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Qwen Code CLI vs Gemini CLI: Which one is better?

qwen code vs gemini cli
For those who prefer a familiar interface, Qwen Code CLI is the well-adapted fork, while Gemini CLI offers a powerful yet evolving original experience.

Following Gemini CLI’s path, Qwen has now also released its code CLI to the public. For those unaware, code CLIs function as sophisticated developer copilots that can read and modify files, execute shell commands, fetch web data, and integrate with external tools via the Model Context Protocol (MCP).

Regardless, while both Gemini and Qwen CLIs are open-source and terminal-based, they differ in terms of models, quotas, integrations, and usability. And to help you understand the difference and strengths of each, here’s a detailed Qwen code CLI vs Gemini CLI comparison that covers almost everything you need to know to make a judgment on which one is better for you.

TL;DR

  • Choose Qwen Code CLI if you want a free, generous quota and you’re happy using Qwen3-Coder models (open or hosted). It’s adapted from Gemini CLI but tuned for Qwen’s coder family and offers an especially friendly Qwen OAuth path with 2,000 requests/day for individuals.
  • Choose Gemini CLI if you want tight integration with Google’s ecosystem (Search grounding, Code Assist in VS Code, Vertex AI, GitHub Actions), and access to Google’s Gemini 2.5 Pro with up to a 1M token context. Free tier is solid (up to 1,000 requests/day via OAuth).

Qwen Code CLI vs Gemini CLI—at a glance

Qwen

Qwen Code CLI: “AI-powered command-line workflow tool for developers,” adapted from Gemini CLI and optimized for Qwen3-Coder models. It adds parser-level changes targeting Qwen’s code models and foregrounds workflows like code understanding, editing, and automation. It’s Apache-2.0 licensed and actively developed on GitHub.

Gemini

Gemini CLI: An open-source AI agent that brings Gemini into your terminal. It uses a ReAct loop, integrates Google Search grounding, supports MCP servers, and powers “agent mode” in Gemini Code Assist (VS Code). Also Apache-2.0. You can read a detailed comparison between Gemini CLI, Claude Code, and Cursor here.

How to install Qwen Coder CLI

Both CLIs require Node.js 20+ and install globally via npm.

Qwen Code

npm install -g @qwen-code/qwen-code@latest
qwen –version
qwen

During first run, pick Qwen OAuth for the fastest start (browser login).

Gemini

# Try without install

npx https://github.com/google-gemini/gemini-cli

# or install

npm install -g @google/gemini-cli
gemini

Gemini supports OAuth, Gemini API keys (AI Studio), and Vertex AI/Google Cloud setups.

Verdict: Both are trivial to install; Qwen’s OAuth “just works” experience is especially slick for individuals, while Gemini’s breadth of auth choices is better for teams with Google Cloud or Vertex AI.

Qwen Code CLI vs Gemini CLI: Models and context windows

  • Qwen Code targets Qwen3-Coder models (hosted or OpenAI-compatible endpoints). The repo highlights profiles like qwen3-coder-plus and OpenRouter/ModelScope routes; it’s essentially the “best possible” terminal experience for Qwen’s coder family, including recent Qwen2.5-Coder options if you’re running locally or via compatible providers.
  • Gemini CLI gives you Gemini 2.5 Pro, including up to a 1M token context for enormous repositories or long research threads. If you live in Google’s ecosystem (Search grounding, Code Assist), the CLI is the first-class citizen.

Verdict: If your bottleneck is context length and deep multimodal reasoning with tight Search grounding, Gemini wins. If your priority is leveraging Qwen Coder strengths (especially if you already run Qwen models locally or through OpenRouter/Alibaba endpoints), Qwen Code is the more natural fit.

Qwen Code CLI vs Gemini CLI: Pricing and quotas

Bind AI

A. Qwen Code: (Qwen OAuth) advertises a generous free tier: 2,000 requests/day and 60 requests/min, with no token counting for individuals. Mainland China users get 2,000 requests/day free, while overseas users (via OpenRouter) receive 1,000 requests/day. The agent can fire multiple API calls per cycle.

B. Gemini CLI (OAuth): 60 requests/minute and 1,000 requests/day on the personal account free tier.

  • Gemini AI Studio keys: Google AI Studio usage is completely free in all available countries.

B.1. Gemini API (including Gemini 2.5 Pro model) free tier limits: 5 requests per minute, 25 requests per day via API. Once billing is enabled, paid tiers unlock higher usage—starting with 150 RPM and 1,000 requests/day, scaling up with more spend.

