Claude Code vs. Codex vs. Gemini Code Assist: 2025 dev review

AI-powered coding assistants have evolved from autocomplete into agentic developer tools that can autonomously modify codebases, run tests, and craft pull requests. This blog compares three leading systems: Claude Code (Anthropic), OpenAI Codex (ChatGPT’s coding agent and CLI), and Google’s Gemini Code Assist on practical software development tasks. We focus on how each handles real scenarios, what features they offer, and the trade-offs in openness, cost, and integration so you can choose the best fit for your workflow.

By the end, you will know exactly which tool to use to make your work faster, cleaner, and less exhausting.

Introduction to AI coding assistants#

Beyond the marketing, each tool has a distinct personality, and understanding how they work reveals their intended use. They are all powered by large language models (LLMs), but their approach to helping you code is fundamentally different.

Claude Code (Anthropic)#

Claude Code is Anthropic’s solution for developers who enjoy working in the terminal. It embraces a design that feels familiar to anyone who appreciates the Unix philosophy: a sharp, composable tool that does one thing exceptionally well.

It runs as a CLI tool (claude) and maintains awareness of your entire project structure, with the ability to pull in context from files, web search, and even external sources via the open Model Context Protocol (MCP). Claude Code can directly edit files, execute shell commands, run tests, and commit to git, all from natural language instructions.

Its standout feature is a massive 200K token context window. This means Claude can ingest and reason over a large amount of code at once, roughly equivalent to a 500-page book. In my experience, this is a game changer for tasks that require a holistic view of a project, like debugging a complex issue across multiple services or planning a large-scale refactor.

Claude is also truly agentic. An agentic AI can understand a high-level goal, create a multi-step plan, execute it, and even fix its mistakes without constant supervision. This makes it highly effective for large-scale automation.

It requires either an Anthropic API key, which is pay-as-you-go, or a Claude Pro subscription. It can also be self-hosted via Anthropic’s enterprise solutions.

• Best for: Complex, multi-file automation and deep codebase analysis.

• Pain point it solves: The tedious, error-prone work of large refactors or dependency upgrades.

OpenAI Codex (ChatGPT and CLI)#

OpenAI’s Codex is a versatile tool in two primary forms, both powered by the GPT-4 model.

First, there is the agent inside the ChatGPT Plus interface. This cloud-based agent (powered by the codex-1 model, a GPT-4 derivative optimized for coding) turns coding into a conversation. Using prompts, it can be assigned tasks executed in an isolated cloud environment with your repository preloaded. It reads and writes files, runs tests/linters, and even proposes GitHub pull requests. Task execution is asynchronous, taking anywhere from one minute to 30 minutes depending on complexity, with results including logs and test output citations for verification.

OpenAI offers Codex CLI, an open-source, lightweight agent you run locally in your terminal. Codex CLI uses OpenAI’s API (with models like GPT-4 or a faster codex-mini model optimized for CLI use) to pair program in real time. It can answer code questions and perform edits with lower latency. OpenAI’s solution is partially open (the CLI code is open-source, but models are closed).

ChatGPT’s Codex agent is included with certain plans (Pro/Enterprise, with Plus support rolling out), and API usage is billed per token (e.g., ~$1.50 per 1M input and $6 per 1M output tokens for codex-mini after free credits).

• Best for: General-purpose problem-solving and quick code generation across different languages.

• Pain point it solves: The context-switching and knowledge gaps that come with being a polyglot developer.

Google Gemini Code Assist #

Google’s suite of AI coding tools, called Gemini Code Assist, integrates an AI model (“Gemini 2.5”) across IDEs, a CLI agent, and cloud services. It is available in a free Individuals edition and paid Standard and Enterprise editions.

Gemini Code Assist works in VS Code, JetBrains IDEs, Android Studio, Colab, and Cloud Shell, providing code completion, a conversational chat helper, and an agent mode for multi-step tasks. It cites sources for code or documentation it uses. The underlying model is trained on public code, Google’s documentation, and more, meaning it’s knowledgeable about common frameworks and Google Cloud APIs.

A key differentiator is its focus on enterprise safety. Since it can cite sources for its suggestions, it helps you avoid accidentally incorporating licensed code. Its “developer in the loop” model proposes a plan but requires your approval for each step.

The Gemini CLI is an open-source terminal agent with built-in tools (like grep, file I/O, and web search) and MCP servers to handle complex tasks in your local environment. Notably, it can run directly in Google Cloud Shell without setup.

Google offers generous free usage for individual developers (the VS Code extension has over 1.4M installs and is free to use), while Standard and Enterprise licenses cost ~$19–$45 per user/month for larger usage quotas and features like private code customization.

• Best for: In-editor assistance and accelerating the daily code-edit-test loop

• Pain point it solves: The constant friction and small interruptions that break your flow.

