_patch14,
  author = {{USASI}},
  title = {CLIP ViT-L/14},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/clip-vit-large-patch14/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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