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Robert Adamson
Robert Adamson

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How to Use Claude Code for Free in 2026: OpenRouter Free Models, Ollama & Free GPU Credits

#ai #opensource #programming #tutorial

How to Use Claude Code for Free in 2026: OpenRouter Free Models, Ollama & Free GPU Credits

Claude Code has become one of the most useful AI coding tools for developers.

It can explore an existing repository, understand multiple files, edit code, run terminal commands, debug errors, refactor projects, and work through development tasks more like an agent than a normal chatbot.

But there is one problem:

Using Claude Code with Anthropic's Claude models normally costs money.

What many developers don't realize is that, in 2026, the Claude Code interface and the AI model behind it do not necessarily have to be the same thing.

You can use Claude Code as the coding-agent interface while sending its requests to:

  • free models on OpenRouter
  • open models running locally with Ollama
  • models running on your own GPU
  • cloud GPU infrastructure funded by free credits

That creates several interesting ways to experiment with Claude Code without continuously paying for Anthropic API usage.

But there is a lot of misinformation around this topic, so let's separate what is actually free from what only sounds free.

First: Understand What Claude Code Actually Is

There are two separate pieces:

Claude Code
    ↓
Coding agent / terminal interface
    ↓
AI model
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Claude Code handles things such as:

Reading files
Editing files
Running commands
Searching your repository
Maintaining context
Calling tools
Planning coding tasks
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The model provides the intelligence.

Normally the architecture looks like this:

Claude Code
      ↓
Anthropic API
      ↓
Claude Sonnet / Claude Opus
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Anthropic's normal Claude Code setup supports Anthropic Console billing, Claude subscription plans, and enterprise providers such as Amazon Bedrock and Google Vertex AI.

But Claude Code can also communicate through LLM gateways. Anthropic documents ANTHROPIC_BASE_URL specifically for gateway configurations.

And in 2026, tools such as Ollama and OpenRouter have built compatibility around this capability.

That gives us several alternatives.

Option 1: Claude Code + OpenRouter Free Models

Probably the easiest cloud-based option is:

Claude Code
      ↓
OpenRouter
      ↓
Free open model
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OpenRouter provides one API through which developers can access models from many different providers.

More importantly for this tutorial, it currently has free model endpoints.

As of August 2026, OpenRouter's Free plan lists more than 25 free models and allows up to 50 requests per day for free accounts.

OpenRouter also provides a special router:

openrouter/free
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It automatically chooses an available free model that supports the capabilities required by the request.

So instead of:

Claude Code → Claude Sonnet
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you could run something closer to:

Claude Code
      ↓
OpenRouter
      ↓
Free coding-capable model
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Step 1: Install Claude Code

Anthropic currently provides native installers.

macOS / Linux / WSL 

curl -fsSL https://claude.ai/install.sh | bash
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Then verify it:

claude --version
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You can also run:

claude doctor
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to inspect your installation.

Step 2: Create an OpenRouter Account

Create an OpenRouter account and generate an API key.

Your key will look roughly like:

sk-or-v1-...
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Never commit this key to GitHub.

Instead, keep it in an environment variable.

Step 3: Connect OpenRouter to Claude Code

This part used to require several manual environment variables.

In August 2026, OpenRouter introduced Ori, a CLI designed specifically to configure coding harnesses including Claude Code, Codex, OpenCode, and Hermes.

Install it:

curl -fsSL https://openrouter.ai/labs/ori/install.sh | bash
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Then authenticate:

ori login
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And start Claude Code through OpenRouter:

ori claude
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OpenRouter handles much of the gateway configuration automatically.

That is much cleaner than maintaining a collection of environment variables manually.

Step 4: Choose a Free Model

OpenRouter models with:

:free
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in their model ID are free variants.

The exact list changes over time, so do not hard-code a 2026 blog post around one model being permanently free.

Instead, check:

OpenRouter → Models → Free
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before choosing one.

There is also:

openrouter/free
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which automatically routes your request to a currently available free model.

