Claude Code vs Gemini CLI vs Codex, 2026 Comparison of the three giants of AI command line programming

Claude Code vs Gemini CLI vs Codex, 2026 Comparison of the three giants of AI command line programming

Command-line AI programming will become an independent tool category in 2026. In the past, when developers discussed AI writing code, the default scenario was to pop up completion in the IDE plug-in; this year, more and more developers are running AI directly in the terminal, allowing it to read projects, change files, run commands, run tests, and submit code. This way of working is a bit like having a tireless junior engineer by your side. You give it a task description and it completes it on its own. The three leading companies that make large models have launched corresponding command line tools, Anthropic's Claude Code, Google's Gemini CLI, and OpenAI's restarted Codex CLI. This article does not list scores, but makes a qualitative comparison from the perspectives of core capabilities, cross-file understanding, tool invocation, context window, price strategy, and crowd adaptation to help you judge whether these three tools are suitable for your workflow.

A brief history of command-line AI programming tools

Going back in time, the evolution path of AI code writing can actually be divided into three stages. The first stage is single-line completion. The early version of GitHub Copilot is a typical example. When the cursor is paused for a while in the IDE, a gray prompt text will appear. To accept or reject, press Tab. The second stage is chat-based assistance. Each company has added a dialogue window to the IDE. You can select a code segment and let AI explain or rewrite it. The task granularity is expanded from one line to one function. The third stage is the current command line Agent form. The tool is separated from the IDE and runs directly on the terminal. The task granularity is expanded to a complete requirement, such as adding an interface, migrating a module, and fixing a type of bug. The tool plans its own steps, reads relevant files, runs its own tests, and modifies the code by itself. There are two driving forces behind the rise of this form. First, the model context window is getting longer and longer, and the entire project structure can be stuffed into it at once; second, the tool calling ability of the model has become stronger, and it can reliably decide when to read files and when to execute shell commands. Claude Code, Gemini CLI, and Codex CLI are all products of this wave.

A quick overview of Claude Code’s core capabilities

Claude Code is the official command line tool launched by Anthropic. It runs in the terminal and calls the Claude series model behind it. Its core interaction mode is Agent loop. You enter a natural language task description in the terminal, and the tool will enter a loop, repeatedly deciding which file to read next, which command to execute, and which piece of code to modify. It will not pause until the task is completed or a link that requires confirmation is encountered. Claude Code does not require you to manually specify context files when working. It will automatically explore the project structure according to the task and read related files on demand. This is particularly trouble-free when dealing with unfamiliar code bases. In terms of tool invocation, Claude Code has built-in basic tools such as file reading and writing, shell command execution, and code editing. It also supports access to external tools through the MCP protocol, and its scalability is relatively flexible. In terms of billing method, you can use Anthropic's subscription package, or you can directly use API billing. The specific price level and quota strategy are subject to the Anthropic official website. Regarding the stability of long tasks, Claude Code's experience is relatively stable among the three tools, and it is suitable for large tasks that require continuous running for half an hour or more.

A quick overview of Gemini CLI core capabilities

Gemini CLI is an open source command-line AI programming tool launched by Google. It calls the Gemini series model behind it. Open source is obviously different from the other two. The code repository is public on GitHub. Anyone can view the implementation details, submit PR, and fork to make modifications according to their own needs. At the functional level, Gemini CLI supports accessing local file systems, executing shell commands, calling external tools such as web searches, and has complete basic capabilities. Similar to Claude Code, Gemini CLI also operates in Agent loop mode, allowing you to plan and execute multi-step tasks by yourself. The advantage lies in the integration with Google's own ecosystem. The Gemini model has strong multi-modal capabilities. Multi-modal inputs such as viewing pictures, viewing screenshots, and reading PDFs can be used in the CLI. It is very suitable for scenarios such as changing code based on design drafts and reproducing bugs based on screenshots. In terms of billing, Gemini CLI follows the strategy of the Google AI platform, and the free quota is relatively generous. Please see the official public page for details. The open source attribute also makes it naturally attractive to teams that do not want to be bound to a single vendor.

A quick overview of Codex CLI core capabilities

Codex is the latest addition to OpenAI’s code programming product line. The brand Codex actually has its origins. In the early years, OpenAI used this name to launch a specialized code model, but then it faded out for a while. After the brand was restarted in 2025, it returned to the two forms of CLI and IDE. The Codex CLI discussed in this article is the version that takes the terminal route. Codex CLI runs in the terminal, and is connected to OpenAI's own code enhancement model. Its functional form is similar to Claude Code and Gemini CLI. Both support natural language task input, automatic file reading, automatic code modification, and shell calling. Codex CLI is differentiated mainly in two directions. First, it is deeply integrated with the ChatGPT subscription ecosystem. Users who have paid to subscribe to ChatGPT can share the same subscription entrance when using Codex CLI. Second, the model is specially strengthened in coding tasks, and supports more direct support for common programming languages ​​and mainstream frameworks. The specific subscription price level and API billing method are subject to the official public page of OpenAI. In terms of overall positioning, Codex CLI is more suitable for users who are already in the OpenAI ecosystem.

