OpenAI CLIP

CLIP (Contrastive Language-Image Pre-training) is a foundational neural network architecture developed by OpenAI that efficiently learns visual concepts from natural language supervision. Unlike traditional computer vision models trained on discrete labels, CLIP uses a dual-encoder architecture—a Vision Transformer (ViT) or ResNet for images and a Transformer for text—to project both modalities into a shared high-dimensional latent space. As of 2026, CLIP remains the dominant backbone for semantic image retrieval, content moderation, and text-to-image generation systems (like Stable Diffusion). Its primary strength lies in 'zero-shot' transfer, allowing it to classify objects it has never seen in a specific training set by simply providing a text description. The model's robustness to distribution shifts makes it superior to traditional supervised models for real-world applications where data noise is high. In the 2026 market, it is primarily deployed via vector databases for multi-modal RAG (Retrieval-Augmented Generation) and enterprise-grade visual search engines, often utilized through optimized implementations like OpenCLIP or hosted inference endpoints.