Claude Code vs. Codex vs. Gemini Code Assist: The comparison arena#

To compare the assistants, I used a small React Flappy Bird–style game as a proxy for a real, cohesive project. I used each tool to perform two common tasks:

1. Modify a React component’s logic.

2. Add a new feature to the game.

For each task, I documented the prompt, the steps taken by the AI, key metrics (time to result, files/lines changed, and test outcomes), and a brief quality note. All tasks were run with default configurations of each tool using the latest models available. Times are approximate and based on observed performance. A human reviewed all code for correctness.

To set the stage (and for some fun), here is a working demo of the React Flappy Bird game. You can view the code on the GitHub repository as well.

Preparing the environments#

I set up each assistant in a standard development environment to ensure a fair comparison. The process was straightforward for all three tools.

Claude Code CLI was installed from npm with a single command: npm install -g @anthropic/claude-cli. After installation, I configured it to work with an Anthropic API key.

I installed the official extension from the Visual Studio Code Marketplace for Gemini Code Assist. The setup involved a quick search, one click to install, and signing in with a Google account. If you’re using a Google Workspace account, it might prompt you to connect the extension to a GCP project.

You can access the OpenAI Codex agent through the standard ChatGPT web interface. This requires no local installation, only a subscription. However, suppose you’re running tasks on a repository for the first time. In that case, you will need to set up an environment, although it can run common scripts like npm install automatically for a simple project.

Task 1: Modify a React component#

Many frontend developers waste time making small but repetitive UI tweaks. I asked them to animate Flappy Bird’s wings to test how each AI coding assistant handles this. This required state management, rendering logic, and minimal disruption to existing code.

Prompt used: “Create a flapping animation for the bird’s wings. The wings should always be flapping when playing the game.”

Claude Code#

Claude reviewed the FlappyBird component and added a smooth wing flapping animation inside the drawBird function. It created CSS keyframes and used a sine wave function (Math.sin(t * 0.8) * 0.3). The animation keeps running even when the game is paused, giving the bird a natural and lively flapping motion throughout the gameplay.

• Metrics:

  • Time taken to produce the result: Just under a minute

  • Files/lines changed: 1 file modified (FlappyBird.jsx), 31 lines added and 2 removed

• Quality note: The solution was correct and well implemented. Claude took a creative approach and coded it so that the bird’s wings would keep flapping even when the game was paused.

OpenAI Codex#

Codex updated the wing drawing logic by adding a sinusoidal rotation. This makes the bird’s wing pivot at its base and flap continuously during gameplay, creating a more natural motion.

• Metrics:

  • Time taken to produce the result: Two minutes and 14 seconds

  • Files/lines changed: 1 file modified (FlappyBird.jsx), 9 lines added, and 1 removed

• Quality note: The solution was correct, robust, and performant. Codex used CSS keyframes correctly for the animation and produced clean, readable code.

Gemini Code Assist#

Gemini Code Assist also modified the drawBird function for the flapping animation. It used the game’s internal timer (worldRef.current.t) along with a sine wave to create a smooth, looping up-and-down motion.

• Metrics:

  • Time taken to produce the result: Less than a minute

  • Files/lines changed: 1 file modified (FlappyBird.jsx), 4 lines added, and 2 removed

• Quality note: Gemini Code Assist made the fewest number ofchanges to achieve the desired flapping effect. I was pleasantly surprised by the sheer simplicity of the implementation.

Final takeaway (Task 1): All three assistants handled this single-component change successfully. Gemini Code Assist takes the win here because it produced a working solution with minimal additions to the code. Codex produced the cleanest and most readable code, while Claude provided a creative solution.

Here’s a demo of Gemini Code Assist’s implementation.

Task 2: Add selectable gravity modes#

The game currently has a hardcoded gravity value that determines how fast the bird falls. I asked each assistant to add UI controls that allow the player to switch between “Low,” “Medium,” and “High” gravity settings. My goal was to test the AI’s ability to refactor existing logic, introduce new states, create new UI components, and handle component communication.

Prompt used: “Implement a feature to change the game’s gravity. The current gravity is a constant. Refactor it to be a state variable. Add a drop-down to the UI labeled ‘Low’, ‘Medium’, and ‘High’. Clicking an option should adjust the bird’s falling speed accordingly, with ‘Medium’ as the default.”

Claude Code#

Claude added a new gravity state with three options: Low (0.6x, slower falling), Medium (1.0x, default), and High (1.4x, faster falling). It added a drop-down for selecting gravity. Changing the gravity setting also automatically restarts the game.

• Metrics:

  • Time taken to produce the result: One minute and 25 seconds

  • Files/lines changed: 1 file modified (FlappyBird.jsx), 33 lines added, and 3 removed

• Quality note: Claude kept the implementation straightforward, using a multiplier on the world gravity. Claude also correctly added the drop-down matching the existing UI patterns.

OpenAI Codex#

Codex also successfully implemented selectable gravity modes by adding a gravityLevel state. The new game’s gravity is now updated through an effect that recalculates values based on the chosen level and display size. It also added a drop-down with “Low,” “Medium,” and “High” options.

