In an era where speed and agility define the developer experience, the Codex CLI from OpenAI emerges as a landmark innovation. Building on the revolutionary capabilities of the Codex models, this lightweight tool brings powerful AI coding assistance directly into the terminal. It empowers developers to prototype, generate, and test ideas in seconds—without ever needing a full IDE setup.

The Codex CLI isn’t just a tool; it’s a statement about the future of developer productivity.

Why Codex CLI?

Why Codex, you might ask? The answer lies in its extraordinary ability to bridge human thought and code execution. Codex models are designed to comprehend natural language and translate it into functional code across dozens of programming languages.

The Codex CLI taps into this capability, offering developers a way to:

• Quickly generate and test ideas
  
• Explore AI coding capabilities without breaking focus
  
• Enhance learning, prototyping, and debugging workflows
  

In an industry where context switching is the silent productivity killer, Codex CLI feels almost revolutionary in its simplicity and speed.

System Requirements

Before using Codex CLI, ensure you have:

• Git Bash installed
• Node.js installed
• An OpenAI API Key or access to another supported provider (like Anthropic)
• Internet connection for API access

💡 Note: Codex CLI is compatible with Windows, macOS, and Linux.

Setting Up Codex CLI

Installing Codex CLI via Git Bash or Command Prompt

First, you’ll need to install the CLI globally. In your terminal:

Setting Up API Keys

I set up my API keys by creating a `.env` file in a dedicated folder on my desktop. To load the environment variables into the shell, I used the following command:

Codex CLI in Action: Demo

For demonstration, I used the model `gpt-4o-mini` with the following 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 demonstrate the flexibility of Codex CLI, I showcased how to switch models and providers directly from the terminal. After generating a prompt using gpt-4o-mini, I used the command:

• Navigating models: After typing `/model`, I navigated the list of available models using the Up and Down arrow keys, then pressed Enter to select.
• Switching providers: If I wanted to switch between providers (like OpenAI or Anthropic), I simply pressed Tab after the `/model` command.

I re-ran the same prompt using the gpt-4.1-mini-2025-04-14 model. The second model provided a much more detailed and sophisticated codebase, showcasing better planning, optimization, and modularity.

A Few Limitations to Keep in Mind

While Codex CLI delivers impressive performance, it’s important to recognize that no tool is without its boundaries. Complex domain-specific tasks, highly contextual business logic, or unclear prompts can sometimes cause the model to generate incomplete or inaccurate code. Moreover, while switching models is intuitive, knowing which model best suits a particular task still requires user familiarity and experimentation.

That said, these limitations are not weaknesses — they are simply reminders that Codex CLI is a powerful assistant, not a replacement for developer intuition, expertise, and judgment. Used thoughtfully, it elevates workflows and accelerates innovation.

Quick Comparison to Claude Code

During the exploration, I also reflected on how Codex CLI’s functionality compares with Claude Code, an AI model from Anthropic that also focuses on coding tasks. Both models are designed to generate code from natural language instructions and can be integrated into a terminal-based environment for easy, interactive use. However, there are some distinct differences:

• Claude Code is often noted for producing cleaner code with a focus on clarity and explainability, making it particularly good for educational purposes.
• Codex CLI, powered by OpenAI’s Codex models, tends to generate more optimized and performance-focused code, especially when dealing with complex coding tasks, as showcased with the AI cleaning robot prompt.

While both systems are capable of handling a wide range of coding requests, Codex CLI’s models like gpt-4o-mini and gpt-4.1-mini-2025-04-14 offer greater depth in terms of technical complexity and customizability, while Claude Code shines when clear, well-commented, and readable code is a priority.

Final Thoughts on Codex CLI

Once a programmer knows what they want to build, the act of coding becomes a twofold journey: first, breaking complex problems into smaller, manageable parts; second, finding and mapping those parts onto existing tools—libraries, APIs, functions—that already exist.

It’s this second step—the sometimes tedious hunt for resources—that has long been the biggest barrier to seamless development. Codex CLI changes that.

With OpenAI Codex integrated directly into the terminal, programmers can refactor, translate, explain, and even architect new projects at incredible speed. From speeding up mundane tasks to becoming an essential thought partner, Codex CLI represents a profound shift in how we approach coding itself.

Yet one thing feels certain: we are only scratching the surface of what tools like Codex CLI can accomplish. As these AI assistants grow even smarter, they may not just change how we code—they may redefine the very act 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