How to Use Claude Code for Free with OmniRoute

Claude Code is one of my favorite AI coding agents.

It can inspect your codebase, edit files, execute terminal commands, use tools, and work through multi-step coding tasks directly from the terminal.

But normally, the intelligence behind that experience comes from Anthropic’s Claude models.

What if we kept Claude Code as the coding agent harness, but replaced the model underneath it?

That’s what I wanted to test.

The result looks like this:

Claude Code
     ↓
 OmniRoute
     ↓
Free AI Models
     ↓
OpenRouter / NVIDIA / Other Providers

Instead of sending model requests directly to Anthropic, Claude Code sends them to OmniRoute, which acts as an AI gateway and routes them to the models and providers you configure.

And yes — that means you can run Claude Code using free AI models. 🆓

But there’s an important distinction.

We are NOT getting Claude Sonnet or Claude Opus for free.

We’re using Claude Code as the agent harness while replacing the underlying model with free models from other providers.

Let’s set it up.

🎥 Full video walkthrough

I recorded the entire process step-by-step, including:

✅ Installing OmniRoute locally
✅ Connecting OpenRouter
✅ Connecting NVIDIA
✅ Importing free AI models
✅ Configuring Claude Code
✅ Fixing the authentication issue I encountered
✅ Launching Claude Code through OmniRoute
✅ Running Claude Code with free models

🧠 Claude Code Is More Than the Model

An important idea behind this experiment is separating the agent harness from the LLM powering it.

Claude Code provides the agent experience around the model.

It can:

• inspect files
• understand your project
• modify code
• execute terminal commands
• use tools
• iterate through coding tasks

Normally, Claude Code communicates with Anthropic’s models.

But if we place an AI gateway between Claude Code and the model, the architecture becomes much more flexible.

┌─────────────────┐
│   Claude Code   │
│  Coding Agent   │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│    OmniRoute    │
│   AI Gateway    │
└────────┬────────┘
         │
         ▼
┌─────────────────────────┐
│ OpenRouter │ NVIDIA │ … │
└─────────────────────────┘
         │
         ▼
┌─────────────────┐
│ Free AI Models  │
└─────────────────┘

Claude Code remains the interface and coding agent.

OmniRoute controls where the model requests go.

🔀 That separation is what makes this experiment interesting.

⚙️ Step 1: Install OmniRoute

The first thing we need is OmniRoute.

OmniRoute is an open-source AI gateway that can sit between applications such as Claude Code and different AI model providers.

Install OmniRoute using the installation instructions for your operating system.

Once installed, start it:

omniroute

After a few seconds, the OmniRoute server should start.

Open the dashboard URL shown in your terminal.

You now have a local AI gateway running on your machine.

🔌 Step 2: Connect an AI Provider

Next, we need models.

For my setup, I connected two providers:

• OpenRouter
• NVIDIA

Let’s start with OpenRouter.

Inside the OmniRoute dashboard, navigate to:

Providers → OpenRouter → Add Connection

Generate an API key from your OpenRouter account and add it to the connection.

If you only want free models, enable the option to:

Only import free models

Save the connection.

OmniRoute will import the available free models associated with that provider.

You can also run health checks from the dashboard to see which models are currently available. 🩺

🚀 Step 3: Add NVIDIA Models

I also connected NVIDIA to give OmniRoute another source of models.

The process is similar.

Navigate to:

Providers → NVIDIA → Add Connection

Generate an NVIDIA API key, paste it into OmniRoute, validate it, and save the connection.

Again, I configured the connection to import the free models available through the provider.

Now the architecture starts becoming more interesting:

                    ┌──→ OpenRouter → Free Models
Claude Code → OmniRoute
                    └──→ NVIDIA ───→ Free Models

Instead of Claude Code being tied directly to one model provider, the gateway can expose models from multiple providers.

💻 Step 4: Connect Claude Code to OmniRoute

Now for the important part.

If you don’t already have Claude Code installed, install it using Anthropic’s official installation instructions.

Once Claude Code is available, OmniRoute can configure it automatically.

Run:

omniroute setup-claude

OmniRoute creates profiles that allow Claude Code to communicate through the gateway.

However, I ran into an issue here.

🐛 The Authentication Problem I Encountered

My OmniRoute API was protected with authentication.

Normally, you should be able to create an access token and pass it while configuring Claude Code.

In my testing, however, the API key parameter wasn’t being respected correctly and Claude Code continued returning an authentication error.

So I used a temporary workaround.

I disabled API authentication in OmniRoute.

