OpenAI Codex: A Formidable Rival for Claude Code

Applications of LLM

How to Maximize OpenAI Codex

OpenAI's coding agent Codex has recently shown notable improvements, often surpassing Claude Code in terms of speed and accuracy for many tasks. According to the author, Codex is not only effective for various tasks but also excels at executing the requested tasks precisely without making unsolicited changes.

The author shares their experience with Codex for advanced coding tasks and other application areas, detailing some techniques used to enhance its performance.

Why Use OpenAI Codex?

It is crucial to note that the subscription price for Codex 20x Max is the same as that of Claude Code. The difference mainly lies in the quality of the results produced by the model and its efficiency in completing tasks.

The author, who programs daily, emphasizes the importance of staying updated with the latest coding models. They regularly test new models, such as GPT-5.5, to evaluate their performance against their current setup.

For about two weeks, the author has been using Codex with GPT-5.5 for concrete tasks. They believe that testing coding models on fictitious tasks does not truly reflect their capabilities. When they used Codex for more complex tasks, they were impressed by its efficiency and speed. Codex demonstrated a better ability to execute exactly the requested tasks without altering other parts of the code, unlike Claude Code, which sometimes modified unwanted sections.

However, this represents a delicate balance. On one hand, Claude Code offers greater freedom for the model to decide on modifications, which can lead to undesirable changes. On the other hand, Codex focuses solely on what is requested, which can sometimes lead to bugs if certain parts of the code are not updated.

Specific Techniques to Optimize Codex

The author discusses specific techniques to improve Codex's performance beyond its default capabilities. They use the fast mode on Codex as it often does not hit its limits. However, if limits are encountered, it is advisable to disable fast mode or open another Codex account.

They also utilize a very high level of reflection in plan mode and a high level of reflection in normal mode while using GPT-5.5. The author has given Codex access to Playwright MCP, allowing it to interact with their browser. This proves very effective, for example, for OpenClaw bots and for testing features implemented by Codex. Allowing your coding agents to test their own work significantly enhances their performance.

The author also uses the YOLO mode with Codex, enabling it to perform any action in the folder they are working on. From their experience, advanced coding models like Claude Code and Codex do not make serious errors, such as deleting production databases, and they generally warn you before taking irreversible actions.

They believe that if you properly configure your codebase and infrastructure, this should not pose a problem. An agent or yourself should not have access to permanently delete databases.

Another use case for Codex is for their OpenClaw bots. One of the significant advantages of Codex over Claude Code is that you can feed your OpenClaw bots with your Codex subscription, which is no longer allowed with Claude Code. Codex is a leading intelligent model that you can use for your OpenClaw bots at a reasonable rate.

They also use fast mode for their OpenClaw bots, as they have a sufficient budget. However, you can disable it if necessary, depending on the use case.

Unfortunately, Codex has not yet implemented a simple work tree setup, as Claude Code has done. This represents a drawback for the author, as work trees are an essential feature when working on multiple items in the same repository.

To work around this issue, they created an alias to generate their own work tree by asking Codex to set it up for them. This was very simple to implement.

codex-wt <work-tree-name>

Codex vs Claude Code

In this final section, the author compares Codex and Claude Code. In their opinion, there is no clear winner between the two models. Both are extremely powerful, and they can accomplish their most complex tasks with either. However, they have certain preferences in specific scenarios.

For very precise tasks or for searching for specific bugs, Codex is more efficient. In many cases, Claude Code can also accomplish the same task but generally takes longer.

Additionally, as mentioned in the OpenClaw section, Codex allows the use of the subscription for OpenClaw bots, which Claude Code does not permit. If you rely on running many OpenClaw bots, the author strongly recommends using Codex.

Conversely, Claude Code is also very powerful and can accomplish all their complex tasks while offering features that the author appreciates, such as the work tree and agent view. In their opinion, Claude Code's feature set is richer than that of Codex, which could justify a preference for Claude Code.

In summary, the author believes that both models are very close in terms of power. It will be necessary to follow their evolution and continue testing them to see which one will emerge victorious in a few months. For now, they consider both to be excellent choices, and the best one for you will depend on your situation and preferences.

In this article, the author discussed how to optimize the use of OpenAI Codex, highlighting the importance of staying updated with coding models and comparing it to Claude Code. They had a very positive first impression, noting that the model could execute even their most complex tasks. They also shared specific techniques to improve Codex, such as:

• Allowing Codex to validate its own work
• Setting up an alias for work trees
• Using it for their OpenClaw bots

Finally, they compared Codex to Claude Code, emphasizing that the competition is very close and that the choice of the best model depends on your preferences. They recommend exploring both models to see what works best for you and closely following their evolution, as new features and more powerful LLM models will soon be available.