How to Use Claude Code 100% Free
Claude Code is one of the most powerful AI coding tools available, but the paid plan is a barrier for many. There's a way to use it completely free by connecting it to OpenRouter's free models. With the right configuration, the right project structure, and the right prompting strategy, you can build real, scalable applications without spending anything. This guide covers the setup, the three critical principles for building real projects with free models, and the exact workflow used to build a full application in about an hour.
Step 1: Configure Claude Code to Use OpenRouter
The setup takes five minutes and requires nothing more than a free OpenRouter account and a configuration file in your project.
Create the configuration directory. In your project folder (VS Code or any editor), create a new folder called .claude. Inside it, create a file called settings.json.local. This file controls Claude Code's behavior locally for this specific project.
Get an API key from OpenRouter. Go to openrouter.ai and create a free account. Navigate to Personnel → Settings → API Keys, create a new key (name it whatever you want, like "claude-code-free"), and copy it.
Configure the settings file. Paste the API key into the settings.json.local file as the authentication token. Add a free model—OpenRouter provides many. Search "free" in the models directory. NVIDIA's free model works well for coding. MiniMax M2.5 is another strong option. Copy the model identifier and paste it into your configuration. Set the base URL to https://openrouter.ai/api/v1.
Verify the setup. Open a terminal in your project and run Claude. Check the available models—your free model should appear. Select it. Send a test message. Go back to OpenRouter, check the activity logs, and confirm the request went through with a cost of $0.00.
The configuration file is ready to copy directly. The full setup is available in the description.
Tip 1: Keep Your Application Modular
This is the most important principle for building real applications with AI. The source code of a production website reveals the pattern: small, focused files, each doing one thing. Auth in one file. Database queries in another. UI components separated.
The reason is the context window. AI models have limited memory for each session. If you dump everything into one giant file, the AI loses track. It starts hallucinating. It breaks things you already fixed. Keeping files under approximately 600 lines of code means the AI can understand each file completely, connect them logically, and scale the application without introducing regressions.
Add this rule to your prompts or, better, to your Claude MD file so you never have to repeat it: "Keep files small and focused. Each file does one thing. Maximum 600 lines per file."
Tip 2: Understand the Building Blocks Before Prompting
AI will not protect you from problems you don't know exist. If you're building an application where multiple users can update the same data simultaneously and you don't know what a race condition is, you won't think to mention it in your prompt. The application will work perfectly in testing with one user and break the moment multiple users click save at the same time.
Your job is not to write every line of code. Your job is to know what to ask for. Before building any feature, understand the underlying concepts. What are the failure modes? What edge cases exist? What security considerations apply? The AI implements what you describe. If your description is incomplete because your understanding is incomplete, the implementation will be incomplete.
This is the difference between blind prompting and engineering. The free setup removes the financial barrier. The knowledge of what to ask for removes the quality barrier.
Tip 3: One Task Per Session, Test, Then Push to GitHub
Before writing a single prompt for the CardioFlow application—a full cardio workout generator—a plan file was created. Every feature broken into small, discrete tasks. Each task becomes one Claude Code session.
The workflow is rigid: complete one task, test it manually, verify everything works, push to GitHub. Then move to the next task. If the AI breaks something later—and it will, especially with free or smaller models—you can revert to any previous working state. Every step is backed up. No progress is lost.
This is particularly important with the free models available through OpenRouter. They are powerful but less consistent than the paid Claude models. The session-per-task discipline with Git checkpoints turns that inconsistency from a fatal problem into a minor inconvenience. If a session goes wrong, you reset and try again from the last working commit.
Building CardioFlow: The Complete Workflow
The CardioFlow application was built in about an hour using this exact method. Free model through OpenRouter. Modular file structure. One feature per session. Git commits between each session. The five prompts used to build it—from initial scaffolding to final deployment—are available in the description along with the configuration file, so you can replicate the process immediately.
