OpenAI recently released the highly anticipated Codex programming intelligence, a powerful tool integrated with ChatGPT that has officially entered the research preview phase. As a cloud-based software engineering assistance system, Codex is expected to revolutionize the way developers work, improve programming efficiency, and simplify the processing of complex tasks. In this article, we will comprehensively analyze the features, working principle and practical application cases of this revolutionary technology product.
Official website entrance:https://openai.com/index/openai-codex/
Codex Intelligentsia: The Beginning of a New Era of Programming
OpenAI launched the Codex Programming Intelligence in May 2025, following the addition of the ability to connect to GitHub repositories in ChatGPT. This is a cloud-based software engineering intelligence capable of performing a variety of programming tasks, including:
- Writing new functional modules
- Fix code bugs and vulnerabilities
- Running Test Validation
- Submitting Code Changes
- Manage and execute multiple coding tasks simultaneously
Unlike traditional programming assistants, Codex is based on the codex-1 model (which is a specialized version of the OpenAI o3 model) optimized specifically for software engineering, and is trained through reinforcement learning in a real programming environment so that the code it generates reflects human coding styles, strictly follows instructions, and can be tested over and over again until it achieves the desired results.
How Codex works and its core features
workflow
Codex's workflow is designed to be simple and intuitive:
- User access to Codex via ChatGPT sidebar
- Enter your requirements and click the "Code" button to assign a task, or click the "Q&A" button to ask a code-related question.
- Codex performs tasks in a secure, isolated cloud environment that is pre-loaded with the user's code base
- Users can track task progress in real time
- Upon task completion, Codex commits the changes and provides detailed evidence of execution, including terminal logs and test outputs
- Users can review the results, request further modifications, or integrate changes into the workflow
Key technical features
| characterization | descriptive |
|---|---|
| multitasking | Ability to handle multiple independent programming tasks simultaneously |
| Run in the cloud | Tasks are executed in securely isolated cloud containers without tying up local resources |
| Codebase Integration | Supports seamless integration with GitHub repositories, enabling direct reading and manipulation of user code. |
| Intelligent Code Understanding | Ability to understand complex code structures, identify potential problems and provide solutions |
| complete chain of custody | Provide verifiable evidence of task execution through terminal logs, test output, etc. |
| Environment Configuration | Support for customized configurations to match the environment to the actual development settings |
| Safety and security | Disable Internet access during execution and interact only with explicitly authorized code and dependencies |
It's worth noting that Codex supports following the codebase's AGENTS.md Documentation guides, similar to a human developer reading a README file to understand a project's specifications. Codex performs best when properly configured, reliably tested, and clearly documented.
Practical Case: Codex Programming Competency Demonstration
Below are examples of Codex's use in real open source projects, demonstrating its ability to handle a variety of programming tasks:
Case 1: Fixing a Nested CompoundModels Calculation Problem in the astropy Library
In this case, Codex needed to solve the problem that the separability_matrix of the Modeling module in the astropy/astropy repository was not calculating the separability of nested CompoundModels correctly.
The Codex generated a very concise and precise code modification, with only the necessary changes to the core of the problem. In contrast, the modification scheme for the o3 model was more lengthy and even added some unnecessary comments.
Case 2: Fixing matplotlib window calibration errors
This task requires that the matplotlib library be fixed. mlab._spectral_helper The window correction is not correct.
Codex also demonstrates the ability to make precise and concise fixes, modifying only the necessary lines of code to maintain clarity and maintainability.
Case 3: Solving the duration expression problem in django
In this case, it was necessary to fix an issue in the django framework where expressions containing only duration did not work properly on SQLite and MySQL.
Codex not only provides a clean fix, but also fills in the missing dependency calls first, demonstrating its ability to fully understand the context of the code.
Case 4: Fix expensify member chat room name update issue
This case involves a bug in expensify (a financial collaboration software centered around chat): after deleting the cache, member chat room names were not updated in LHN.
Codex pinpointed the problem and provided a precise and effective fix, while the o3 model made some ineffective code changes.
Performance evaluation and comparative analysis
Benchmark test scores
In the SWE-Bench Verified benchmark, Codex (codex-1) achieved impressive results:
| mould | SWE-bench score |
|---|---|
| Codex (codex-1) | 72.1% |
| Claude 3.7 | 62.3% |
| o3-high | 71.7% |
Tests were conducted with a context length of up to 192,000 tokens, using a medium "reasoning effort" setting, the same as is currently available in Codex product releases.
Comparison of code generation with o3 model
Real-world examples demonstrate that codex-1 consistently generates cleaner, clearer code change patches than OpenAI o3, which can be immediately reviewed manually and integrated into standard workflows. In multiple open source library tests, codex demonstrated higher accuracy and better code quality.
Feedback on actual use
The internal OpenAI team has adopted Codex as part of its daily development tools, primarily for performing repetitive and well-scoped tasks such as code refactoring, renaming, and writing tests that typically interrupt a developer's stream of concentration.
In addition, early testing with multiple external partners, including Cisco, Temporal, Superhuman, and Kodiak, has shown that Codex significantly accelerates tasks such as feature development, issue debugging, test writing and execution, and improves team efficiency.
Availability, Pricing and Future Outlook
Current Availability
Codex is open to the following users:
- ChatGPT Pro users ($200 per month)
- ChatGPT Enterprise users
- ChatGPT Team users
ChatGPT Plus and Edu users will soon be able to use this feature as well.
pricing strategy
Currently, OpenAI offers a free trial period where users can try out the Codex functionality without restrictions for the next few weeks. After that, speed limits and flexible pay-as-you-go options will be introduced.
For developers, the codex-mini-latest model is available on the Responses API for:
- Token per million inputs: $1.50
- Token per million output: $6.00
- Enjoy a discount on the 75%'s alert cache
The way forward
OpenAI plans to further enhance the interactivity and flexibility of Codex:
- Support in providing guidance and feedback during mandate implementation
- Collaborating with AI to implement programming strategies
- Receive proactive progress update notifications
- Deep integration with popular development tools (e.g. GitHub, command line, issue trackers, CI systems)
The launch of Codex Intelligence marks a new stage in AI-assisted programming. It's not meant to replace engineers, but to act as a reliable assistant for tedious and repetitive tasks, allowing developers to focus on more creative and strategic work. Although it is still in the research preview stage and has some limitations (e.g., lack of Internet access, long task response times, etc.), Codex has shown great potential to reshape the underlying logic of software development and become an important part of the programming paradigm of the future.
