Codex CLI vs Gemini CLI vs Claude Code: Which is the Finest?

In 2025, a number of AI coding assistants have been launched, which might be accessed immediately from the terminal. Codex CLI, Gemini CLI, and Claude Code are a few of the common names that embed giant language fashions into command-line workflows. These programming instruments that may generate and repair code by way of pure language prompts are actually unbelievable. We doc our analysis of all three of those throughout completely different duties to find out which is most helpful.

Every assistant relies on a classy AI mannequin like o4-mini, Gemini 2.5 Professional, or Claude Sonnet 4 to reinforce productiveness. We place each in the identical setting and take a look at them with particular metrics on sensible programming duties. Various from internet growth to knowledge evaluation, via this, we goal to make the strengths of every agent clear!

Meet the Contenders: Codex CLI, Gemini CLI & Claude Code

The command line is rapidly turning into a battleground for the following era of AI coding assistants. Corporations, together with OpenAI, Google, and Anthropic, have launched superior CLI-based AI coding assistants, every with very highly effective and spectacular capabilities immediately into the terminal. However what are the variations, and which is finest on your workflow? Let’s go over the instruments. 

Codex CLI: OpenAI’s Code-Centric Terminal Agent

Codex CLI capabilities like a wise terminal assistant for coding. It listens to what you say to it and creates code. Codex CLI has entry to your shell and file system. It could actually scaffold a undertaking, write a operate, and repair a bug. Codex CLI is using OpenAI’s Codex fashions within the background. You employ plain English to inform Codex CLI what code you prefer to for a activity. Then the CLI suggests new code and information. Codex CLI helps a number of languages, together with Python, JavaScript, and Go.

Gemini CLI: Google’s Terminal Agent

Gemini CLI by Google brings collectively the strengths of the Gemini 2.5 Professional mannequin with entry to the terminal and filesystem with the intention to create an uninterrupted coding and utility assistant for builders. It may be used for way more than easy code era. Gemini CLI is adept at finishing duties in actual time, akin to acquiring stay info or operating shell instructions. Developed on the Google infrastructure and built-in with varied instruments akin to VS Code AI, Gemini CLI supplies utility throughout terminals and IDEs.

Claude Code: Anthropic’s CLI Assistant

Claude Code is a number one coding AI made for high-performance terminal workflows. It’s based mostly on Claude Sonnet 4 and might simply deal with end-to-end software program growth capabilities. Resembling writing new modules to operating checks, to robotically creating pull requests. Claude Code goals to offer depth, consistency, and certified codebase navigation. Whereas it’s skill-based and closed-sourced. So if you’re an expert software program developer on the lookout for AI that may perceive and evolve giant, advanced tasks, Claude Code is for you.

Codex CLI vs Gemini CLI vs Claude Code: Abstract

How We Examined Them: Setup, Metrics & Duties

Testbed & Setting: All of the CLI-based AI coding assistants have been examined utilizing an area workstation operating Ubuntu 24.04. The brokers Codex CLI (based mostly on OpenAI’s o4-mini), Gemini CLI (Gemini 2.5 Professional), and Claude Code (Claude Sonnet 4) have been put in by way of npm or pip. Codex CLI and Claude required Node.js and legitimate API keys. Gemini CLI required a Google login for authentication.

Analysis Metrics That Matter: We evaluated every agent based mostly on 5 standards: 

• Code correctness
• Code era pace
• Simplicity of prompts
• Output readability
• Dealing with of errors

These measures take a look at not simply efficiency, however how usable and dependable a developer can count on the brokers to be in an actual workflow.

Actual-World Duties Used within the Battle: Every agent was tasked with three duties to check versatility:

• Construct a recreation just like Tremendous Mario.
• Construct a Climate Clock that presents the time and the climate.
• Start exploratory knowledge evaluation (EDA) in Python utilizing the Nike_Sales_Uncleaned.csv dataset.

Codex CLI vs Gemini CLI vs Claude Code: Process-by-Process Faceoff

Process 1: Making a Tremendous Mario Sport 

Aim: Construct a fundamental 2D Mario-style recreation

Immediate: “Create a fundamental 2D Tremendous Mario-style platformer recreation. The sport ought to function a easy tile-based structure with Mario standing on floor blocks, a background sky with clouds, a query mark block above him, and a inexperienced pipe close by. Embody fundamental mechanics like left/proper motion and leaping utilizing keyboard arrow keys. Simulate gravity and collision with platforms. Use pixel-art type graphics with embedded or referenced native property.”

Gemini CLI:

Codex CLI:

Claude Code:

CLI Comparability

• Claude Code: Finest and most related of all three. It additionally makes use of the pixelated model, and the consumer has full management over Mario. It additionally reveals the thriller containers for cash and power-ups, however nothing occurs when Mario hits them.
• Codex CLI: created an interface with a pixelated interface, however was not in a position to play the sport as Mario is trapped contained in the inexperienced field.
• Gemini CLI: created an interface with a block format interface and in a position to play the sport, however the factor is it doesn’t observe the unique guidelines, prefer it permits me to cross via the objects and leap robotically when Mario reaches close to the sting with out urgent the leap key.

Claude Code excels in recreation dealing with logic from each Codex and Gemini. It reveals constant controls, gravity, and collision, and delivers probably the most immersive gameplay expertise.

