Just as I was settling into my workflow with Claude Code, Google dropped a bombshell: the Gemini CLI. As a command-line enthusiast, this announcement immediately caught my attention. It feels like a direct competitor to what I’ve been using, and it has the potential to be a game-changer.

First Impressions and Key Features

My initial reaction was a mix of excitement and a little bit of irony. The timing is also ironic, as Google released this just as I had upgraded to the Claude Code max plan. The free tier of Gemini CLI is incredibly generous, offering access to the Gemini 2.5 Pro model with its 1 million token context window. This should be more than enough for most developers’ needs.

The fact that it’s a command-line tool is a huge plus for me. As I’ve written before, I believe the command line is the perfect interface for AI coding assistants. It’s fast, efficient, and integrates seamlessly into my existing workflow.

One of the standout features of Gemini CLI is its integration with Google Search. This gives the assistant access to real-time information from the web, which can be incredibly useful for research and problem-solving.

The Double-Edged Sword

The similarities between Gemini CLI and Claude Code create a fascinating dynamic, one that I believe is a double-edged sword for the companies involved. While competition drives rapid innovation, the feature parity makes it difficult to build a “sticky” ecosystem and avoid commoditization.

For me as a user, this is a massive advantage. The command-line nature of both tools means I can have them running simultaneously in different terminals, pointed at the same project directory. I can give them the same prompt and directly compare their performance and output before deciding which changes to accept.

This opens up powerful workflows. For instance, I can leverage git worktrees to take this comparison to the next level. I could have one worktree where I ask Claude to implement a feature on a new branch, and a second worktree where I ask Gemini to do the same. Then, I can use git diff to see a side-by-side comparison of their approaches. This allows for a meticulous evaluation of code quality, style, and correctness, letting me choose the best-of-breed solution to merge into my main branch. It effectively turns my development process into a real-time A/B test of AI coding assistants.

While Gemini CLI has the advantage of being vertically integrated with Google’s ecosystem, it lacks some of the other benefits of Anthropic’s models. However, for those who are already heavily invested in the Google ecosystem, especially with a Google Workspace account, the seamless integration will be a major draw.

Community Reaction and My Own Experience

I immediately checked Hacker News to see what the community was saying. The discussion was lively, with a few key themes emerging:

  • Data Privacy: Concerns were raised about Google using data from free accounts for training, which is not the case for Workspace accounts.
  • Product Confusion: Many users expressed confusion about Google’s ever-expanding lineup of AI products.
  • Authentication Issues: Some users reported problems with Workspace account authentication.

I was a bit worried about the authentication part, as I’ve had my share of frustrations with Google account management in the past. However, the installation was surprisingly straightforward. I did run into a minor authentication issue, but it was quickly resolved by setting the GOOGLE_CLOUD_PROJECT environment variable.

Claude vs. Gemini: A Quick Comparison

Before diving in, I decided to see what both Claude and Gemini had to say about the new CLI. I gave both of them a research prompt, and the results were interesting. Claude produced a highly readable document, while Gemini returned a more academic-style thesis. However, Gemini redeemed itself by creating a well-structured and easy-to-digest webpage that summarized the key information.

The main takeaway from my research is that Claude currently has a slight edge in software engineering performance and code accuracy, while Gemini’s main advantage is its massive 1 million token context window.

Final Thoughts

The release of Gemini CLI is a significant development in the world of AI coding assistants. It’s a powerful, free, and open-source tool that is sure to attract a lot of attention. While I’m still committed to my “focus experiment” as detailed in a previous post, I’m excited to see how Gemini CLI evolves.

I’m sure both products will compete with each other fiercely. Them being command-line tools where the teams can use agentic coding will mean an explosion of features which are easily copied, just as Gemini CLI has already done. The competition is heating up, and that’s always a good thing for us developers.