At the time of writing, OpenRouter has coding/reasoning-capable free models available, but that list can change at any time.

This distinction matters.

A model being free today does not guarantee that its provider will continue supplying free inference indefinitely.

OpenRouter's Free Limits

Free does not mean unlimited.

A normal free OpenRouter account currently gets approximately:

50 requests/day
20 requests/minute
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for free models.

OpenRouter says accounts that have purchased at least $10 in credits can receive a higher free-model daily limit, currently up to 1,000 requests/day.

So OpenRouter's free tier is excellent for:

Learning Claude Code
Small repositories
Bug fixing
Code explanation
Occasional refactoring
Experiments
Personal projects
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It is probably not enough for:

8-hour autonomous coding sessions
Large commercial repositories
Heavy agent loops
Production-scale automation
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Agents can make many model requests while completing what looks like one task.

That makes rate limits especially important.

Option 2: Claude Code + Ollama = Local AI Coding

Now we get to the option I find more interesting.

Instead of using somebody else's API:

Claude Code
      ↓
Internet API
      ↓
Model
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you can run:

Claude Code
      ↓
Ollama
      ↓
Model on your computer
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There are no per-token API charges.

You are using your own hardware.

And this is no longer an unofficial hack.

In January 2026, Ollama added Anthropic Messages API compatibility, specifically enabling tools such as Claude Code to use Ollama models.

Step 1: Install Ollama

Install Ollama for your operating system.

Then check:

ollama --version
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Step 2: Download a Coding Model

For example:

ollama pull gpt-oss:20b
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Other coding-focused models can also be used.

Ollama currently recommends models including:

gpt-oss:20b
qwen3-coder
glm-4.7-flash
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depending on the available hardware and version.

Remember that model recommendations change quickly.

Before downloading a huge model, check:

RAM
VRAM
model size
context requirement
quantization
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on your machine.

Step 3: The Easy 2026 Method

Ollama introduced an even easier command:

ollama launch claude
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Ollama can now configure and launch Claude Code directly.

It lets you choose a model and handles the required connection settings.

So your workflow becomes:

ollama launch claude
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Select your model.

Then Claude Code runs with that model.

That's it.

The Manual Ollama Method

Understanding what happens underneath is still useful.

Ollama exposes an Anthropic-compatible API.

You can configure Claude Code with:

export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_BASE_URL=http://localhost:11434
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Then run:

claude --model gpt-oss:20b
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Ollama officially documents this configuration.

Now requests follow this path:

Claude Code
       │
       ▼
localhost:11434
       │
       ▼
Ollama
       │
       ▼
gpt-oss / Qwen / another model
       │
       ▼
Your GPU / CPU
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No Anthropic inference API is involved in that model request.

Is Local Ollama Really Free?

From an API billing perspective:

Yes.

You aren't paying a company per million tokens.

But technically nothing involving computers is completely free.

You still have:

Hardware cost
Electricity
Storage
GPU memory limitations
Your own machine's compute time
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So the accurate description is:

Local inference has no per-token API fee.

Not:

AI inference costs absolutely nothing.

That difference matters.

Hardware Is the Biggest Limitation

Claude Code can create large prompts.

It may need:

repository context
system instructions
tool definitions
conversation history
code
command outputs
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That means tiny local models and tiny context windows can struggle.

Ollama recommends relatively large context lengths for coding-agent workflows; its newer launch documentation recommends configuring around 64K context when practical.

Running a model is one thing.

Running a model effectively as a coding agent is another.

A laptop with 8 GB RAM is not going to behave like a cloud server with an H100.

Option 3: Don't Have a GPU? Use Free GPU Programs

This is where things become more interesting.

Suppose your laptop cannot comfortably run a coding model.

You could theoretically do this:

Laptop
   ↓
Claude Code
   ↓
Your model API
   ↓
Cloud GPU
   ↓
Open model
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There are several ways to get low-cost or free GPU access.

But this is also where many articles become misleading.

Let's clarify the major options.

Kaggle: Up to 30 GPU Hours Per Week

Kaggle currently provides free GPU access for notebooks.

