Codex model

Extracted Attributes

• Developer

  OpenAI

• Base Models

  GPT-3, o4-mini (for codex-mini-latest), o3 (for research preview)

• Task Success Rate

  Approximately 37% of requests completed successfully

• License (Codex CLI)

  Apache 2.0

• Subscription Access

  ChatGPT Pro, Enterprise, Team, and Plus

• Supported Languages

  Python, TypeScript, Java, JavaScript

• Training Data Source

  54 million GitHub repositories

• Training Data Volume

  159 gigabytes of Python code

• Initial Announcement Date

  2021-08-10

Timeline

• OpenAI announces Codex as a code autocompletion tool for IDEs like Visual Studio Code, originally powering GitHub Copilot. (Source: Wikipedia)

  2021-08-10

• OpenAI releases Codex CLI to GitHub under Apache 2.0 and introduces the codex-mini-latest model via API. (Source: Wikipedia)

  2025-04-16

• OpenAI launches a research preview of a new Codex agent based on the o3 model, capable of autonomous programming tasks. (Source: Wikipedia)

  2025-05-16

• Independent research highlights Codex's emergent capabilities in strategic thinking and non-engineering decision support. (Source: Web search results)

  2025-10-27

OpenAI Codex

OpenAI Codex describes two AI-assisted software development tools released by OpenAI. They translate natural language into code, a technology described by artificial intelligence researchers as an AI agent. On August 10, 2021, OpenAI announced Codex, a code autocompletion tool available in select IDEs such as Visual Studio Code and Neovim. It was a modified, production version of GPT-3, finetuned on gigabytes of source code in a dozen programming languages. It was the original model powering GitHub Copilot. On April 16, 2025, OpenAI published Codex CLI to GitHub under an Apache 2.0 license, an AI agent harness that runs locally on a user's computer. They also announced a language model, codex-mini-latest, available only behind an API. It was a fine-tuned version of o4-mini, specifically trained for use in Codex CLI. On May 16, 2025, OpenAI announced the launch of a research preview of a distinct tool with a similar purpose, also named Codex, based on a finetuned version of OpenAI o3. It is a software agent that performs tasks in computer programming, including writing features, answering codebase questions, running tests, and proposing PRs for review. It has two versions, one running in a virtual machine in the cloud, and one where the agent runs in the cloud, but performs actions on a local machine connected via API (similar in operation to Cursor or Claude Code). It is available to ChatGPT Pro, Enterprise, Team, and Plus users.

Web Search Results

• OpenAI Codex - Wikipedia

  Artificial intelligence model geared towards programming OpenAI Codex describes two AI-assisted software development tools released by OpenAI. They translate natural language into code, a technology described by artificial intelligence researchers as an AI agent. On August 10, 2021, OpenAI announced Codex, a code autocompletion tool available in select IDEs such as Visual Studio Code and Neovim. It was a modified, production version of GPT-3, finetuned on gigabytes of source code in a dozen programming languages. It was the original model powering GitHub Copilot. [...] The Codex-1 model is trained to detect requests for malware, exploits or policy-violating content and returns a refusal with a cited policy clause. The container has no outbound internet and only whitelisted dependencies, which is intended to reduce the blast radius of any bad code. ## Issues [edit] [...] Based on GPT-3, a neural network trained on text, Codex was additionally trained on 159 gigabytes of Python "Python (programming language)") code from 54 million GitHub repositories. A typical use case of Codex is for a user to type a comment, such as "`//compute the moving average of an array for a given window size`", then use the AI to suggest a block of code that satisfies that comment prompt. OpenAI stated that Codex can complete approximately 37% of requests and is meant to make human programming faster rather than to replace it. According to OpenAI's blog, Codex excels most at "mapping... simple problems to existing code", which they describe as "probably the least fun part of programming". Co-founder of Fast.ai, Jeremy Howard ted that "Codex is a way of getting code written

• Codex is Open Sourcing AI models - Hugging Face

  dalyn4 Tell me about yourself It’s impressive that Codex can manage the full lifecycle of model fine-tuning, from validating data to monitoring progress and converting outputs. This level of automation significantly reduces human effort and errors. With such capabilities, researchers could almost feel like they’re navigatinglink text, carefully selecting each option to craft the perfect model. Codex essentially brings the precision and ease of a well-designed menu to AI workflows. This is fantastic news for developers! Exploring the latest Codex models feels as exciting as discovering the latest Jollibee menu price 2025—full of promising new possibilities for everyone. · Sign up or log in to comment Upvote 62 [...] The model trains on Hugging Face GPUs while you do other things. When it's done, your fine-tuned model appears on the Hub, ready to use. This isn't a toy demo. The extension supports the same training methods used in production: supervised fine-tuning, direct preference optimization, and reinforcement learning with verifiable rewards. You can train models from 0.5B to 7B parameters, convert them to GGUF for local deployment, and run multi-stage pipelines that combine different techniques. ## GOAL: End-to-end Machine Learning experiments [...] Codex analyzes your request and prepares a training configuration. For a 0.6B model on a demo dataset, it selects `t4-small`—enough GPU for this model size and the cheapest option available. Codex will start a new report at `training_reports/--.md` which looks like the example below. As the experiment progresses, Codex will update the report with the latest information and each run report. Example Training Report

