Codex vs. Cursor: The agent or the co-pilot?

According to the latest Stack Overflow survey, 84% of developers already use or plan to use AI tools in their development process.

This blog is for developers, tech leads, and engineering teams evaluating how to integrate AI into their development process. Whether you want to fully delegate coding tasks or pair program with an intelligent partner, this hands-on review will help you choose between Codex and Cursor.

Codex vs Cursor#

The debate is no longer if you should use AI, but how. Do you need an autonomous coding agent that works for you, or a deeply integrated co-pilot that works with you?

That’s the central question in the Codex vs. Cursor showdown. One promises to be a lightning-fast junior developer you can delegate to; the other, a cognitive partner embedded inside your editor. This review cuts through the hype with side-by-side testing to help you find the right tool to dominate your workflow.

A year ago, this was a clear choice between two distinct philosophies that OpenAI's Codex and the AI-native IDE, Cursor, embodied. But the lines are blurring. Let’s dive into the core philosophies and the recent convergence that changed the decision.

Codex’s architecture#

Codex offers a fully autonomous workflow if you’re tired of micromanaging AI outputs or manually stitching together code snippets. It lets you assign tasks like refactoring a component or adding a new feature, and handles the entire process: plan, code, test, and create a PR. The latest version of OpenAI Codex is now accessible to paid ChatGPT users.

The best way to think of the new Codex is as a brilliant, lightning-fast junior developer you can hire for about $20 a month. This developer operates with a high degree of autonomy, functioning on a principle of trust rather than requiring micromanagement. You write a concise project brief, give them access to the necessary resources, and let them get to work.

How it works#

The process is fundamentally asynchronous and “out of the loop.”

1. Brief the agent: Inside ChatGPT, you issue a high-level instruction and point it to a GitHub repo.

2. Cloud sandbox: Codex clones the repo into a secure environment with access to a file system, terminal, and interpreter, keeping your secrets safe.

3. Autonomous execution: Codex analyzes the code, forms a plan, writes code, runs tests, debugs, and commits the changes.

4. Pull request delivery: When finished, it creates a new branch and submits a clean PR with a summary of changes.

You review the PR like a senior dev. Codex is your remote junior dev that is fast, precise, and independent.

Codex is the embodiment of delegation in a secure, isolated environment. It’s designed to take entire, well-defined tasks off your plate so you can focus on higher-level architectural and product decisions.

For local, command-line-based tasks, OpenAI also offers the codex-cli, a powerful tool for developers who want agent-like capabilities directly in their terminal.

Cursor’s philosophy#

Suppose you’re looking for a coding partner who thinks with you while you work, rather than waiting for instructions. Cursor integrates into your editor, offering real-time suggestions, deep codebase understanding, and powerful in-place refactors. Instead of creating an agent that works for you in a separate environment, Cursor has rebuilt the environment to work with you.

In contrast to Codex’s role as a delegated assistant, Cursor can be considered a deeply integrated cognitive partner. It acts as a constant, intelligent presence within your development environment, always aware of context and ready to watch, predict, and enhance everything you do as a developer.

How it works#

Cursor began as a fork of VS Code, which is a stroke of genius. This means that for many developers, the environment is instantly familiar. Your keyboard shortcuts, themes, and extensions work out of the box. On top of that familiar foundation, Cursor has layered a profound level of AI integration.

1. Codebase-wide context: When you open a project, Cursor indexes your entire codebase. It builds a semantic understanding of every function, class, and component.

2. Conversational collaboration: Using the chat panel, you can ask it simple questions or give it complex commands. The magic happens when you use @ symbols to reference specific files (@components/Button.tsx) or the entire project (@Codebase).

3. Real-time refactoring: The interaction is iterative and “in-the-loop.” You highlight a messy block of code and ask Cursor to refactor it. Suggestions are surfaced as inline diffs, that you can review, modify, and apply in seconds.

4. Mixture of experts: Cursor doesn’t lock you into a single AI model. You can configure it to use OpenAI's fastest models (like GPT-4o) for quick chats, their most powerful models for complex code generation, and even Anthropic's Claude 4 Opus for tasks that require more creativity or prose, like writing documentation.

Cursor is the pinnacle of integration. Its goal is to work alongside you, enhancing your abilities, creating a tight, collaborative feedback loop that makes you a faster, smarter, and more efficient developer.

So, we had two choices: the out-of-loop agent (Codex) and the in-loop co-pilot (Cursor). While Cursor originally focused on in-editor collaboration, it recently introduced a standalone web agent, mirroring Codex’s delegated workflow. This blurs the line between agent and co-pilot, and raises the question: what happens when both platforms offer both styles?

Cursor’s new agent: A direct Codex competitor#

Just as the market seemed to have two clear paths, Cursor introduced its powerful Cursor Agent on the web. Unlike the agent that works inside the IDE, this is a standalone, cloud-based service that mirrors the Codex workflow almost exactly.

The new workflow: You go to a web page, give the agent access to a GitHub repository, and write a prompt. The Cursor agent works in the cloud to produce a pull request, just like Codex.

The strategic implication: Cursor is more than just an IDE. It’s now a comprehensive AI development platform offering a best-in-class integrated co-pilot and a cloud-based autonomous agent. They are now competing with OpenAI on every front.

Codex Agent vs Cursor Agent on Web: A Detailed Comparison#

With both platforms now offering a web-based agent, how do they stack up?

