Recently, tech giant Google officially launched its independently developed Gemini CLI tool to the developer community, which has created a heated debate in developer circles. The command-line interface tool centers on the integration of advanced AI Q&A and content generation features, and is designed to empower developers with AI technology, thereby significantly improving their productivity and user experience.
The heart of the Gemini CLI is its powerful Gemini 2.5 Pro inference model. The model has the ability to process up to 1 million token contextual messages and has the power to accomplish complex and large-scale data processing tasks. What’s more, it has deep integration with Gemini Code Assiss Code Assistan and introduces the Model Context Protocol (MCP), as well as Google search integration. This means that developers can enjoy the convenience of AI in many areas such as programming, content creation, task management and problem solving. The Gemini CLI certainly provides a smarter, more efficient solution than traditional coding methods.
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Although the Gemini CLI is currently in preview, Google has released a free license for Gemini Code Assist to developers, which can be obtained by simply using a personal Google account. The industry sees this move as an important step for Google to tap its AI models into developer workflows. It also directly benchmarks OpenAI’s Codex CLI and Anthropic’s Claude code, and competition is emerging. Offering a free license puts a lower bar on developers and helps them more easily experience and evaluate the value of the tool.
Since the release of the Gemini 2.5 Pro model in April of this year, Google’s AI technology has garnered a lot of attention from the developer community. Not only has this contributed to the popularity of third-party AI programming tools such as Cursor and Github Copilot, but it also reflects Google’s commitment to strengthening its direct engagement with developers. It’s worth noting that the Gemini CLI does much more than code generation. It can also generate videos with the Google WEO 3 model and use in-depth research proxies to generate research reports, connect to Google to search for real-time information, and even connect to external databases for information integration and effective utilization. This versatility makes the Gemini CLI more than just a coding tool; it’s more like a full-fledged AI assistant.
To promote co-development across the AI ecosystem, Google chose to open source the Gemini CLI under the Apache 2.0 relaxed license and encouraged developers to actively participate in contributing to the project on the Github platform. Google also shows a large quota of bona fide resources: free users are allowed to post 60 model requests per minute, with a daily limit of up to 1,000. given the average usage by developers, it’s fair to say that such a quota is very generous, and it certainly provides a great deal of convenience for developers.
It’s worth noting that with the rapid popularity of AI coding tools, the industry’s trust in them is gradually surfacing. According to a survey conducted by Stack Overflow in 2024, only 43% of developers recognize the accuracy of AI tools. In addition, some studies have pointed out that AI-generated code can introduce errors and may even make it difficult to address security vulnerabilities. Despite the huge potential of AI coding tools, these issues will also require the industry to work together to ensure that they can truly empower developers rather than introduce potential risks.