Its documentation states that GPU usage has a weekly quota of approximately:

30 GPU hours/week
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and sometimes more depending on available resources and demand.

That sounds incredible.

And it is useful.

You can use Kaggle GPUs to:

experiment with open models
fine-tune models
test quantization
benchmark inference
learn PyTorch
run ML experiments
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But there is an important limitation.

Kaggle Is Not Your Free Production AI Server

Kaggle Notebook sessions are temporary compute environments.

Do not design a production architecture that assumes:

Claude Code
     ↓
Permanent Kaggle API
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will remain online indefinitely.

That is not what Kaggle's free GPU notebooks are designed for.

Use them for experimentation and temporary compute, not as a permanent production inference endpoint.

This is one of the biggest pieces of misinformation I see in "free GPU" tutorials.

Modal: A More Realistic Deployment Option

A different option is Modal.

Modal is serverless infrastructure designed for AI workloads.

Its Starter plan currently includes:

$30/month free compute credit
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with access to GPU workloads.

Unlike a notebook platform, Modal is specifically designed to deploy functions, containers, inference services, and AI applications.

Conceptually, you could build:

Claude Code
      ↓
Your API
      ↓
Modal endpoint
      ↓
vLLM / model server
      ↓
Open model
      ↓
GPU
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Modal even maintains examples and a model library for deploying open models.

The $30 credit doesn't mean every GPU suddenly becomes unlimited.

GPU usage consumes those credits based on the GPU and runtime.

But for experimentation, small applications, and learning how inference infrastructure works, this can be extremely useful.

Kaggle vs Modal

Think about them differently:

Service Best Use
OpenRouter Ready-made free model API
Ollama Local inference
Kaggle Temporary free GPU experiments
Modal Deployable GPU compute using monthly credit
Anthropic API First-party Claude models

That distinction prevents a lot of confusion.

What About Google Colab?

Google Colab also provides free GPU access.

However, Google does not guarantee a fixed GPU type, fixed availability, or a simple permanent number of free GPU hours for every free user.

Availability depends on capacity and usage.

So I would not write:

"Google gives everyone X free GPU hours."

unless Google explicitly publishes that guarantee.

Colab describes its service as providing free GPU access, but availability is dynamic.

Use Colab for experiments.

Don't treat it as guaranteed production infrastructure.

The Four Architectures You Can Use

Let's put everything together.

Architecture 1 — Normal Claude Code 

Developer
    │
    ▼
Claude Code
    │
    ▼
Anthropic API
    │
    ▼
Claude
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Cost

Paid Anthropic usage/subscription depending on your setup.

Advantage

Best integration with Claude Code.

Architecture 2 — OpenRouter Free Model 

Developer
    │
    ▼
Claude Code
    │
    ▼
OpenRouter
    │
    ▼
Free Model
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Cost

$0 within the free model limits.

Best for

Beginners who don't own a powerful GPU.

Architecture 3 — Ollama Local Model 

Developer
    │
    ▼
Claude Code
    │
    ▼
Ollama
    │
    ▼
Qwen / GPT-OSS / GLM / etc.
    │
    ▼
Local GPU
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API cost

$0.

Best for

Developers with enough RAM/VRAM who want privacy and control.

Architecture 4 — Your Own Cloud Model 

Developer
     │
     ▼
Claude Code
     │
     ▼
Gateway / API
     │
     ▼
Your model server
     │
     ▼
Open model
     │
     ▼
Cloud GPU
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The model server might use:

vLLM
SGLang
llama.cpp
Ollama
another inference engine
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and the compute could come from a normal GPU provider or promotional/free credits.

This is the most technically interesting route because you control the entire stack.

But it also requires more engineering.

Which Method Should You Choose?

If you just want to learn Claude Code:

Claude Code + OpenRouter
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is probably the easiest place to start.

If you have a capable GPU:

Claude Code + Ollama
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is arguably the best no-per-token-cost setup.

If you're learning ML infrastructure:

Kaggle
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gives you valuable free GPU experimentation time.