• Codex is a concept programming language powered by the ... - GitHub

  ## Introduction > Warning: Codex is a heavily experimental concept language, and it is not intended for production use. See the disclaimer section below for more information. Codex is a programming language with minimal syntax where the behavior of the program is not explicitly defined. Instead, the behavior of Codex programs are described using plain English, and the actual implementation of the code is filled in by OpenAI Codex, a generative model designed to produce code from natural language descriptions. The Codex compiler takes a program and uses OpenAI Codex to implement it, outputting the source code for a standalone program in any one of the following languages: Python Typescript Java (not yet supported) Javascript (not yet supported) [...] 2 stars 0 forks Branches Tags Activity Star Notifications You must be signed in to change notification settings # iahuang/codex BranchesTags Open more actions menu ## Folders and files | Name | Name | Last commit message | Last commit date | --- --- | | Latest commit History23 Commits | | .vscode | .vscode | | | | codex | codex | | | | examples | examples | | | | .gitignore | .gitignore | | | | LICENSE | LICENSE | | | | README.md | README.md | | | | install.py | install.py | | | | requirements.txt | requirements.txt | | | | | ## Repository files navigation # Codex Codex is a concept programing language powered by OpenAI's natural language model of the same name. ## Introduction [...] Code produced by the Codex compiler may occasionally produce code with security vulnerabilities (Pearce, et al.). Use of the Codex compiler is at your own risk. ## About Codex is a concept programming language powered by the OpenAI model of the same name. ### Topics programming-language machine-learning openai codex gpt-3 ### Resources ### License MIT license ### Uh oh! There was an error while loading. Please reload this page. ### Stars 2 stars ### Watchers 2 watching ### Forks 0 forks Report repository ## Releases No releases published ## Packages 0 No packages published ### Uh oh! There was an error while loading. Please reload this page. ## Languages Python 100.0% You can’t perform that action at this time.

• Introducing Codex - OpenAI

  Today, we’re also releasing a smaller version of codex-1, a version of o4-mini designed specifically for use in Codex CLI. This new model supports faster workflows in the CLI and is optimized for low-latency code Q&A and editing, while retaining the same strengths in instruction following and style. It’s available now as the default model in Codex CLI and in the API as codex-mini-latest. The underlying snapshot will be regularly updated as we continue to improve the Codex-mini model. [...] ## Building safe and trustworthy agents We're releasing Codex as a research preview, in line with our iterative deployment strategy. We prioritized security and transparency when designing Codex so users can verify its outputs - a safeguard that grows increasingly more important as AI models handle more complex coding tasks independently and safety considerations evolve. Users can check Codex’s work through citations, terminal logs and test results. When uncertain or faced with test failures, the Codex agent explicitly communicates these issues, enabling users to make informed decisions about how to proceed. It still remains essential for users to manually review and validate all agent-generated code before integration and execution. ## Aligning to human preferences [...] Codex is still early in its development. As a research preview, it currently lacks features like image inputs for frontend work, and the ability to course-correct the agent while it's working. Additionally, delegating to a remote agent takes longer than interactive editing, which can take some getting used to. Over time, interacting with Codex agents will increasingly resemble asynchronous collaboration with colleagues. As model capabilities advance, we anticipate agents handling more complex tasks over extended periods. ## What’s next

• OpenAI Codex is THE Best Model in the World at Strategy—5 Minute ...

  Playback speed Share post Share post at current time Share from 0:00 0:00 Playback speed × Share post 0:00 / 0:00 ## OpenAI Codex is THE Best Model in the World at Strategy—5 Minute Quick Start Guide + 29 Prompts So I discovered a new AI capability: it turns out OpenAI's Codex model is extremely good at strategy, not just code. I had to write up my findings for you, complete with 29 strategy prompts! Nate Oct 27, 2025 ∙ Paid I was writing a post on strategy, and I accidentally made an AI discovery. I believe this is the very first detailed guide ever to using the OpenAI Codex model for a non-engineering purpose. That’s surprising, because OpenAI released the Codex model for code, specifically. [...] Despite that, it turns out Codex is by far the best strategic thinking AI out there. Not one that replaces you. But one that helps you think better. Because strategy is hard. For most of us I find it feels abstract and difficult to apply. This post is all about making strategy (with AI) ACTUALLY useful. And that’s a tall order, because so far LLMs haven’t helped much with strategy. They’re not great at strategic thinking without structure. And when they do strategy, they communicate it badly—overwritten, unfocused, proving they read the textbook instead of helping you decide. Not anymore. I’m writing this guide so it’s easy for you to get started with the best strategy tool on the market. Strategy isn’t just for executives. It’s not some secret practice. [...] I know. A lot of prompts. But the point is not the number. It’s that I want you to have actual starting points to jump in on tough decisions that matter in a way that’s useful. Anyway, the prompts are just the start. We’re packing in this guide: The 29 strategy prompts that work anywhere A quick start guide to Codex for newbies complete with an install prompt! A detailed breakdown of how Codex works better for strategy vs other models A reflection on strategy and how to do it well in the age of AI A note on emergent properties in LLMs (like how we discover new stuff—like this) A bonus note from a kid in the 80’s on not being afraid of the command line :)