GPT-5.5 vs CLIP

GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.

Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.

OpenAI CLIP (Contrastive Language-Image Pretraining) is a vision-language model released in January 2021 by OpenAI. It jointly trains an image encoder and a text encoder to produce matching embeddings for image-caption pairs, using a contrastive objective over WebImageText (WIT), a dataset of 400 million image-text pairs collected from the public web. By learning to associate images with free-form text rather than a fixed set of class labels, CLIP produces a shared embedding space that enables zero-shot classification with arbitrary vocabularies at inference time.

CLIP supports zero-shot image classification by embedding candidate class labels as text and selecting the label whose embedding is closest to a given image's embedding. It is also widely used for image-text retrieval, as a frozen backbone in downstream vision-language models, and as a building block for content moderation, similarity search, and generative model guidance — notably as the text conditioning mechanism in early versions of Stable Diffusion. OpenAI released several CLIP variants built on different vision encoders, including ResNet and Vision Transformer backbones at multiple sizes and input resolutions, with ViT-L/14 at 336 pixels being the largest and most widely adopted. CLIP is distributed under the MIT license. The model has been widely influential as the basis for subsequent vision-language work — including SigLIP, OpenCLIP, and MetaCLIP — and remains a common reference baseline despite being released in 2021 and surpassed on many benchmarks by later models.