OpenAI GPT-5 System Card

This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say 'think hard about this' in the prompt). The router is continuously trained on real signals, including when users switch models, preference rates for responses, and measured correctness, improving over time. Once usage limits are reached, a mini version of each model handles remaining queries. This system card focuses primarily on gpt-5-thinking and gpt-5-main, while evaluations for other models are available in the appendix. The GPT-5 system not only outperforms previous models on benchmarks and answers questions more quickly, but -- more importantly -- is more useful for real-world queries. We've made significant advances in reducing hallucinations, improving instruction following, and minimizing sycophancy, and have leveled up GPT-5's performance in three of ChatGPT's most common uses: writing, coding, and health. All of the GPT-5 models additionally feature safe-completions, our latest approach to safety training to prevent disallowed content. Similarly to ChatGPT agent, we have decided to treat gpt-5-thinking as High capability in the Biological and Chemical domain under our Preparedness Framework, activating the associated safeguards. While we do not have definitive evidence that this model could meaningfully help a novice to create severe biological harm -- our defined threshold for High capability -- we have chosen to take a precautionary approach.

CUTS: A Fully Unsupervised Framework for Medical Image Segmentation

In this work we introduce CUTS (Contrastive and Unsupervised Training for Segmentation) the first fully unsupervised deep learning framework for medical image segmentation, facilitating the use of the vast majority of imaging data that is not labeled or annotated. Segmenting medical images into regions of interest is a critical task for facilitating both patient diagnoses and quantitative research. A major limiting factor in this segmentation is the lack of labeled data, as getting expert annotations for each new set of imaging data or task can be expensive, labor intensive, and inconsistent across annotators: thus, we utilize self-supervision based on pixel-centered patches from the images themselves. Our unsupervised approach is based on a training objective with both contrastive learning and autoencoding aspects. Previous contrastive learning approaches for medical image segmentation have focused on image-level contrastive training, rather than our intra-image patch-level approach or have used this as a pre-training task where the network needed further supervised training afterwards. By contrast, we build the first entirely unsupervised framework that operates at the pixel-centered-patch level. Specifically, we add novel augmentations, a patch reconstruction loss, and introduce a new pixel clustering and identification framework. Our model achieves improved results on several key medical imaging tasks, as verified by held-out expert annotations on the task of segmenting geographic atrophy (GA) regions of images of the retina.