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We’ve implemented initial support for plugins in ChatGPT. Plugins are tools designed specifically for language models with safety as a core principle, and help ChatGPT access up-to-date information, run computations, or use third-party services.
GPT-5.3-Codex is a Codex-native agent that pairs frontier coding performance with general reasoning to support long-horizon, real-world technical work.
Developers can now integrate GPT‑3.5 Turbo and Whisper models into their apps and products through our API.
We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.
The updated Codex app for macOS and Windows adds computer use, in-app browsing, image generation, memory, and plugins to accelerate developer workflows.
Introducing GPT-5.3-Codex-Spark—our first real-time coding model. 15x faster generation, 128k context, now in research preview for ChatGPT Pro users.
We are introducing Structured Outputs in the API—model outputs now reliably adhere to developer-supplied JSON Schemas.
Introducing ChatGPT agent: it thinks and acts, using tools to complete tasks like research, bookings, and slideshows—all with your guidance.
Introducing GPT-4.1 in the API—a new family of models with across-the-board improvements, including major gains in coding, instruction following, and long-context understanding. We’re also releasing our first nano model. Available to developers worldwide starting today.
GPT-5.2-Codex is OpenAI’s most advanced coding model, offering long-horizon reasoning, large-scale code transformations, and enhanced cybersecurity capabilities.
Our latest image generation model is now available in the API via ‘gpt-image-1’—enabling developers and businesses to build professional-grade, customizable visuals directly into their own tools and platforms.
Use Codex anywhere with the ChatGPT mobile app. Monitor, steer, and approve coding tasks in real time across devices and remote environments.
Introducing GPT-5.1-Codex-Max, a faster, more intelligent agentic coding model for Codex. The model is designed for long-running, project-scale work with enhanced reasoning and token efficiency.
Introducing GPT-5 in our API platform—offering high reasoning performance, new controls for devs, and best-in-class results on real coding tasks.
A technical deep dive into the Codex agent loop, explaining how Codex CLI orchestrates models, tools, prompts, and performance using the Responses API.
Extend creativity and tell a bigger story with DALL·E images of any size.
Using new techniques for scaling sparse autoencoders, we automatically identified 16 million patterns in GPT-4's computations.
We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
Codex just got faster, more reliable, and better at real-time collaboration and tackling tasks independently anywhere you develop—whether via the terminal, IDE, web, or even your phone.
Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases.
We’re announcing updates including more steerable API models, function calling capabilities, longer context, and lower prices.
We’ve created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today.
This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing our work, this post will tell you a bit more about generative models: what they are, why they are important, and where they…
SWE-bench Verified is increasingly contaminated and mismeasures frontier coding progress. Our analysis shows flawed tests and training leakage. We recommend SWE-bench Pro.
We’ve observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported. The…
By Ryan Lopopolo, Member of the Technical Staff
We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples. By establishing a correlation between sample quality and image classification accuracy, we show that our best generative…