The race for AI developer dominance is no longer just about chatbots generating snippets of code. We’ve entered a new era: the age of the AI terminal agent. Two of the most powerful contenders are Anthropic’s Claude Code and Google’s newly released Gemini CLI.
Promising to live in your terminal, understand your codebase, and accelerate your workflow, they sound like a developer’s dream. But how do they hold up in a real-world project? I decided to find out. I installed both, started the same project from scratch with each, and put them through their paces. The results were surprising.
The Gemini CLI Experience: A Story of Speed and Stumbles
Getting started with Gemini CLI had a few initial hurdles with account authentication, but once it was running, the initial experience was impressive. It was fast. I had the distinct feeling it was leveraging the massive context window of the Gemini models, quickly grasping the scope of the project.
However, the honeymoon period was short-lived. After a few hours of development, Gemini CLI ran into its first major wall while trying to solve some emergent issues in the frontend code. It got stuck. Badly. It entered a desperate loop, repeatedly trying the same failing solutions without making any real progress.
The real shock came when I checked my API usage. In just those few hours of frantic, looped debugging, it had burned through over €150 in API credits. It was a powerful lesson in how quickly an AI agent without robust error handling can run up costs.
The Claude Code Experience: Slow and Steady Wins the Race
In stark contrast, Claude Code felt like a seasoned architect. Its approach was more deliberate and methodical. From the outset, it seemed to be considering factors beyond just the immediate code, like safe authentication and long-term scalability.
This meant it took longer to build out a similar set of features compared to Gemini’s initial sprint. But the difference in quality was night and day. The code was more robust, the structure was cleaner, and critically, it never got trapped. When it encountered an error, it would pause, analyze, and suggest a different, more logical path forward.
The most telling moment of the entire experiment? I used Claude Code to successfully debug and fix the very issues that had completely stumped Gemini CLI. It calmly analyzed the flawed code from the other project and provided the correct solution.
Head-to-Head: My Key Takeaways
| Feature | Gemini CLI | Claude Code |
|---|---|---|
| Initial Speed | 🚀 Very fast initial output. | 🐢 More methodical and deliberate. |
| Robustness | brittle; got stuck in debugging loops. | ✅ Highly robust; navigated errors logically. |
| Problem-Solving | Struggled with complex debugging. | Excellent; could even fix the other AI’s errors. |
| Cost-Effectiveness | 💸 Expensive due to inefficient loops. | 💰 More predictable and cost-effective. |
| Overall Quality | Good start, but the final output was fragile. | Higher quality, more scalable code from the start. |
My Verdict: For Now, There’s a Clear Winner
Gemini CLI is a promising and powerful tool, and I have no doubt that Google will iterate on it rapidly. For hobbyists or those who can take advantage of a free tier for smaller tasks, it’s an exciting glimpse into the future.
However, for serious development in larger applications where reliability, robustness, and predictable costs are non-negotiable, my experience shows that Claude Code is currently the superior tool. Its more planned, robust approach saved time, money, and frustration in the long run.
It will be fascinating to watch this race unfold. But for my own projects, for now, my terminal is home to Claude Code.
What are your experiences with these tools? Have you found one to be better than the other? Let me know in the comments!