Exploring the OpenAI Codex CLI: A Hands-On Guide
This article was authored by Gen Benedict Casio. Ben is a Computer Science student and aspiring MLOps Engineer at Philippine Christian University - Dasmariñas. As the Captain of AWS Cloud Club - PCU Cavite, he leads cloud education through workshops and collaborations. He’s passionate about Cloud, AI, ML, and DevOps, and certified in AWS and DataCamp.
In a time when speed and flexibility shape the developer experience, OpenAI's Codex CLI stands out as a groundbreaking innovation. Leveraging the advanced capabilities of Codex models, this lightweight tool offers powerful AI-driven coding support right in the terminal. It enables developers to quickly prototype, generate, and test ideas without the need for a full IDE.
The Codex CLI is more than just a tool; it represents the future of developer productivity.
Why Codex CLI?
You may wonder, why Codex? The answer is its remarkable ability to connect human intent with code execution. Codex models are built to understand natural language and convert it into functional code in various programming languages.
The Codex CLI leverages this ability, providing developers with a way to:
In an industry where frequent context switching hampers productivity, the Codex CLI stands out for its simplicity and speed, making it feel almost revolutionary.
System Requirements
Make sure you have the following before using Codex CLI:
💡 Reminder: Codex CLI works with Windows, macOS, and Linux.
Setting Up Codex CLI
Installing Codex CLI via Git Bash or Command Prompt
To begin, you'll need to install the CLI globally. Use the following command in your terminal:
Setting Up API Keys
I configured my API keys by creating a .env file in a specific folder on my desktop. To load the environment variables into the shell, I ran the following command:
Codex CLI in Action: Demo
For the demonstration, I utilized the model gpt-4o-mini with the prompt: “Create an algorithm for an AI cleaning robot that plans the optimal path to clean all dirty spots in a 3D space.”
Switching Between Models
To highlight the versatility of Codex CLI, I demonstrated how to switch models and providers directly from the terminal. After generating a prompt with gpt-4o-mini, I executed the following command:
I re-executed the same prompt using the gpt-4.1-mini-2025-04-14 model. The second model generated a far more detailed and advanced codebase, demonstrating improved planning, optimization, and modularity.
A Few Limitations to Keep in Mind
Although Codex CLI offers remarkable performance, it's crucial to acknowledge that no tool is without limitations. Complex domain-specific tasks, highly contextual business logic, or vague prompts can occasionally lead the model to produce incomplete or incorrect code. Additionally, while switching models is straightforward, understanding which model is best for a specific task still requires user experience and trial and error.
These limitations are not shortcomings — they serve as reminders that Codex CLI is a powerful tool, but not a substitute for a developer's intuition, expertise, and judgment. When used wisely, it enhances workflows and speeds up innovation.
Quick Comparison to Claude Code
In my exploration, I also considered how Codex CLI's capabilities stack up against Claude Code, an AI model from Anthropic focused on coding tasks. Both models are built to generate code from natural language commands and can be used interactively within a terminal. However, there are notable differences between them:
Both systems can handle a variety of coding tasks, but Codex CLI’s models, such as gpt-4o-mini and gpt-4.1-mini-2025-04-14, provide more technical depth and customizability. In contrast, Claude Code excels when the focus is on generating clear, well-commented, and readable code.
Final Thoughts on Codex CLI
Once a programmer determines what they want to create, coding becomes a two-step process: first, breaking down complex problems into smaller, more manageable components; and second, identifying and matching those components to existing tools, such as libraries, APIs, and functions.
The second step—the often tedious search for resources—has traditionally been the main obstacle to smooth development. Codex CLI eliminates this challenge.
With OpenAI Codex integrated directly into the terminal, developers can refactor, translate, explain, and even design new projects at remarkable speed. By accelerating routine tasks and acting as a valuable collaborator, Codex CLI marks a significant transformation in how we approach coding.
One thing seems clear: we are just beginning to explore the potential of tools like Codex CLI. As these AI assistants continue to advance, they might not only transform how we code—they could redefine the entire process of creation.
References:
Stack Overflow for Teams. (2024). Better Together: Getting the Most Value from AI Code Generation Tools. Retrieved from: https://stackoverflow.co/teams/resources/better-together-getting-the-most-value-from-ai-code-generation-tools/
Anthropic Documentation. (2024). Claude Code Overview – Agents and Tools. Retrieved from: https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview
OpenAI GitHub. (2024). Codex Repository. Retrieved from: https://github.com/openai/codex
OpenAI Platform. (2024). Model Documentation. Retrieved from: https://platform.openai.com/docs/models
* This newsletter was sourced from this Tutorials Dojo article.
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