I Built 3 Apps Using Claude Code, Gemini CLI, and Codex CLI — One Winner Dominated

The AI CLI Coding War of 2026: Claude Code Wins in Every Single Round

Claude Code for AI-powered terminal development just changed how I write software forever, and I did not see it coming.

A few months ago, I was perfectly happy sitting inside my code editor, chatting with an AI assistant through a side panel, watching it autocomplete my thoughts like a very fast intern.

Then something shifted.

Gemini released a terminal CLI.

OpenAI dropped Codex CLI.

Anthropic had already been building Claude Code quietly in the background, and by 2026, all three tools were sitting on my screen asking me to close my editor and live in the terminal.

I was not ready.

But then Reddit made the decision for me, the way Reddit always does, with threads full of developers saying the terminal is the only real way to build with AI now.

So I rolled up my sleeves, installed all three tools back to back, and spent real time building actual projects with each one.

What I found surprised me, especially when it came to Claude Code.

We strongly recommend that you check out our guide on how to take advantage of AI in today’s passive income economy.

What Is the Point of Coding in a Terminal Anyway?

Before I get into the results, I want to answer the question I kept asking myself before I started this test.

Why would anyone willingly leave a polished code editor like VS Code or Cursor, with its file trees, syntax highlighting, and visual diff views, just to type commands into a black box?

The answer, it turns out, is agents.

When you use AI inside a chat interface, you are sending one message, waiting for one response, and then reviewing one chunk of output.

It is a conversation, and conversations are slow.

When you use a terminal CLI with an AI agent running inside it, something different happens entirely.

The AI can open files, read your entire codebase, run commands, install packages, test its own output, catch errors, and then fix those errors, all without you lifting a finger after the first prompt.

That is not a conversation anymore.

That is a worker.

And once I understood that framing, everything about this comparison changed.

The three tools I tested were Gemini CLI by Google, Claude Code by Anthropic, and Codex CLI by OpenAI, and I put each one through the same real-world test to see which one actually delivers as a working AI coding agent in 2026.

Gemini CLI — The Free Powerhouse With a Catch

Getting Started With Gemini CLI

The first thing that pulls people into Gemini CLI is the price tag, and honestly, that is a fair reason to start here.

Gemini CLI is completely open source, available to download directly from GitHub for both Windows and macOS, and it runs with a single NPX command or as a globally installed npm package.

What makes it genuinely attractive is that it comes with a free tier that gives you access to Gemini 2.5 Pro, Google’s most capable model, with up to 60 requests per minute and 1,000 requests per day at absolutely no cost.

For a developer who is experimenting or building side projects on a budget, that number matters a lot.

Setup is clean. You call Gemini in the terminal, pick a theme, sign into your Google account, and you are live.

I pointed it straight at one of my real projects, a website running on Wix Studio that uses Velo, Wix’s proprietary JavaScript framework, and asked it to animate the hero section.

I connected the site to GitHub first, pulled it into VS Code, opened the terminal inside VS Code, called Gemini, and watched it go.

Just like an AI chat interface, it started making changes to the files, but the key difference is that you can spin up multiple Gemini instances running in parallel, each one completing a different task in the background at the same time.

What Gemini CLI Does Well and Where It Falls Short

Gemini CLI also connects to MCP servers, which opens up a huge range of possibilities for tool use and external integrations.

I connected it to the Wix MCP server using a simple JSON configuration file, relaunched the terminal, and it authenticated the server, giving it direct access to make changes inside my Wix Studio environment on my behalf.

That part was genuinely impressive.

The context window is large enough to handle a full project codebase in one session, and the parallel instance feature means you can essentially have multiple AI workers running different feature branches at the same time.

To test its raw build quality, I asked it to create a financial budgeting app from scratch.

It built the plan, scaffolded the project on top of Next.js, and delivered a working build.

The output was functional, but visually it felt closer to a rough draft than a finished product.

I have seen much cleaner one-shot builds come out of Lovable or Bolt.new, but those tools are built for visual output, not for the deep codebase manipulation a CLI agent is designed for.

The real warning with Gemini CLI is the credit burn.

That generous free tier disappears faster than you expect when you are running parallel agents, pulling large context windows, and using web search features at the same time.

Track your usage carefully or you will hit the wall mid-session.

Claude Code — The Original Terminal Agent That Still Leads the Pack

Installing and Setting Up Claude Code for AI-Powered Development

Claude Code was here before terminal coding CLIs were trending, and that head start shows in every corner of the product.

Installing Claude Code for AI-assisted terminal coding follows the same global npm install pattern as Gemini, but the onboarding experience immediately feels different in a way that is hard to put into words until you experience it.

The first thing it suggests after installation is running the forward-slash initialize command, which triggers a full read of your project directory.

It burns some tokens during this phase, but what it produces in return is a README file so well-written and thorough that another engineer jumping onto your project cold could understand the entire codebase just from reading it.

That detail alone tells you something about how Claude Code was designed.

It does not just complete tasks.

It tries to understand the project as a whole before touching anything, which is a fundamentally different philosophy from tools that jump straight into writing code on the first prompt.

You will need billing connected, either through an Anthropic API key or a paid Claude.ai account, so this is not a free tool, and that is a fair point of comparison against Gemini’s free tier.

But the output quality difference starts justifying that cost quickly.