• Metrics:

  • Time taken to produce the result: Three minutes and 41 seconds

  • Files/lines changed: 1 file modified (FlappyBird.jsx), 29 lines added, and 1 removed

• Quality note: Codex did something unexpected here. Instead of using the hardcoded gravity value that was previously in the game, Codex created a dynamic gravity effect that it calculated using the height of the current display. It then added a multiplier to it. I’m not sure how I feel about this. On one hand, it makes sense to scale the gravity based on the scale of the game, but, on the other hand, it might result in an inconsistent game experience across different viewports.

Gemini Code Assist#

Gemini Code Assist also added a gravityLevel state to store the chosen setting (low, medium, or high). The physics logic was updated so that gravity adjusts dynamically with this state. It also added a drop-down menu to let us change gravity.

• Metrics:

  • Time taken to produce the result: Around one minute.

  • Files/lines changed: 1 file modified (FlappyBird.jsx), 29 lines added and 6 removed.

• Quality note: Gemini Code Assist proved to be the fastest and implemented a correct solution. Like the rest, it also created a drop-down similar to our current implementation. A good surprise was that Gemini Code Assist also tidied up the Tailwind classes that were used to display the HUD.

Final takeaway (Task 2): Claude made the most changes to the code but produced clean, readable code. OpenAI Codex opted for the creative solution here, which, although correct, might not be the best use of its creativity for our game. Gemini Code Assist produced the cleanest and most modular solution, while also cleaning up some Tailwind CSS code.

Here’s a demo of Gemini Code Assist’s implementation.

Feature comparison table#

To summarize the key features and differences of Claude Code, OpenAI Codex, and Gemini Code Assist, here’s a side-by-side comparison:

Key factors for daily developer productivity#

My hands-on testing revealed a few key factors that matter more than any marketing claim.

Code generation quality#

Raw code quality is one of the most important factors. While hard to quantify perfectly, performance on benchmarks like the SWE-bench leaderboard shows that models from Anthropic and OpenAI are top-tier. In my own tests, I found that:

• Claude Code produces clean, well-structured code and is particularly good at elegant refactoring.

• OpenAI Codex is excellent at synthesizing answers from broad knowledge, though it can sometimes be verbose.

• Gemini Code Assist is highly competitive and does a great job when working with Google-specific frameworks like Android or Angular.

Trust and verification#

Never ship unverified code. This is where process matters.

• Gemini Code Assist builds trust through transparency. Its source citation feature is a huge win for any team concerned about intellectual property.

• All assistants also build trust through validation. Their ability to run your test suite to check their own work creates a powerful, self-correcting feedback loop.

Workflow integration and friction#

Friction slows us down. How easily a tool fits into your workflow determines how often you will use it.

• Lowest friction: Gemini Code Assist wins here. It is available inside your IDE, meaning zero context switching.

• Medium friction: Claude and the Codex CLI require moving to the terminal. For developers comfortable in the shell, this transition is seamless.

• Highest friction: The ChatGPT web interface for Codex is best for offloading tasks, not for a tight code-edit loop.

Which assistant should you choose?#

The tests show that each tool can do the job. The right choice depends on your workflow, project, and priorities.

Choose Claude Code for autonomous power#

Choose Claude when you need to delegate a large, complex task. It excels at architectural refactors, dependency upgrades, and other work that spans multiple files. It is the best choice for developers who are comfortable on the command line and trust an AI to work autonomously. A solid test suite is your best friend when working this way.

Choose OpenAI Codex for versatility#

Choose Codex when you need a versatile consultant. Its broad knowledge benefits full-stack developers who switch contexts frequently. Its ability to work separately in an isolated sandbox on the cloud is great, especially when you are working across multiple projects.

Choose Gemini Code Assist for smooth integration#

Choose Gemini Code Assist when you want a constant pair programmer. It is ideal for frontend development and teams that want to augment their existing workflow without leaving the IDE. Its interactive nature means it sometimes needs more guidance on complex tasks, but its safety-first approach and free tier make it a fantastic, low-risk tool for daily use.

The bottom line

The right choice depends on your role and what you are building.

There is no single “best” tool. You are choosing a specific kind of teammate.

• Claude Code is the autonomous expert you delegate to.

• OpenAI Codex is the knowledgeable consultant you brainstorm with.

• Gemini Code Assist is the diligent pair programmer who helps with every line of code.

Start with the tool that addresses your biggest point of friction, and keep experimenting.

AI as a partner, not a replacement#

These AI coding assistants are not replacements for developers. They are powerful amplifiers of our skills. They automate tedious tasks, accelerate complex work, and act as readily available resources of knowledge.

The real skill now is learning which tool to pick for the work at hand. These tools are being updated constantly, so my best advice is to start experimenting now. Pick the tool that matches your workflow, use it for a week, and see how it changes the way you work.

By letting these AI assistants handle the routine tasks, we free ourselves to focus on the core aspects of software engineering: creativity, architectural vision, and a deep understanding of the problems we are trying to solve. The future of coding is collaborative, and our new teammates are AI-powered.