If you want to experiment with actually deploying model inference:

Modal
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is a more appropriate platform because its free allowance is compute credit for infrastructure rather than temporary notebook sessions.

A Practical Setup I Would Use

For a developer trying to minimize AI costs in 2026, I would build a three-level setup.

Level 1 — Local

Use:

Claude Code
+
Ollama
+
local coding model
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for everyday tasks.

Things like:

Generate tests
Explain code
Small refactors
Write documentation
Search repositories
Fix simple bugs
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don't necessarily require the most expensive frontier model.

Level 2 — Free Cloud Models

When the local model struggles:

Claude Code
+
OpenRouter
+
free model
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gives you access to larger hosted models without needing more GPU hardware.

Stay within the free quotas.

Level 3 — Frontier Models

For extremely difficult tasks:

Complex architecture decisions
Hard debugging
Large migrations
Security-sensitive review
Deep multi-file reasoning
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pay for a strong frontier model when necessary.

This can be much cheaper than sending every coding request to the most expensive model.

Free Doesn't Mean Equal to Claude

There is another point worth discussing.

Running an open model inside Claude Code does not magically turn that model into Claude Sonnet or Claude Opus.

Claude Code gives the model a powerful environment.

But:

Agent quality
≠
Model quality
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The same coding interface can behave very differently depending on the model behind it.

A model may struggle with:

tool calling
long-context reasoning
instruction following
multi-step planning
patch generation
recovering from failed commands
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even though it performs well on ordinary coding benchmarks.

So test models against your real repository, not just benchmark rankings.

Why This Matters in 2026

The interesting shift isn't simply:

AI models are becoming free.

The bigger shift is that the layers are separating.

We now increasingly have:

Interface
        ↓
Agent harness
        ↓
Model gateway
        ↓
Inference provider
        ↓
Model
        ↓
GPU infrastructure
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Each layer can potentially be swapped.

Claude Code is the agent harness.

OpenRouter can become the routing layer.

Ollama can become the local inference layer.

An open model can become the intelligence layer.

And local or cloud GPUs become the compute layer.

Developers therefore have far more control over the cost/performance tradeoff than they had only a few years ago.

Important Things to Avoid

There are several mistakes I would avoid.

Don't say Claude models are free.

They aren't simply made free because you use Claude Code through another backend.

Don't expose API keys.

Never write:

const key = "sk-or-v1-my-real-key";
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inside a repository.

Use environment variables or a secret manager.

Don't assume free models stay free forever.

OpenRouter's available free models change.

Always check the current catalog.

Don't treat Kaggle like permanent hosting.

Its GPU quota is extremely useful for experimentation, but notebook compute and production inference hosting are different things.

Don't expect a 7B local model to behave like a frontier model.

Claude Code provides the tools. The underlying model still determines much of the intelligence.

The Simplest Free Setup

If you want the shortest possible path:

Local

Install Ollama.

Then:

ollama pull gpt-oss:20b
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and:

ollama launch claude
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You now have:

Claude Code interface
+
local open model
+
no per-token API bill
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For a cloud option:

ori login
ori claude
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and select an appropriate OpenRouter free model.

Final Thoughts

So, can you use Claude Code for free in 2026?

Yes—but the wording matters.

You are not getting unlimited Claude Sonnet or Claude Opus inference for free.

Instead, you can use the Claude Code coding-agent interface with alternative model backends.

The most realistic options today are:

Claude Code + OpenRouter free models
Claude Code + Ollama local models
Claude Code + your own open-model endpoint
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And if you don't own a powerful GPU, free compute programs can help you experiment.

Kaggle currently provides roughly 30 GPU hours per week for notebook workloads, while infrastructure platforms such as Modal currently provide $30/month of free compute credit that can be used toward deployable workloads.

The deeper lesson is bigger than Claude Code.

The future of AI development probably won't be:

one coding tool
+
one proprietary model
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It increasingly looks like:

your favorite agent
+
your preferred model
+
your preferred inference provider
+
your preferred compute
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And that is a much more interesting world for developers.

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