Gemini API pricing (Gemini 2.5 Pro, paid tier):

  • Input price: $1.25 per 1M tokens (prompts ≤ 200k tokens), $2.50 per 1M tokens (prompts > 200k tokens)
  • Output price: $10.00 per 1M tokens (prompts ≤ 200k tokens), $15.00 per 1M tokens (prompts > 200k tokens)
  • Context caching: $0.31–$0.625 per 1M tokens, $4.50 per 1M tokens/hour for storage
  • Grounding with Google Search: $35 per 1,000 requests for paid tier, first 1,500 requests/day free

Verdict: In pure free daily request count, Qwen Code’s OAuth is more generous. Gemini’s free tier is still strong and comes with the Google ecosystem perks.

Qwen Code CLI vs Gemini CLI: Built-in tools and agent loop

Both CLIs do more than chat—they read/write files, run shell commands, search or fetch the web, and maintain session state with slash commands.

Shared ideas

  • ReAct-style loop and terminal-first design.
  • Commands like /stats, /tools, history compression, and session clearing.
  • Multi-file operations and non-interactive/batch modes.

Gemini emphasizes:

  • Google Search grounding (“ground your queries”) and web fetch out of the box.
  • Checkpointing (save/resume sessions).
  • GEMINI.md project context file (persistent memory), plus token caching.
  • The MCP story is very robust—connect to external services (e.g., GitHub, Slack, databases) via configured servers.

Qwen Code emphasizes:

  • Code understanding & editing tailored to Qwen-Coder;
  • Workflow automation for Git and file ops;
  • A parser adapted specifically for Qwen-Coder models.

Verdict: For a batteries-included agent with Search grounding and mature MCP docs, Gemini has the edge. Qwen Code’s advantage is its tight coupling to Qwen-Coder behavior (fewer “lost in translation” moments if Qwen is your model of choice).

IDE and ecosystem integrations

  • Gemini CLI ↔ VS Code (Gemini Code Assist): The CLI powers Agent Mode inside the VS Code extension; you get a subset of CLI features directly in the IDE (MCP servers, /memory, /stats, web search/fetch, “Yolo mode”). There’s also a GitHub Action to automate reviews, issue triage, and @mentions.
  • Qwen Code focuses on the terminal, but since it supports OpenAI-compatible endpoints, you can combine it with your existing editor setups or provider-specific SDKs. The repo’s guidance spotlights multiple providers (Alibaba Cloud Bailian/ModelStudio, ModelScope, OpenRouter) for easy routing.

Verdict: If you want a cohesive “CLI ↔ VS Code ↔ GitHub” story with official support, Gemini is ahead today. Qwen Code is catching up fast and benefits from a vibrant open-source Qwen ecosystem, especially for local or hybrid setups.

Qwen Code CLI vs Gemini CLI: Practical examples

Below are side-by-side tasks and how you might tackle them with each CLI.

1) Summarize the architecture of the current repo

Qwen Code

cd my-service
qwen
> Describe the main architectural components of this codebase.
> List all API endpoints and their auth modes.

This uses Qwen’s code-understanding prompts from the README. Use /stats to watch token usage; /compress if you’re approaching session limits.

Gemini

cd my-service
gemini
> Summarize the architecture and generate a diagram-friendly outline.
> Ground with web fetches only if you need library docs.

Gemini can checkpoint the session and use Search grounding for library APIs you reference.

2) Refactor a function and add tests

Qwen Code

qwen
> Refactor src/users/updateProfile.ts for readability and performance.
> Generate Jest tests with edge cases; place them in __tests__/updateProfile.test.ts.

Qwen Code’s parser tweaks help it edit code more reliably with Qwen-Coder.

Gemini

gemini
> Improve src/users/updateProfile.ts to remove duplication and add input validation.
> Create unit tests and run `npm test`. If failures, fix them.

Gemini’s agent can run shell commands and iterate using ReAct, including invoking npm test and editing files.

3) Automate release notes from recent commits

Qwen Code

qwen
> Analyze git commits from the last 7 days, grouped by feature.
> Produce a CHANGELOG.md entry following Keep a Changelog format.

This workflow is highlighted in the Qwen README’s “Automate Workflows” section.

Gemini

gemini -p “Create release notes from commits since v2.3.0; group by scope, link PRs, and summarize breaking changes.”

Combine with GitHub Action to run on every release for repeatability.

4) Cross-repository triage and GitHub ops

Gemini

  • Install the Gemini CLI GitHub Action and mention @gemini-cli on issues for prioritization, or auto-review PRs with quality signals. This is an out-of-the-box productivity win for large orgs.