Differences in performance between the three in cross-file understanding

Cross-file understanding is the key capability of the Agent-style CLI, and it is also the gap between it and the older generation of single-line completion tools. In this regard, all three tools can automatically explore the project structure, read relevant files on demand, and track function call relationships across files. However, the actual experience will have perceptible differences under different project scales. For small projects, such as dozens of files, a single language, and clearly structured scenes, there is not much difference between the three tools, and they can all accurately locate the location to be changed. Medium-sized projects begin to show differences. Claude Code is more stable in its active exploration strategy and will read the project entry and configuration files first before expanding; Gemini CLI can plug in more source files at once with its long context window, which is suitable for reading the entire directory tree at once; Codex CLI recognizes the standard structure of mainstream languages ​​​​faster. For large-scale projects, such as thousands of files, across multiple languages, and scenarios with historical baggage, all three tools will encounter challenges. The strategies of fuzzy retrieval and context compression are different, and the actual performance needs to be measured on a project-by-project basis. It should be noted here that any judgment of absolute advantages and disadvantages must be based on specific projects. This article does not give a simple conclusion of "who is better", because different code bases and different tasks are very different in the friendliness of different tools.

Comparison between tool invocation and external command execution

Another core dimension of command-line AI programming tools is tool invocation and external command execution capabilities. In this regard, all three tools support basic file reading and writing, shell commands, and Git operations. The differences are mainly in the permission model and scalability. Claude Code's permission model is cautious. It will ask the user for confirmation when executing commands that may have side effects, such as git push, rm, and npm install. Operations such as git push, rm, and npm install will not be executed silently by default, making the experience more secure for users who are worried about AI accidentally deleting files. The scalability of Gemini CLI is more open. Because it is open source, users can add custom tools, modify the default permission policies, and package the API of the internal system into tools callable by the Agent. Codex CLI has advantages in integrating with the tool ecosystem of the OpenAI platform. For example, it can be integrated with the existing tool systems of Function Calling and Assistants API, and the tool chains built in the OpenAI ecosystem can be directly reused. For developers, the choice of which tool has more appropriate tool calling capabilities depends on whether they use many external systems on a daily basis, whether they need secondary development permission models, and whether they care about the confirmation prompts for each step of the operation.

Context windows and long task stability

The context window size directly determines how much code the AI ​​can see in a task. The models behind the three tools all support long context. The specific window length is subject to the latest data published by each manufacturer's official website. This article does not give specific numbers to avoid being outdated. It is worth discussing that the window itself does not mean that it is actually available. In long tasks, the context will be continuously occupied by intermediate results, tool returns, and conversation history. The part that can really give the code content is the remainder after subtracting these from the window. The three tools have their own trade-offs in strategies such as context compression, sliding windows, and summary reuse. When actually running long tasks, Claude Code's ability to maintain task goals between multiple rounds of tool calls is relatively stable, which is suitable for continuous tasks that require the Agent to run by itself for half an hour; Gemini CLI performs naturally in scenarios where a large number of files are inserted at once; Codex CLI responds faster in medium-length tasks. The stability of long-term tasks is also related to the tool calling accuracy of the model. One wrong call of the tool may bias the entire task. In this regard, the three tools are continuously iterating. For actual selection, it is recommended to run one or two typical tasks on your own project for comparison.

Subscription pricing and how to get it

Price is an unavoidable topic, but this article will not write specific figures because the price strategies of the three manufacturers are adjusted frequently, and any specific figures may be outdated within a few weeks. The difference in principle can be described this way. Claude Code follows Anthropic's subscription package + API dual track. Individual developers can choose to subscribe monthly to obtain quotas, or they can use API billing for automated integration. For specific price levels, see Anthropic's official website. Because Gemini CLI is an open source tool, the tool itself is free. The real cost comes from the Gemini API called behind it. Google provides a relatively generous free quota. After that, it will be billed on a pay-as-you-go basis. For details, see the official Google AI public page. Codex CLI and ChatGPT subscription ecology are connected. Users who have paid to subscribe to ChatGPT Plus, Pro and other packages can share the entrance. OpenAI API pay-as-you-go is also supported. For details, please see the OpenAI official website. Each of the three has its own positioning in terms of price, but none is particularly expensive. If you really want to use it for a long time, it is recommended to start with a free or low-priced trial, and then consider upgrading the package after the process goes smoothly, rather than buying a top-tier subscription right away.