Building Real Apps With Claude Code and What Makes It Different

I ran Claude Code through the same financial budgeting app prompt I had given Gemini, building it completely from scratch to make the comparison clean and direct.

I ran this test inside Cursor, which now has Claude Code integrated directly into the IDE interface, making it more accessible for developers who are not yet comfortable living entirely in a raw terminal window.

The difference in output was visible within the first few minutes.

Claude Code’s completed budgeting app had a cleaner visual design, more thoughtful feature architecture, and extras that Gemini’s version simply did not include, like the ability to filter and track expenses by week, month, or year, plus a running monthly savings projection based on the budget you create.

But the output quality is only part of what makes Claude Code for AI-powered terminal development stand apart from the competition.

The bigger difference is how it thinks.

When Claude Code works on a task, it does not just start writing code immediately.

It reasons out loud first, builds a plan, then executes that plan step by step, and then tests its own output after each major change.

This process uses more tokens than a tool that jumps straight into implementation, and at first glance that feels like a flaw.

But once you watch how rarely it needs to backtrack or correct itself compared to tools that move faster and break more things, the token cost starts looking like an investment rather than a waste.

There is a version control gap worth addressing honestly here.

Unlike the revision history you get inside Cursor’s chat panel or VS Code’s built-in diff view, Claude Code does not give you a visual before-and-after for every change it makes in the same way a sidebar AI does.

The workaround that actually solves this cleanly is disciplined Git commits.

Commit before you start a Claude Code session, commit after major milestones, and you now have a full change log you can review, cherry-pick from, or roll back to at any point.

It is an extra step, but it becomes automatic quickly.

Claude Code also supports parallel agent execution, either by opening multiple terminal tabs and running separate instances, or by writing parallel sub-agent instructions directly into your system prompt, since Claude Code natively understands how to spin up and coordinate sub-agents on its own.

Just keep an eye on token consumption when running multiple parallel agents because it scales up fast.

OpenAI Codex CLI — The Third Contender That Comes Close But Does Not Quite Get There

Codex CLI on the Web and in the Terminal

OpenAI’s entry into the terminal coding CLI space is Codex CLI, available on GitHub and installable as a global npm package in the same pattern as its competitors.

But Codex also offers something neither Claude Code nor Gemini CLI has in the same form, which is a web-based interface that connects directly to your GitHub repositories and lets you manage parallel agents through a browser dashboard rather than a terminal window.

I tested the web version first.

I connected it to my GitHub account, opened the same Wix Studio project I had used for Gemini, and it immediately generated three parallel starting tasks: explain the codebase, audit for bugs, and flag fixable issues.

All three ran simultaneously in the background with their own isolated agent containers, and I could expand each one to watch a live log of what was happening inside each container in real time.

The first task delivered a clean written summary of the project architecture, identifying the Velo framework and breaking down what each part of the codebase does.

The bug audit flagged a cleaner way to run a command for toggling a navigation menu element I had built earlier.

The pull request creation was a single click from the interface, which is a genuinely polished touch for a web tool.

Codex CLI in the Terminal and Why Claude Code Still Wins

Switching to the terminal version of Codex CLI, the setup follows the familiar pattern: install the package, log into your OpenAI account, connect your organization ID, and call codex in your project directory.

I gave it the same financial budgeting app prompt I had used with Claude Code and Gemini to keep the comparison equal.

It took roughly the same amount of time to complete as both of its competitors, which is a positive sign for consistency.

The problem showed up when the build finished.

Unlike both Claude Code and Gemini CLI, Codex did not install Next.js as part of the scaffolding process.

I had to open a second terminal tab, manually install and configure the project dependencies, and run the server myself before I could even preview the output.

That extra friction is not catastrophic, but it reveals something about the tool’s approach to the full development lifecycle.

When I previewed the finished app, it worked.

The core functionality was there and the logic was sound.

But visually, it sat a clear step behind both Claude Code and Gemini in terms of design quality and feature completeness.

It also lacked Claude Code’s weekly, monthly, and yearly expense tracking features, which had made that version feel like a real product rather than a proof of concept.

Codex CLI has strong bones, especially in the web interface’s multi-agent dashboard, and it will likely improve fast given OpenAI’s development pace.

But right now, in 2026, it is not leading this comparison.

Final Verdict — Which AI Coding CLI Should You Actually Use?

After running all three tools through real builds, real projects, and real sessions that lasted multiple hours each, the ranking is clearer than I expected it to be.

Gemini CLI is the right starting point if cost is your primary constraint, the free tier is genuinely generous and the parallel agent support and MCP server integrations make it a capable tool for experimentation and smaller projects.

Codex CLI earns points for its web interface and GitHub integration, and developers who are already deep in the OpenAI ecosystem will find the workflow familiar and convenient.

But Claude Code for AI-powered software development is the tool I kept coming back to after every test session, and it is the one I recommend if you are serious about using a terminal agent to build real things.

The planning-before-execution approach, the consistency of output quality, the native sub-agent coordination, the depth of project understanding it develops through initialization, and the visual quality of what it actually produces all place it ahead of both competitors in every area that matters for real daily development work.

The token cost is real, but so is the output.

Close your editor.

Open your terminal.

Install Claude Code.

You will understand why it leads this space within the first hour of using it.

We strongly recommend that you check out our guide on how to take advantage of AI in today’s passive income economy.