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Why language models hallucinate
OpenAI’s new research explains why language models hallucinate. The findings show how improved evaluations can enhance AI reliability, honesty, and safety.
Collective alignment: public input on our Model Spec
OpenAI surveyed over 1,000 people worldwide on how AI should behave and compared their views to our Model Spec. Learn how collective alignment is shaping AI defaults to better reflect diverse human values and perspectives.
OpenAI and Anthropic share findings from a joint safety evaluation
OpenAI and Anthropic share findings from a first-of-its-kind joint safety evaluation, testing each other’s models for misalignment, instruction following, hallucinations, jailbreaking, and more—highlighting progress, challenges, and the value of cross-lab collaboration.
Helping people when they need it most
How we think about safety for users experiencing mental or emotional distress, the limits of today’s systems, and the work underway to refine them.
Accelerating life sciences research
Discover how a specialized AI model, GPT-4b micro, helped OpenAI and Retro Bio engineer more effective proteins for stem cell therapy and longevity research.
Medical research with GPT-5
Learn how GPT-5 is used for medical research.
How Amgen uses GPT-5
Learn how Amgen uses GPT-5.
From hard refusals to safe-completions: toward output-centric safety training
Discover how OpenAI's new safe-completions approach in GPT-5 improves both safety and helpfulness in AI responses—moving beyond hard refusals to nuanced, output-centric safety training for handling dual-use prompts.
Estimating worst case frontier risks of open weight LLMs
In this paper, we study the worst-case frontier risks of releasing gpt-oss. We introduce malicious fine-tuning (MFT), where we attempt to elicit maximum capabilities by fine-tuning gpt-oss to be as capable as possible in two domains: biology and cybersecurity.
OpenAI’s new economic analysis
Analysis provides insights into ChatGPT’s impact on the economy. OpenAI also launches new research collaboration to study AI’s broader effects on the labor market and productivity.
Toward understanding and preventing misalignment generalization
We study how training on incorrect responses can cause broader misalignment in language models and identify an internal feature driving this behavior—one that can be reversed with minimal fine-tuning.
Preparing for future AI risks in biology
Advanced AI can transform biology and medicine—but also raises biosecurity risks. We’re proactively assessing capabilities and implementing safeguards to prevent misuse.
Disrupting malicious uses of AI: June 2025
Our latest report featuring case studies of how we’re detecting and preventing malicious uses of AI.
Introducing HealthBench
HealthBench is a new evaluation benchmark for AI in healthcare which evaluates models in realistic scenarios. Built with input from 250+ physicians, it aims to provide a shared standard for model performance and safety in health.
Expanding on what we missed with sycophancy
A deeper dive on our findings, what went wrong, and future changes we’re making.
Our updated Preparedness Framework
Sharing our updated framework for measuring and protecting against severe harm from frontier AI capabilities.
BrowseComp: a benchmark for browsing agents
BrowseComp: a benchmark for browsing agents.
New commission to provide insight as OpenAI builds the world’s best-equipped nonprofit
Already a nonprofit, and already using AI to help people solve hard problems, OpenAI aims to build the best-equipped nonprofit the world has ever seen—combining potentially historic financial resources with something even more powerful: technology that can scale human ingenuity itself.
PaperBench: Evaluating AI’s Ability to Replicate AI Research
We introduce PaperBench, a benchmark evaluating the ability of AI agents to replicate state-of-the-art AI research.
Moving from intent-based bots to proactive AI agents
Moving from intent-based bots to proactive AI agents.