⚠️ Important Security Warning

I would only consider doing this when OmniRoute is:

• running locally
• bound appropriately
• not exposed publicly
• not accessible from an untrusted network

Do not expose an unauthenticated AI gateway to the internet.

After disabling authentication locally, I ran the Claude setup again and the connection succeeded.

🎯 Step 5: Launch Claude Code with a Free Model

Now we can actually launch Claude Code through OmniRoute.

I used OmniRoute’s coding profile:

omniroute launch --profile auto-coding

The auto-coding profile allows OmniRoute to select among the coding models available in the configuration.

And that’s it.

Claude Code starts normally.

Except now the architecture is:

Claude Code
     ↓
OmniRoute
     ↓
Alternative AI Model

Instead of:

Claude Code
     ↓
Anthropic Model

🎉 Claude Code is now running using the models routed through OmniRoute.

So… Is Claude Code Actually Free Now?

Kind of.

And this distinction is important.

You’re not getting Anthropic’s Claude Sonnet or Opus models for free.

You’re using the Claude Code agent experience with alternative models that may have free access tiers or free routes.

Think of it as separating two pieces:

Claude Code = Agent Harness
Free Model = Intelligence
OmniRoute = Routing Layer

This means the quality of the experience depends heavily on the model you choose.

And that brings us to the trade-offs.

⚡ Trade-Off #1: Free Models Have Limits

Free doesn’t mean unlimited.

Free model providers commonly impose things like:

• request limits
• token quotas
• rate limits
• temporary availability restrictions

A model that works today may also become unavailable or limited later.

For experimentation and personal projects, this can still be extremely useful.

For production workloads, you’ll want something more predictable.

🧩 Trade-Off #2: Not Every Model Works Well with Claude Code

This is probably the biggest issue.

Claude Code isn’t simply sending a prompt and displaying some text.

The model needs to participate in an agentic coding workflow.

That means capabilities such as:

Understand task
      ↓
Inspect files
      ↓
Call tools
      ↓
Modify code
      ↓
Run commands
      ↓
Observe results
      ↓
Continue reasoning

Some models handle these workflows surprisingly well.

Others don’t.

A model might be excellent at generating code but struggle with:

• tool calling
• instruction following
• long coding tasks
• maintaining context
• structured outputs
• multi-step agent workflows

So don’t evaluate a model purely by its coding benchmark score.

Agent compatibility matters too.

🔐 Trade-Off #3: Your Code Goes to the Upstream Provider

This one is important.

When you route Claude Code through another provider, your prompts and potentially parts of your codebase are being sent to that provider.

Before using this setup with private repositories or sensitive code, check the provider’s:

• privacy policy
• data retention policy
• model training policy
• API terms

I would be particularly careful with proprietary company code, credentials, customer information, and production secrets.

📜 Trade-Off #4: Check Provider Terms

You should also check the terms for every provider you connect.

Some providers may have restrictions around:

• automation
• proxies
• redistribution
• free API usage
• rate limits
• agent workloads

A few minutes reading the provider’s terms can save you from account problems later.

💡 Why This Is More Interesting Than Just “Claude Code for Free”

The cost savings are interesting.

But I think the architecture is the bigger story.

We’re increasingly separating the AI application from the AI model.

Instead of:

AI Application
      ↓
One Model
      ↓
One Provider

we can build:

                    ┌──→ Model A
                    │
AI Agent → Gateway ─┼──→ Model B
                    │
                    ├──→ Model C
                    │
                    └──→ Model D

Now the gateway becomes an abstraction layer between the agent and the models.

That opens up some interesting possibilities.

🧠 Use Different Models for Different Coding Tasks

Imagine routing:

Simple code edits
      ↓
Small / Free Model
Complex debugging
      ↓
Powerful Reasoning Model
Documentation
      ↓
Fast Cheap Model
Large Refactor
      ↓
Frontier Coding Model

Instead of asking:

“Which AI model should power my coding agent?”

the better question may eventually become:

“Which model should handle this particular task?”

That’s a very different architecture.

Final Thoughts

Claude Code doesn’t necessarily have to be coupled to a single model provider.

By introducing an AI gateway like OmniRoute, you can separate the coding agent experience from the model powering it.

That gives you room to experiment with:

🔀 multiple providers
🆓 free models
💰 cheaper models
🧠 stronger reasoning models
⚡ faster models
🛠️ models optimized for specific tasks

Free models won’t replace Claude Sonnet or Opus for every coding task.

But that’s not really the point.

The interesting part is being able to choose.

And as AI coding agents become more capable, I suspect the question will increasingly shift from:

“Which coding agent has the best model?”

to:

“Which models should my coding agent use for each task?”