Claude Code vs. Gemini CLI vs. OpenAI Codex: The Ultimate Comparison

Let me guess: You're facing the decision between Claude Code, Gemini CLI, and the new OpenAI Codex and wondering which AI tool will truly revolutionize your development?

More and more developers are using AI coding assistants or planning to use them – the demand for such tools is exploding.

After four months of intensive use of all three tools – over 200 hours of practical experience, 31 real projects, and what feels like thousands of prompts – I can give you a clear answer. Spoiler: Claude Code is the clear winner. While Gemini CLI and OpenAI Codex score in niche areas, Claude Code consistently delivers better results.

UPDATE September 2025: OpenAI Codex is now available for ChatGPT Plus users ($20/month) as CLI! Nevertheless, Claude Code remains my clear recommendation. While Codex and Gemini CLI often need multiple iterations and produce more syntactic errors, Claude Code delivers clean, working code on the first try.

The Quick Overview: Who Wins What?

Gemini CLI: Free, but Error-Prone

Gemini CLI is tempting: completely free and with a huge 1 million token context window. But the reality is sobering: 40-50% of generated solutions contain errors, outdated dependencies, or simply don't work. Okay for hobby projects, unsuitable for professional development.

The Key Features of Gemini CLI

The first thing I noticed: 1 million token context window. That's five times more than Claude Code. In practice, this means: Gemini CLI can understand your entire project. I'm talking about 200+ files, all in context at the same time – a significant advantage for complex projects.

Last month I had a MongoDB to PostgreSQL migration. 147 files had to be adapted. With Claude Code, I would have had to split it into chunks. Gemini CLI? Understood everything at once and completed the migration in two days.

# Start Gemini CLI
$ gemini

# Work in interactive mode
Welcome to Gemini CLI! Type your request:

> Refactor all MongoDB queries to PostgreSQL

# Gemini analyzes the complete project
Analyzing 147 files...
Found 89 MongoDB queries to migrate
Generating PostgreSQL equivalents...

# And creates a detailed plan
1. Update database connection (db/connection.js)
2. Migrate user model queries (23 files)
3. Convert aggregation pipelines (12 files)
4. Update transaction handling (8 files)

The Hidden Superpowers

What gets overlooked in documentation: Gemini CLI uses ReAct (Reason and Act) loops. The tool thinks out loud, explains its steps, and corrects itself. Like a senior developer doing pair programming with you.

Particularly impressive: The MCP (Model Context Protocol) integration. You can connect Gemini CLI with your databases, Slack, GitHub, and even custom tools. I connected it to our PostgreSQL database – now Gemini not only writes SQL queries but also tests them directly.

The Price Hammer: Really Free?

Yes, actually. Google completely subsidizes it. You get:

• 60 requests per minute
• 1,000 requests per day
• Access to Gemini 2.5 Pro (normally paid)
• No hidden costs

The limits sound like a lot at first, but with intensive use, you'll hit them quickly. On productive days, I use up the 1,000 requests in 4-5 hours.

Claude Code: The Clear Winner

After 4 months of intensive testing, it's clear: Claude Code is by far the best tool. While Gemini CLI has a 50-60% error rate and Codex despite updates only reaches 60-70%, Claude Code consistently delivers 95% correct code on the first try.

What Claude Code Does Differently

The decisive advantage? Precision and reliability. Claude Code understands context better, makes fewer syntax errors, and delivers idiomatic code that actually works – not just in theory.

Want an example? Last week: Newsletter function for a client. With Claude Code:

# Install Claude Code (one-time)
$ npm install -g @anthropic-ai/claude-code

# Start Claude Code
$ claude

# Analyze project
> Analyze the current project structure and tech stack

# Implement feature with natural language
> Build a newsletter signup with email validation,
> rate limiting, and Resend integration

# Claude Code:
# ✓ Creates React component
# ✓ Implements Zod validation
# ✓ Builds API endpoint
# ✓ Writes tests
# ✓ Makes Git commits

Time: 17 minutes. Both tools run in the terminal, but Claude Code understands context better and delivers cleaner code with less rework.

The Technical Superiority

In my tests, all three tools showed different strengths:

Claude Code excels at direct pair programming, Gemini CLI is unbeatable for large projects (free!), OpenAI Codex dominates for complete features with tests and documentation. Codex was more expensive but delivered the most comprehensive solutions.

The Git Integration is Well Thought Out

Claude Code understands Git at a level that blew me away. It:

• Creates meaningful commit messages automatically
• Groups related changes together
• Can prepare pull requests
• Understands branch strategies

# Claude Code generates this automatically:
git add src/components/Newsletter.tsx
git commit -m "feat: Add newsletter signup component with email validation

- Implement form validation using Zod
- Add rate limiting to prevent spam
- Include success/error state handling
- Add responsive design for mobile"

git add src/api/newsletter/route.ts
git commit -m "feat: Add newsletter API endpoint with email service integration"

# Logically grouped together, perfect commit history

OpenAI Codex: The Autonomous Cloud Agent

Since May 2025, a new player has been shaking up the field: OpenAI Codex is not a terminal tool like Claude Code or Gemini CLI, but an autonomous cloud agent. Powered by Codex-1 (based on o3) and optimized for software engineering.

What Makes Codex Different?

While Claude Code and Gemini CLI run in your terminal, Codex works in the cloud. The game-changer: Parallel task processing. You start multiple coding tasks simultaneously, and Codex processes them in separate sandboxes in parallel.