Qwen Code

  • Use terminal workflows today; GitHub Action parity will depend on community scripts and your CI tooling.

5) Huge context workflows

Gemini

  • If you routinely pass hundreds of thousands of tokens (e.g., monorepos or research dossiers), the 1M-token context on Gemini 2.5 Pro is a differentiator.

Qwen Code

  • You can manage session token limits (e.g., ~/.qwen/settings.json) and compress history, but absolute upper bounds depend on your chosen Qwen model/provider.

Qwen Code CLI vs Gemini CLI: Extensibility and MCP

Both support MCP to integrate external tools and servers, but Gemini has the more mature documentation and examples today. You can point Gemini CLI at GitHub/Slack/database MCP servers and then issue “@server” commands right in the chat. Qwen Code inherits the concept (it’s adapted from Gemini CLI) and focuses its innovation on parser/model alignment with Qwen-Coder.

Qwen Code CLI vs Gemini CLI: Developer experience

  • Prompt ergonomics: Both CLIs support a natural chat loop with slash commands for help, stats, history compression, and tool inspection. Gemini’s checkpointing and GEMINI.md file for persistent context are particularly helpful for multi-day tasks. Qwen’s sessionTokenLimit and /compress keep costs in check and are easy to reason about.
  • Speed and stability: This is model/provider dependent. Gemini CLI’s default path to Gemini 2.5 Pro is consistently strong and benefits from Google’s infra. Qwen Code’s performance will vary with your chosen endpoint (Qwen OAuth, OpenRouter, Bailian/ModelScope, local vLLM with Qwen2.5-Coder, etc.).
  • Multimodality: Gemini CLI leans into image/video generation through MCP integrations (Imagen, Veo, Lyria). Qwen Code focuses on code workflows first. If you value “generate an app from a sketch/PDF” workflows, Gemini currently markets that path more clearly.

Qwen Code CLI vs Gemini CLI: Where each one shines

Pick Qwen Code CLI if…

  • You want the most generous free individual quota today (2,000 requests/day), and you don’t want to think about tokens.
  • You already prefer Qwen-Coder models (open weights or hosted) and want a parser tuned for them.
  • You’re comfortable stitching editor integrations yourself or you favor a pure-terminal workflow.

Pick Gemini CLI if…

  • You’re embedded in the Google ecosystem (VS Code Gemini Code Assist, Search grounding, Vertex AI, GitHub Actions).
  • Your projects need massive context windows or frequent web grounding.
  • You want a clearly documented MCP and roadmap from a large vendor.

Qwen Code CLI vs Gemini CLI – try these prompts

1. Given this Python function that calculates the nth Fibonacci number using recursion, rewrite it using memoization and explain the time complexity improvement.
Try with Qwen Code CLI Try with Gemini 2.5 Pro
2. A train leaves City A at 60 km/h and another leaves City B (300 km away) at 40 km/h at the same time heading toward each other; calculate when and where they meet.
Try with Qwen Code CLI Try with Gemini 2.5 Pro
3. Create a RESTful API in Node.js using Express that allows users to register, log in, and retrieve their profile data securely with JWT authentication.
Try with Qwen Code CLI Try with Gemini 2.5 Pro
4. Given a CSV of daily stock prices, write a Python script using Pandas and Matplotlib to calculate and plot the 7-day moving average, then highlight the days with the highest trading volume.
Try with Qwen Code CLI Try with Gemini 2.5 Pro
5. Explain how a hash table works internally and describe a scenario where using a hash table would be a poor choice compared to a binary search tree.
Try with Qwen Code CLI Try with Gemini 2.5 Pro
6. Write a Python function that takes a paragraph of text, extracts all named entities using spaCy, and stores them in a normalized SQL database schema.
Try with Qwen Code CLI Try with Gemini 2.5 Pro

The Bottom Line

Both Qwen code CLI and Gemini CLI are optimized for different centers of gravity. Choose Qwen Code CLI for generous free usage and an optimized toolchain for Qwen-Coder models, ideal for local Qwen2.5-Coder experimentation and mixing providers like OpenRouter/ModelScope. Opt for Gemini CLI for deep ecosystem integration (Search, VS Code, Vertex, GitHub Actions), large context windows, and strong documentation. It’s perfect for large teams on Google Cloud or individuals needing web-based research in the terminal. 

But what’s better than both is a platform that offers integrations like GitHub, multiple models, from Claude 4, GPTs, Gemini 2.5 Pro, DeepSeek R1, and beyond, and a built-in IDE with direct deployment. If that’s what you need, look no further than Bind AI.