Codex vs. Cursor vs. Gemini Code Assist

What if your coding assistant could do more than just autocomplete? Imagine a tool that understands your entire project, helps review your pull requests, and even refactors code across multiple files on its own.

Today, this level of AI-driven software development is no longer a futuristic concept, but a daily reality. We’ve moved far beyond basic syntax highlighting because today’s AI-powered tools act as collaborative partners.

In this blog, we will dissect three of the most prominent AI code assistants shaping this revolution: OpenAI Codex, Cursor, and Google Gemini Code Assist.

By the end, you’ll have a clear framework for this decision, whether you’re a software engineer optimizing a personal workflow, a tech lead choosing a tool for your team, or a manager vetting a solution for the enterprise.

The most effective way to understand these tools is to examine them individually to appreciate their unique philosophies before comparing them. Let’s begin.

What is OpenAI Codex?#

OpenAI Codex has come a long way since it first appeared in 2021. Back then, it was an API-first model built on top of GPT-3 and fine-tuned on publicly available code. Its main job was to turn our natural language instructions into working code. That early version powered the first GitHub Copilot and quickly proved that it could handle a number of features, as mentioned below.

• Code generation: Turn prompts into complete functions, scripts, or even full files.

• Code completion: Finish lines, functions, or boilerplate we’d started.

• Language translation: Convert code between programming languages with impressive accuracy.

This API-only setup offered us a ton of flexibility. We could plug Codex into custom tools, automate workflows, and connect it to any environment we wanted. The trade-off was that we had to handle all the integration ourselves, and it didn’t give us a built-in IDE experience.

The Codex web agent (in ChatGPT)#

Fast forward to May 2025, and Codex stepped into a new role as a fully-fledged software engineering web agent inside ChatGPT. Now powered by codex-1, a next-generation coding model based on OpenAI’s o3 reasoning architecture and fine-tuned on real-world pull requests, it works like a virtual teammate. It can:

• Connect to our GitHub repository and work inside a secure, sandboxed cloud environment.

• Automate jobs like documenting architecture, refactoring modules, or fixing urgent bugs.

• Draft pull requests, propose changes, and run linting/tests before we merge anything.

• Suggest multiple possible solutions so we can pick what fits best.

• Keep a detailed, verifiable log of every action for transparency and security.

We can also tweak its environment with domain allowlists, and choose whether it has internet access (off by default for safety purposes).

Codex CLI: The local, open-source option#

In April 2025, OpenAI gave us another option, the Codex CLI. This is an open-source, terminal-based version we can run entirely on our own machines. By default, it uses OpenAI’s newer o4-mini model, which is optimized for efficiency and performance in terminal-based workflows. Unlike the hosted ChatGPT agent, the CLI:

• Runs locally, giving us full control over code execution.

• Accepts multimodal inputs, allowing us to pass not just text but also screenshots or diagrams directly in the terminal to guide the AI.

• Lets us run natural language commands right in the terminal (for example: codex "add logging to all API calls").

• Fits easily into CI/CD pipelines or scripts so we can automate coding tasks without leaving the shell.

It doesn’t have the same deep, multi-file reasoning (the AI’s ability to understand connections across multiple files in a project) or cloud sandboxing as the hosted agent. However, it’s lightweight, highly scriptable, and perfect for local-first workflows or situations where we can’t use cloud tools for compliance reasons.

Codex now offers multiple ways of working, each with its own strengths and trade-offs. Let’s start by looking at what it does especially well.

Strengths of Codex#

The following are the capabilities that make Codex a powerful part of our development workflow:

• Codex gives us flexible options, including the API for maximum customization, the hosted agent for full-service automation, and the CLI for local control.

• It integrates deeply with GitHub, automating tasks, running tests, and providing multiple solution suggestions.

• It operates in a secure setup with all actions logged clearly for transparency.

Limitations of Codex#

There are also some important constraints we need to keep in mind:

• The Codex web agent is currently accessible only to paid ChatGPT users, including those on Pro, Team, Enterprise, Plus, and Edu plans (with rollout underway).

• The original API is still available, but it no longer represents the most advanced Codex capabilities.

• The CLI works well for local workflows, but it lacks the depth of the web agent’s reasoning in multi-file situations.

Access to Codex capabilities is tied to a ChatGPT subscription or API usage.

While the Codex ecosystem provides incredible flexibility through its various interfaces, Cursor assumes a different approach by integrating AI directly into a dedicated IDE.

What is Cursor?#

Cursor takes a fundamentally different approach from Codex. Rather than being a standalone model or an API-first service, Cursor is a full-featured AI-specific IDE built as a fork of Visual Studio Code. It reimagines the editor itself to place AI at the center of every coding workflow, reducing friction between “writing code” and “talking to your AI assistant.”

This means you’re not just adding AI into your workflow, you’re working inside an environment built around AI from the ground up. The result is a smoother transition between natural language instructions, code generation, and direct edits, all without leaving your editor.

Deeply integrated AI features#

Cursor’s key strength is contextual awareness across your entire project. This allows it to go beyond single-file prompts and perform complex, multi-file reasoning. Its core capabilities are outlined below.

• Chat with codebase awareness: Cursor has a built-in chat panel that understands your entire repository. You can ask it to explain complex logic, locate bugs across multiple files, or recommend architectural improvements, all with full knowledge of your project’s structure and dependencies.

• Inline edit commands: Cursor will rewrite a selected code block in place when you type a natural language instruction (e.g., “Make this more efficient and add error handling”). This works for refactoring, adding features, or even large-scale changes across your codebase.

• Generate new code: Cursor can scaffold new functions, components, or entire modules from scratch. Its multi-file reasoning enables it to automatically create supporting code in other files, if needed.

