> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/cheahjs/free-llm-api-resources/llms.txt
> Use this file to discover all available pages before exploring further.

# Cloudflare Workers AI

> Free access to AI models on Cloudflare's global network

<Note>
  Cloudflare Workers AI provides free access to various open-source models running on Cloudflare's edge network.
</Note>

## Overview

Cloudflare Workers AI offers serverless inference for multiple open-source models, running on Cloudflare's global network for low latency worldwide.

## Rate Limits

<Info>
  **Free Allocation**: 10,000 neurons per day
</Info>

[View detailed pricing](https://developers.cloudflare.com/workers-ai/platform/pricing/#free-allocation)

<Warning>
  Neurons are Cloudflare's unit of measurement for AI workloads. Different models and operations consume different amounts of neurons.
</Warning>

## Available Models

Cloudflare Workers AI offers a wide variety of models:

### Featured Models

* **@cf/openai/gpt-oss-120b** - Open-source GPT model
* **@cf/qwen/qwen3-30b-a3b-fp8** - Qwen 3 model
* **Llama 4 Scout Instruct** - Latest Llama model
* **Llama 3.3 70B Instruct (FP8)** - Optimized Llama 3.3
* **Gemma 3 12B Instruct** - Google's Gemma model
* **Mistral Small 3.1 24B Instruct** - Mistral's efficient model

### DeepSeek Models

* DeepSeek R1 Distill Qwen 32B
* Deepseek Coder 6.7B Base (AWQ)
* Deepseek Coder 6.7B Instruct (AWQ)
* Deepseek Math 7B Instruct

### Llama Models

* Llama 2 7B Chat (FP16, INT8, LoRA)
* Llama 2 13B Chat (AWQ)
* Llama 3 8B Instruct (AWQ)
* Llama 3.1 8B Instruct (AWQ, FP8)
* Llama 3.2 1B, 3B, 11B Vision Instruct
* Llama 3.3 70B Instruct (FP8)
* Llama 4 Scout Instruct
* Llama Guard 3 8B

### Mistral Models

* Mistral 7B Instruct v0.1 (AWQ)
* Mistral 7B Instruct v0.2 (LoRA)
* Mistral Small 3.1 24B Instruct
* Hermes 2 Pro Mistral 7B

### Qwen Models

* Qwen 1.5 (0.5B, 1.8B, 7B, 14B)
* Qwen 2.5 Coder 32B Instruct
* Qwen QwQ 32B

### Gemma Models

* Gemma 2B Instruct (LoRA)
* Gemma 3 12B Instruct
* Gemma 7B Instruct (LoRA)

### Other Models

* @cf/aisingapore/gemma-sea-lion-v4-27b-it
* @cf/ibm-granite/granite-4.0-h-micro
* @cf/zai-org/glm-4.7-flash
* Discolm German 7B v1 (AWQ)
* Falcom 7B Instruct
* Neural Chat 7B v3.1 (AWQ)
* OpenChat 3.5 0106
* OpenHermes 2.5 Mistral 7B (AWQ)
* Phi-2
* SQLCoder 7B 2
* Starling LM 7B Beta
* TinyLlama 1.1B Chat v1.0
* Una Cybertron 7B v2 (BF16)
* Zephyr 7B Beta (AWQ)

## API Usage

<CodeGroup>
  ```python Python theme={null}
  import requests

  url = "https://api.cloudflare.com/client/v4/accounts/YOUR_ACCOUNT_ID/ai/run/@cf/meta/llama-3.3-70b-instruct-fp8"

  headers = {
      "Authorization": "Bearer YOUR_CLOUDFLARE_API_TOKEN",
      "Content-Type": "application/json"
  }

  data = {
      "messages": [
          {"role": "user", "content": "Hello, how are you?"}
      ]
  }

  response = requests.post(url, headers=headers, json=data)
  print(response.json())
  ```

  ```javascript JavaScript theme={null}
  const response = await fetch(
      'https://api.cloudflare.com/client/v4/accounts/YOUR_ACCOUNT_ID/ai/run/@cf/meta/llama-3.3-70b-instruct-fp8',
      {
          method: 'POST',
          headers: {
              'Authorization': 'Bearer YOUR_CLOUDFLARE_API_TOKEN',
              'Content-Type': 'application/json'
          },
          body: JSON.stringify({
              messages: [
                  { role: 'user', content: 'Hello, how are you?' }
              ]
          })
      }
  );

  const result = await response.json();
  console.log(result);
  ```

  ```bash cURL theme={null}
  curl https://api.cloudflare.com/client/v4/accounts/YOUR_ACCOUNT_ID/ai/run/@cf/meta/llama-3.3-70b-instruct-fp8 \
    -H "Authorization: Bearer YOUR_CLOUDFLARE_API_TOKEN" \
    -H "Content-Type: application/json" \
    -d '{
      "messages": [
        {"role": "user", "content": "Hello, how are you?"}
      ]
    }'
  ```
</CodeGroup>

## Getting Started

<Steps>
  <Step title="Create Cloudflare Account">
    Sign up at [cloudflare.com](https://cloudflare.com)
  </Step>

  <Step title="Get API Token">
    Create an API token with Workers AI permissions
  </Step>

  <Step title="Find Account ID">
    Get your account ID from the dashboard
  </Step>

  <Step title="Start Building">
    Use the REST API to run inference
  </Step>
</Steps>

## Key Features

<CardGroup cols={2}>
  <Card title="Global Network" icon="globe">
    Run inference on Cloudflare's edge network
  </Card>

  <Card title="Low Latency" icon="bolt">
    Models run close to your users
  </Card>

  <Card title="Multiple Models" icon="layer-group">
    50+ open-source models available
  </Card>

  <Card title="Optimized Variants" icon="gauge">
    AWQ, LoRA, FP8, INT8 quantized models
  </Card>

  <Card title="Serverless" icon="server">
    No infrastructure to manage
  </Card>

  <Card title="Pay As You Go" icon="dollar-sign">
    Free tier with neurons-based pricing
  </Card>
</CardGroup>

## Model Optimizations

<Info>
  Cloudflare offers various optimized versions:

  * **AWQ**: Activation-aware Weight Quantization (4-bit)
  * **FP8**: 8-bit floating point
  * **INT8**: 8-bit integer quantization
  * **LoRA**: Low-Rank Adaptation for fine-tuning
</Info>

## Use Cases

* **Edge AI**: Run AI close to your users
* **Chatbots**: Build conversational interfaces
* **Content Generation**: Generate text content
* **Code Assistance**: Code completion and generation
* **Translation**: Multilingual applications
* **Global Applications**: Low-latency worldwide

## Additional Resources

<CardGroup cols={2}>
  <Card title="Cloudflare Dashboard" icon="browser" href="https://dash.cloudflare.com">
    Manage your account
  </Card>

  <Card title="Documentation" icon="book" href="https://developers.cloudflare.com/workers-ai">
    Official documentation
  </Card>

  <Card title="Model Catalog" icon="database" href="https://developers.cloudflare.com/workers-ai/models">
    Browse all models
  </Card>

  <Card title="Pricing" icon="dollar-sign" href="https://developers.cloudflare.com/workers-ai/platform/pricing">
    Pricing details
  </Card>
</CardGroup>