How to Run Claude Code for Free: Tested Methods and Best Setup

Claude Code isn’t the chatbot you access in your browser—it’s an AI agent that lives in your terminal. Unlike IDE plugins such as Cursor or Copilot, it operates entirely through the command line: reading and writing files directly, executing shell commands, and completing multi-step tasks without manual intervention. You don’t switch between tabs or copy-paste code. You type a single command, and it acts.

This makes Claude Code uniquely powerful for developers who want automation without a graphical interface. But there’s a catch: Anthropic’s official tool is not free. That’s why we tested six different methods to run it at no cost—and only one delivered consistent results.

Why Look for Free Alternatives to Anthropic’s Claude Code?

Our goal was to complete the free "Claude Code in Action" course on Skilljar, which requires using the terminal agent in real-world scenarios. While the course is free, the tool isn’t. So we searched for ways to access Claude Code without paying for Anthropic’s API. We tested six setups—some widely recommended online—and only one worked reliably.

Here’s what we found, and what failed.

Tested Methods That Didn’t Work

OpenRouter Direct Integration

We tried pointing the official Claude Code client to the OpenRouter API using a ~/.claude/settings.json configuration. It worked—for about 10 minutes—before returning an error 429: request limit exceeded. OpenRouter’s free tier allows only 50 requests per day, and we hit the ceiling quickly.

Claude Code Router (CCR) Proxy

CCR is designed to re-route requests from the official Anthropic client to third-party models like those on OpenRouter, NVIDIA NIM, and DeepSeek. Despite hours of configuration, all attempts failed with the error "Missing model in request body".

This error occurs because CCR acts as a translator between the two systems. When Claude Code sends a request, CCR must extract the model name and inject it into the provider’s request body. Without the correct model field, the provider cannot respond. In our tests, this translation process consistently broke down.

Free-Claude-Code + NVIDIA NIM

After setting up a free NVIDIA API key, we attempted to use the free-claude-code proxy to connect to NVIDIA’s models. The response was a 404 page not found, indicating restricted access. Further research showed that free NVIDIA accounts require manual approval to access public APIs—something not clearly documented or easy to obtain.

Free-Claude-Code + OpenRouter (Proxy Attempt)

Switching the proxy to target OpenRouter’s free models (Qwen, Gemma, DeepSeek R1), we received a 200 OK response. But the Claude Code client immediately returned "Provider API request failed". The issue was with tool handling: the first request worked, but the second—when returning tool output—failed. The root cause was the loss of reasoning_details (encrypted tokens) between streaming responses. OpenRouter doesn’t preserve these tokens, which are required by models like Gemini. Without them, the second request fails with an HTTP 400 error.

Ollama with Local Model

Running ollama launch claude seemed promising—local execution, no external API needed. But when we asked a simple question, the response took over five minutes. For more complex tasks, performance would likely be even worse. Without a dedicated GPU, local models are impractical for real-time development.

The One Reliable Free Method: cc-gemini

After eliminating the unreliable options, we found cc-gemini, a GitHub project that acts as a live translation layer between the official Claude Code client and Google’s Gemini API. It enables the terminal agent to communicate with free Gemini models via a local proxy.

To set it up:

npm install -g claude-code-gemini cc-gemini

Then, generate a free API key from Google AI Studio and run the proxy:

cc-gemini

The proxy starts on port 8100. You can then run the official claude command in your terminal, and it will route requests through your local proxy to the free Gemini model.

Performance: What Works and What Doesn’t

Simple tasks—like listing files, explaining code snippets, or renaming functions—perform well with cc-gemini. Responses arrive in about 10 to 15 seconds.

But complex workflows, such as initializing a new project with /init in a repository of over 500 lines, take significantly longer. In our test, Gemini analyzed the codebase and generated the CLAUDE.md file in approximately five minutes. By comparison, the paid Claude Code with Anthropic’s model completed the same task in just 1 minute and 34 seconds.

While five minutes may still be acceptable for first-time analysis, it highlights a trade-off: free access comes with slower inference, especially on larger contexts.

Why Is Gemini Slower Than Anthropic’s Model?

The primary reason is model architecture and context handling. Anthropic’s Claude models are optimized for tool use and long-form reasoning within the official agent framework. Google’s Gemini, while capable, processes streaming responses differently and lacks native support for the tool execution patterns used by the Claude Code client. This results in additional overhead during parsing and state management.

Is It Worth It?

If your goal is to learn the fundamentals of the Claude Code ecosystem or perform lightweight automation without cost, cc-gemini is a viable solution. It enables access to the same agent-driven workflows—just with a free external model and a local proxy.

However, if you need fast, reliable performance for production use or large-scale development tasks, the free route still falls short. In those cases, investing in Anthropic’s official API or exploring enterprise-grade alternatives remains the better long-term choice.

The search for a completely free, high-performance Claude Code alternative continues—but for now, cc-gemini is the closest practical option available.