• Project-wide refactoring: Cursor can safely apply consistent changes across the entire project, because it’s an IDE, not just a model endpoint. This is something that’s difficult for stateless AI coding assistants.

Like any tool, Cursor comes with strong advantages alongside trade-offs you’ll need to weigh depending on your workflow.

Strengths of Cursor#

Cursor’s advantages make it particularly appealing for developers who work with large, interconnected projects:

• Tight project integration enables more accurate edits and better long-term code consistency.

• Familiar interface for VS Code users, with full theme, keybinding, and extension compatibility.

• Ideal for large-scale refactors and multi-file implementations, thanks to persistent project context.

Limitations of Cursor#

Despite its strengths, there are a few considerations that may make Cursor less suitable for certain users:

• Requires switching to a new IDE (even if VS Code-like), which may disrupt highly customized setups.

• Some of the most advanced features, like deep multi-file edits and extended context length, are locked behind a subscription.

• Cursor is a proprietary tool from a venture-backed startup, Anysphere Inc. While it has secured significant funding, its long-term availability and business model could change. Users should be aware of potential bugs and network issues that can occasionally disrupt workflows.

• While Cursor is SOC 2 certified and offers a “Privacy Mode” that prevents code from being stored or used for training, its default configuration may involve data indexing to improve performance. Teams handling highly sensitive intellectual property should carefully review the privacy settings, and understand how their data is processed.

• Not as portable for quick, one-off coding sessions compared to CLI-based or web-based tools.

What is Google's Gemini Code Assist?#

Google's Gemini Code Assist is Google’s answer to the needs of professional, large-scale software development teams. Built on the powerful Gemini family of models (such as Gemini Pro), this tool is not a general-purpose chatbot. It is a specialized, enterprise-grade coding assistant designed to integrate directly into a company’s development life cycle.

By focusing on enterprise requirements like compliance, security, and deep codebase integration, Gemini Code Assist aims to be more than a productivity booster. It positions itself as a trusted engineering partner for organizations operating at scale.

Primary use cases and capabilities#

Gemini Code Assist is engineered to handle the breadth and depth of enterprise software workflows, supporting everything from day-to-day coding to large-scale, regulated development.

Its standout capabilities include the features mentioned below.

• Advanced code completion and generation: Delivers intelligent, context-aware suggestions and can generate entire functions, classes, or modules that match an organization’s style guides and best practices.

• Testing and debugging: Creates comprehensive unit tests to improve coverage and can assist in debugging by analyzing logic, detecting issues, and suggesting fixes.

• Code reviews: Acts as an initial reviewer, spotting potential problems in code logic, maintainability, style adherence, and even potential security vulnerabilities.

Gemini Code Assist’s value is most apparent in enterprise environments where integration, governance, and security are mission-critical.

Strengths of Gemini Code Assist#

Its design philosophy makes it an excellent fit for large organizations and complex projects:

• Enterprise-grade security and compliance ensure safe handling of sensitive data.

• Deep integration into the Google Cloud ecosystem allows seamless access to cloud-hosted resources and CI/CD pipelines.

• Ability to be customized and grounded in private codebases. This means that the AI is customized using your company’s internal code to provide more relevant and secure suggestions. This, in turn, results in highly relevant suggestions that match internal conventions.

Limitations of Gemini Code Assist#

While powerful, Gemini Code Assist’s enterprise focus does come with trade-offs:

• Primarily targeted at teams already in the Google ecosystem, making it less attractive to those on other platforms.

• Not optimized for individual hobbyists or small teams without enterprise infrastructure needs.

• The most compelling features, like private codebase grounding, only benefit organizations willing to commit to Google’s broader development environment.

Gemini Code Assist is structured for different user scales, from individuals to large enterprises.

Codex vs. Cursor vs. Gemini: Key differences#

To crystallize our analysis, we’ve created a table summarizing the key attributes of each tool:

Which tool should you choose?#

Having explored their architectures and features, we can now provide a clear recommendation. The decision between OpenAI Codex, Cursor, and Gemini Code Assist is not about which one is the “best” overall, but which one is the right tool for a specific job.

• We recommend the Codex API and CLI if you are a researcher, startup, or developer building custom tools. If your goal is to integrate AI coding capabilities into your own application, experiment with foundational models, or automate tasks from the terminal, the API’s flexibility and the CLI’s scripting power are unmatched.

• We recommend the Codex web agent if you are an individual developer or a team member looking to automate complex, repository-wide tasks through a conversational interface. If you need a powerful assistant to review a pull request, refactor an entire module, or document your codebase by connecting directly to a GitHub repository, the web agent is purpose-built for these high-level actions.

• We recommend Cursor if you are an individual developer or part of a small, agile team focused on maximizing productivity. If you crave a deeply integrated AI experience and are willing to embrace an AI-native editor to gain the benefits of project-wide context and conversational refactoring, Cursor offers the most seamless and immersive workflow available today.

• We recommend Gemini Code Assist if you are working within an enterprise, especially one that leverages Google Cloud. If your priorities include security, data privacy, compliance, and the ability to generate code aligned with your organization’s private repositories and best practices, Gemini Code Assist is built to support them. It is specifically designed to meet those demanding requirements.

As we look to the future, the evolution of these distinct tools signals a maturation in the AI development market. We are moving away from one-size-fits-all solutions and toward a future of specialized assistants. Whether it’s a raw, flexible model, a fully integrated AI environment, or a secure enterprise partner, the future of coding is undeniably collaborative. The next great leap will be in how we, as developers, learn to master these tools. More specifically, it will be about transforming these tools from simple assistants into true creative and cognitive partners in building the software of tomorrow.