Clip Vit Large Patch 14
OpenAI's Clip Vit Large Patch 14 is a research-oriented model utilizing a ViT-L/14 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.
Input
Output
Context
0K
Max Output
0K
Parameters
427.6M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Resources & Links
Estimated GPU Requirements for Clip Vit Large Patch 14
Estimates based on INT8 quantization. Actual requirements vary by framework and configuration.
Related Tools
Data sourced from official provider APIs and documentation
Last updated: Jun 24, 2026
Start building with the right model.
Automatically route workloads to the right model for every task, every time.