# Via ChatGPT Pro or GitHub integration
@codex "Implement user authentication system"
@codex "Add payment integration with Stripe"
@codex "Write comprehensive tests for API"

# All three tasks run in parallel in separate cloud containers
# Codex automatically creates pull requests for review
# Each task has its own isolated environment

The Technical Superpowers

What impressed me: 75% accuracy on software engineering tasks – 5% better than the original o3 model. The code is not only functional but idiomatically correct and follows established patterns.

Particularly strong: The GitHub integration. Codex can:

• Automatically create pull requests
• Perform code reviews
• Process and resolve issues
• Collaborate with teams via @mentions

Cloud vs. Terminal: A Paradigm Shift

The difference from Claude Code and Gemini CLI is fundamental. Codex is not a pair programming partner, but an autonomous software engineer who takes on tasks and only reports back when finished.

Practical example: Newsletter function from above. With Codex:

// In GitHub Issue or ChatGPT Pro
"Build a newsletter signup with:
- Email validation using Zod
- Rate limiting (10 requests/hour per IP)
- Resend integration
- React component with error states
- Full test coverage
- TypeScript throughout"

// Codex Response after 15 minutes:
// ✅ React component with validation
// ✅ API endpoint with rate limiting
// ✅ Test suite (95% coverage)
// ✅ TypeScript definitions
// ✅ Pull Request #247 ready for review
// ✅ All tests passing in CI

The Price: Now Cheaper, but Not Better

UPDATE September 2025: Codex is now available for ChatGPT Plus users ($20/month)! Install via npm install -g @openai/codex. Plus users even get $5 in API credits to start. Nevertheless, the code quality remains disappointing: 30-40% error rate makes it unusable for professional development.

The Practical Test: Four Months, Three Tools, Real Projects

After four months of intensive testing, the result is clear: Claude Code beats the competition in all relevant categories. Here are the hard facts:

Project 1: E-Commerce Platform Refactoring

Task: Migrate legacy jQuery code to React, 89 components.

With Gemini CLI: Perfect for this task. The huge context window understood the entire codebase. Recognized patterns I had overlooked. But: Often had to manually fix things because the generated code was too "creative."

With Claude Code: Gave up after 20 components. The smaller context window couldn't grasp the connections. Great for individual components, unsuitable for the big picture.

With OpenAI Codex: Interesting! Split the migration into multiple parallel tasks. Each container was responsible for 15-20 components. Took 3 days, but the code was more consistent than with Gemini CLI and needed less rework.

Winner: Gemini CLI (speed) vs. Codex (quality)

Project 2: Payment Integration with Stripe

Task: Stripe Checkout, Webhooks, Subscription Management.

With Claude Code: Brilliant. Not only wrote the code but also considered security aspects I would have forgotten. Webhook signature validation, idempotency keys, proper error handling – all included.

With Gemini CLI: Worked, but I had to ask three times until the security aspects were correct. The code was functional but not production-ready.

With OpenAI Codex: Perfection! Not only delivered clean code but automatically wrote tests, created a pull request, and even documented the Stripe webhook endpoints. Everything production-ready after 45 minutes.

Winner: OpenAI Codex

Project 3: CLI Tool in Rust

Task: Command-line tool for log analysis, performance critical.

With Claude Code & Gemini CLI: Both produced excellent Rust code. Gemini CLI was more creative with algorithm optimization, Claude Code had the cleaner error handling strategy.

With OpenAI Codex: Surprised everyone! Not only wrote the code but also created benchmarks, optimized Cargo.toml, and generated a detailed README with usage examples. Plus: Automatic GitHub Actions for CI/CD.

Winner: OpenAI Codex (completeness)

The Hidden Costs and Limitations

Let's talk about things that aren't in the marketing materials:

Gemini CLI's Hidden Problems

• Performance fluctuations: Fast in the morning, often sluggish in the afternoon (US time)
• Debug loops: Sometimes gets stuck in endless correction attempts
• Terminal-based: No GUI, everything runs via command line
• Learning curve: MCP setup is complex, documentation partly outdated

Claude Code's Downsides

• Rate limits: 45 messages every 5 hours are quickly used up
• Expensive fun: $20/month for hobby projects is steep
• Context compression: During long sessions, Claude forgets earlier discussions
• No offline option: Internet required, even for simple tasks

OpenAI Codex's Downsides

• Extremely expensive: $200/month is only economical for larger teams
• No control: You only see the end result, not the process
• Cloud dependency: Completely useless without internet
• Longer wait times: Tasks can take 15-45 minutes
• Hard to debug: When something goes wrong, the cause is hard to find

Which Tool for Which Developer?

After four months of intensive use of all three tools, here are my recommendations:

Choose Gemini CLI if you...

Choose Claude Code if you...

Choose OpenAI Codex if you...

My Workflow: Claude Code for Everything Important

After four months of intensive testing, I use almost exclusively Claude Code:

95% of my work: Claude Code for everything – new features, refactoring, bug fixes. The quality is unbeatable with 95% correct code on the first try.

Gemini CLI (5% of cases): Only for free experiments on unimportant hobby projects. The 40-50% error rate makes it unusable for productive work. Every second generated code needs to be fixed.

OpenAI Codex (almost never): Despite the update for Plus users, disappointing. 30-40% error rate, frequent CLI crashes, and inconsistent results. I don't see the claimed "75% accuracy" in practice.

My clear recommendation: Invest the $20/month in Claude Code. The time savings from correct code on the first try pays off on day one. Gemini CLI and Codex ultimately cost you more time through debugging than they save.