The core difference comes down to specialization vs. flexibility. OpenAI bets its purpose-built codex-1 model outperforms general models on coding tasks. Cursor bets that offering a choice of the latest, most powerful generalist models provides a better overall result.

Want to explore real-world workflows using Cursor more deeply? Check out our hands-on Cursor AI course, which covers setup, usage, and building a full project in an AI-native IDE.

Hands-on test: A Three.js Snake game#

Theory and feature lists are one thing; real-world performance is another. To test both web agents, I pointed them to a public GitHub repository containing a simple Snake game built with Three.js. Each was assigned identical tasks. You can view the code in the starting GitHub repository in the widget below.

To begin, we must connect both agents to the GitHub repository. The setup required us to authorize the respective agents to access and make changes to our repositories. Once that was done, we could choose our repository from the main page.

How well do these agents handle basic CSS/HTML styling?#

The high score text appears on the right and the current score on the left. Let’s ask both agents to do the following:

I want the score and high score text to be a more retro font, bigger and both on the left side of the screen.

This is a simple CSS and HTML styling task.

Codex agent: It correctly identified the index.html file and the score’s relevant <style> elements. It added inline styles to increase the font size and change the positioning. It also added a link to load a retro font from Google Fonts in the header page. Before proposing the changes, it ran the tests using npm test, ensuring they were passing. Finally, it also produced a working PR that accomplished the task. Codex completed the task in 2 minutes and 8 seconds, generating a PR with 11 lines of code changed in 1 file.

Cursor agent: Using the auto mode for the model, it also identified the correct elements. It successfully changed the font size and positioning. For the font, it chose “Press Start 2P” from Google Fonts and correctly added the link to import it. Moreover, it added additional styling to enhance the retro arcade aesthetic and readability. It accomplished 100% of the task in a single, clean PR in under a minute. It changed 62 lines of code across 2 files.

Task benchmark#

While both agents succeeded, I noticed Codex was more conservative with its changes, sticking closely to the prompt. Cursor tended to be more creative, adding extra styling. Depending on how much creative freedom you want to grant the agent, this can be a pro or a con.

Here’s a summary of how they performed:

Can these agents adapt game logic in real time?#

If you play the game long enough, you will notice that a blue sphere for a power-up pops up from time to time. Let’s make the following request:

When the power-up is active, the snake color should change to yellow. After the power-up ends, it should go back to its original color.

This requires understanding the game’s JavaScript logic, identifying the state for an active power-up, and manipulating the Three.js material.

Codex agent: The agent correctly located the graphics.js file and the renderSnake() function. It found the section that sets the color of the snake segments. It then correctly modified the snake’s material color to yellow within an if block and, crucially, added an else block to return the color to its original state when the power-up is no longer active. The logic was sound, and the implementation was good. However, it changed the entire snake to yellow, not accounting for the different color of the head.

Cursor agent: The agent also found the correct logic in the renderSnake() function. It modified the function to dynamically change snake's colors based on gameState.powerUpActive. If gameState.powerUpActive is true, the snake's head becomes 0xffff00 (yellow) and the body 0xffdd00 (darker yellow). Otherwise, the original 0x00ff00 (green) head and 0x44aa88 (teal) body colors are used.
The logic and the implementation were sound. Once again, Cursor accomplished the complete task under a minute.

Task benchmark#

Cursor nailed the logic perfectly, including reverting the colors once the power-up ends. I gave it the win since it also accounted for the snake’s head’s different color. This highlights the strength of its specialized training on common programming patterns.

Here’s a summary of how they performed:

Which agent fits your workflow?#

Choosing a platform now means looking beyond individual workflows and considering how the broader ecosystem serves your needs. The hands-on test reveals that both agents are powerful but have different strengths.

Who is OpenAI Codex for?#

• Security through isolation is your top priority: For enterprises in regulated industries, Codex’s ephemeral, sandboxed architecture (meaning it creates a temporary, isolated digital workspace for each task and deletes it afterward) is a compelling, best-in-class security feature.

• You trust in specialization: You believe a model fine-tuned exclusively for professional software engineering will consistently produce higher-quality, more idiomatic code than a general-purpose model.

• You are already embedded in the ChatGPT ecosystem: If your team already uses ChatGPT for other tasks, using the integrated Codex agent is a seamless extension.

Who is the Cursor ecosystem for?#

• You want it all in one place: A platform that provides a world-class AI-native IDE for interactive work and a powerful web agent for delegation, without compromise.

• You value flexibility and cutting-edge models: You want the freedom to choose the best AI model for any given task, be it from OpenAI, Anthropic, or Google, and you want to always have access to the latest and greatest.

• You believe context is king: An agent built on a foundation of deep, persistent codebase understanding (Cursor’s core strength) will ultimately outperform an agent working from a temporary clone of the repository.

Ready to take your Cursor skills to the next level? The “Advanced Cursor AI” course walks you through prompt engineering, smart Composer workflows, CI/CD integration, automated testing, and multi-file refactoring, all within Cursor’s context-aware interface. Learn more and start building!

The future race#

The launch of Cursor’s web agent has transformed the market. The philosophical debate is over, and a direct, feature-for-feature competition has begun. The question for developers is no longer “which path do I take?” but “which platform offers the best-integrated suite of tools for how I want to work?” The innovation spurred by this head-to-head race will undoubtedly benefit every developer, regardless of which ecosystem they choose.