How to choose between different groups of people

Tools ultimately serve people, and developers with different identities have different concerns and different selections. For independent developers, if they work on small and medium-sized projects on a daily basis and pursue tool stability and long-term task capabilities, Claude Code is a safe choice; if the project has many multi-modal requirements and sometimes needs to modify the code based on screenshots or design drafts, the multi-modal advantages of Gemini CLI are more practical; if you are already using ChatGPT Pro, sharing the Codex CLI entrance can save you a subscription. For researchers and students, Gemini CLI's open source attributes and free quota are relatively friendly, and they can try the working method of Agent programming at a low cost. For entrepreneurial teams, the selection should consider cost controllability and subsequent scalability. It is recommended to use the free quota first, and then decide whether to use the API volume or subscription package based on the size of the team. At the same time, consider the secondary development space of the tool. Teams with internal systems that need to be connected can give priority to the open source ecosystem of Gemini CLI. For enterprise users, data compliance and audit capabilities are hard indicators, and priority should be given to evaluating each vendor's enterprise version data terms and private deployment options. There is no absolute best tool, only the tool that best suits your workflow.

FAQ

Which of the three tools is best for beginners?

For beginners who have never been exposed to command line AI programming, it is recommended to start with Gemini CLI. There are three reasons. First, the documentation and community information of open source tools are relatively complete and can be found when encountering problems; second, the free quota allows beginners to play with confidence without worrying about bills; third, the entrance is simple, just install the tool and configure the API key to start running. After you are familiar with the basic working methods of Agent programming, it will be easier to feel the trade-offs of different tools by trying Claude Code and Codex CLI for comparison. It is recommended that beginners choose a small project that they are familiar with for experimentation when using it for the first time. First, they can watch the process of reading files and changing code with the tool to understand its working mode, and then let it do more complex tasks.

Do I have to install them all to compare?

No need. The core interaction modes of the three tools are very close. If you have experienced one of them, you can basically understand what Agent-style CLI is about. It is more cost-effective to choose a main tool for daily work and use it well. The hidden cost of tool switching is underestimated by many people. Every time you switch, you have to re-familiarize yourself with permission policies, configuration files, and tool calling habits. If you really want to make a horizontal comparison, it is recommended to run a typical task on one or two projects that you commonly use, such as adding an interface or fixing a cross-file bug, to see which tool's output is closer to your expectations. Limiting horizontal comparison to the types of tasks you really care about is more reliable than looking at any evaluation list.

Can domestic users directly use Claude Code and Codex?

The official APIs of Claude Code and Codex CLI will encounter some problems when accessed in mainland China's network environment. Specific manifestations and solutions have been discussed in various communities. Network conditions are not the focus of this article and will not be discussed here. It should be reminded that any method of bypassing official network access must assess the compliance risk by itself. For commercial projects and scenarios involving sensitive data, it is recommended to take the compliance path first, such as using manufacturers with official channels in mainland China, or switching to domestic open source large models with compatible CLI tools. Because Gemini CLI is open source, it can theoretically replace the back-end model and point the model interface to other compatible APIs. This provides domestic users with a certain degree of flexibility. Whether it can be run through depends on the implementation details of the fork.

What are the differences in the security policies of the three tools?

The core difference in security policies between the three tools lies in the permission model and default behavior. By default, Claude Code will request user confirmation for commands that may have side effects, such as deleting files, pushing codes, executing long-term commands, etc. The whitelist can be customized through the configuration file. Because Gemini CLI is open source, the permission model can be modified by yourself. The default behavior is open and suitable for use in isolated containers or virtual machines. Codex CLI is connected to the security mechanism of the OpenAI platform in terms of permission policies, and there is a sandbox option to choose from. No matter which tool you use, it is recommended to develop several habits. The first is not to run the Agent directly in the directory containing sensitive credentials, put the key in the environment variable and properly manage it; the second is to use Git for version control on important projects, and make sure there is a clean submission point before any Agent operation; the third is to turn on the confirmation prompt for key operations, and do not turn off all reminders for the sake of speed.

How do these CLIs share configurations during team collaboration?

In team collaboration scenarios, all three tools provide project-level configuration files to write project-specific rules, style preferences, tool whitelists, and context file lists into version management, allowing team members to share the same Agent behavior definition. The specific format and naming of configuration files vary slightly from company to company. Common naming conventions include AGENTS.md, .cursorrules, and CLAUDE.md. Please refer to the official documentation for details. The core idea of ​​shared configuration is to regard the instructions to the Agent as part of the project, and enter version management together with the code, tests, and documents. In this way, when new members join the project, they can immediately obtain the team's unified behavior habits as long as they install the corresponding CLI tool, reducing the problem of inconsistent output styles between different people using the same tool. The content that can be agreed upon in the configuration file includes code style, submission information format, test running commands, directories that are not allowed to be modified by the Agent, a list of sensitive files, etc., and will be gradually improved according to the actual needs of the project.