Process 2: Climate Clock App

Aim: Construct a clock UI with stay climate updates

Immediate: “Design and develop a visually wealthy weather-themed dynamic clock dashboard utilizing solely HTML, CSS, and JavaScript. The primary aim is to create a real-time clock interface that not solely shows the present time but additionally visually adapts to the time of day. Implement 4 animated background transitions representing dawn, midday, sundown, and night time, every with distinctive colours and animated parts like shifting clouds, twinkling stars, or a rising/setting solar/moon, and provide a toggle between 12-hour and 24-hour time codecs. For an added layer of interactivity, embrace a piece that shows a rotating motivational or productiveness quote based mostly on the hour.”

Gemini CLI:

Codex CLI:

Claude Code:

CLI Comparability

• Claude Code: Claude Code supplied probably the most visually profound and feature-complete end result. It carried out 4 animated themes with easy transitions and interactive parts akin to shifting clouds and celestial our bodies. Moreover, Claude Code got here with an auto-theme mode, shifting the backgrounds based mostly on system time. The 12/24-hour toggle and quote-randomization options have been seamlessly completed.

• Codex CLI: Codex CLI had carried out the entire required capabilities and execution, however lacked visible design and polish. The consumer expertise felt antiquated, with static styling and uninspired structure. Functionally, it was sound, however design execution was the weakest among the many three.

• Gemini CLI: Gemini CLI used a set background, i.e, no animation, which introduced down some visible richness. Nevertheless, Gemini was nonetheless a cleaner interface than Codex. Gemini made on a regular basis show and quote-randomization work appropriately, however lacked interactivity and dynamism within the total expertise.

To summarize, Claude Code was forward in UI logic and the general consumer expertise. It introduced collectively sound performance, participating visible transitions, interactive parts, and circulation within the consumer interface. Codex delivered on the essential useful necessities however lacked the UX, and Gemini had a average visible design however very low dynamism.

Process 3: Performing EDA (Exploratory Knowledge Evaluation)

Aim: Clear, analyze, and visualize a dataset

Immediate: “Carry out Knowledge Evaluation and Exploratory Knowledge Evaluation (EDA) on the dataset supplied in the identical listing. All the evaluation ought to be carried out and saved in a Jupyter Pocket book file named eda.ipynb. Start by loading the dataset and inspecting its construction, together with column names, knowledge sorts, and abstract statistics. Proceed to scrub the info by dealing with lacking values, correcting knowledge sorts if mandatory, and eradicating any duplicates. Conduct univariate evaluation to grasp particular person options, after which carry out bivariate and multivariate evaluation to uncover relationships between variables. Use clear and related visualizations to assist your insights. Manage the pocket book with correct Markdown headings and explanations for every step. Conclude with no less than three key observations or insights drawn from the info.”

Gemini CLI:

Codex CLI:

Claude Code:

CLI Comparability

• Claude Code: Claude Code produced a whole professional-grade EDA. It accomplished every bit of the instruction from the immediate, together with the output being organized into three folders:
  • A Plots folder containing all of the generated visualizations
  • A Code folder containing the clear, reproducible pocket book
  • The visuals have been applicable, and the insights have been reported clearly.
• Codex CLI: Codex CLI produced a usable however partial resolution. It produced the mandatory code and suitably adopted the EDA steps, however it didn’t produce any visualizations or present a abstract of necessary insights. The pocket book didn’t have any last analytical conclusions, nor markdown explanations to help in interpretation.
• Gemini CLI: Gemini CLI was unable to finish this activity. It was unable to finish the EDA pipeline and finally produced an incoherent pocket book. There have been many situations of dataset loading failing, no visualizations, and lots of incomplete code blocks.

Claude Code is the one for EDA and knowledge evaluation. It not solely completes the total analytical workflow but additionally organizes the outputs properly and delivers well-structured insights helpful for each single-user knowledge work and team-based environments. Codex could possibly be a helpful backup; nevertheless, Gemini CLI just isn’t applicable for this.

Codex CLI vs Gemini CLI vs Claude Code: Total Evaluation

Claude Code provides a transparent construction and documentation, and is nice to execute. It dealt with the sport logic and error dealing with with out problem. Codex CLI was quick and versatile, however required some handbook intervention. Gemini CLI gave a agency basis and appeared quick. Its polish and documentation have been missing; it suffered probably the most within the EDA task, lacking core outputs and structural completeness.

In pace, Codex CLI was quickest, adopted by Gemini and Claude. Claude was the simplest for immediate engineering. Every CLI was suited effectively to particular workflows. Claude was sturdy on logic-heavy work, Codex could be finest in speed-focused workflows, and Gemini was appropriate for fundamental structured implementations missing refinement.

Conclusion

Claude Code was the very best throughout all duties, offering the highest quality code, consumer expertise, and full vary of options. Whereas it was not the quickest AI coding assistant, its completed merchandise have been polished, documented, organized, and splendid for skilled workflows with lots of belief concerned. Codex CLI was the quickest, and a terrific alternative utilizing to creating fast prototypes or if there was a time constraint on the coding work. 

Gemini CLI was affordable for fundamental builds, however had points with not being quick, polished, or organized for a lot of sorts of labor. It had points with knowledge evaluation duties that required organized or insightful content material. Total, all instruments have completely different matches, however Claude Code supplies probably the most constant depth relating to being a command-line AI coding assistant.

Continuously Requested Questions

Whats up! I am Vipin, a passionate knowledge science and machine studying fanatic with a robust basis in knowledge evaluation, machine studying algorithms, and programming. I’ve hands-on expertise in constructing fashions, managing messy knowledge, and fixing real-world issues. My aim is to use data-driven insights to create sensible options that drive outcomes. I am wanting to contribute my abilities in a collaborative setting whereas persevering with to study and develop within the fields of Knowledge Science, Machine Studying, and NLP.