Distillation - Compliance with Provider and Model Creator Policies

Model distillation is the process of training a smaller, more efficient model using outputs from a larger model. While this technique is powerful for creating specialized models, it’s important to respect the terms of service set by model providers and creators. Some model providers and creators explicitly prohibit using their model outputs to train other models, while others allow it. OpenRouter makes it easy to filter for models that permit distillation, helping you stay compliant with these policies.

Why Distillation Compliance Matters

When you use model outputs to train or fine-tune other models, you need to ensure you have permission to do so. Using outputs from models that prohibit distillation could violate terms of service agreements and potentially expose you to legal liability. OpenRouter tracks which models allow their outputs to be used for training purposes through the is_trainable_text property. Models where the author has explicitly allowed text distillation are marked as distillable.

OpenRouter provides distillation information on a best-effort basis. You should always verify the specific license terms for your use case, as licensing requirements may vary depending on how you intend to use the model outputs.

Finding Distillable Models on the Model Page

The easiest way to find models that allow distillation is to use the Distillable filter on the Models page.

Navigate to the Models page with the distillable filter enabled
The Distillable filter in the filter panel will be set to Yes, showing only models that allow distillation

This filter shows you all models where the author has permitted their outputs to be used for training purposes, making it easy to build compliant distillation workflows.

Using the Routing Parameter

For programmatic control, you can use the enforce_distillable_text parameter in your API requests. When set to true, OpenRouter will only route your request to models that allow text distillation.

Field	Type	Default	Description
`enforce_distillable_text`	boolean	-	Restrict routing to only models that allow text distillation.

When enforce_distillable_text is set to true, the request will only be routed to models where the author has explicitly enabled text distillation. If no distillable models are available for your request, you’ll receive an error.

Example: Enforcing Distillable Models

import { OpenRouter } from '@openrouter/sdk';

const openRouter = new OpenRouter({
  apiKey: '<OPENROUTER_API_KEY>',
});

const completion = await openRouter.chat.send({
  model: 'meta-llama/llama-3.1-70b-instruct',
  messages: [{ role: 'user', content: 'Explain quantum computing' }],
  provider: {
    enforceDistillableText: true,
  },
  stream: false,
});

fetch('https://openrouter.ai/api/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer <OPENROUTER_API_KEY>',
    'HTTP-Referer': '<YOUR_SITE_URL>',
    'X-OpenRouter-Title': '<YOUR_SITE_NAME>',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'meta-llama/llama-3.1-70b-instruct',
    messages: [{ role: 'user', content: 'Explain quantum computing' }],
    provider: {
      enforce_distillable_text: true,
    },
  }),
});

import requests

headers = {
  'Authorization': 'Bearer <OPENROUTER_API_KEY>',
  'HTTP-Referer': '<YOUR_SITE_URL>',
  'X-OpenRouter-Title': '<YOUR_SITE_NAME>',
  'Content-Type': 'application/json',
}

response = requests.post('https://openrouter.ai/api/v1/chat/completions', headers=headers, json={
  'model': 'meta-llama/llama-3.1-70b-instruct',
  'messages': [{ 'role': 'user', 'content': 'Explain quantum computing' }],
  'provider': {
    'enforce_distillable_text': True,
  },
})

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer <OPENROUTER_API_KEY>" \
  -H "HTTP-Referer: <YOUR_SITE_URL>" \
  -H "X-OpenRouter-Title: <YOUR_SITE_NAME>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-3.1-70b-instruct",
    "messages": [{"role": "user", "content": "Explain quantum computing"}],
    "provider": {
      "enforce_distillable_text": true
    }
  }'

Use Cases

The distillable filter is particularly useful for:

Building training datasets: When collecting model outputs to train or fine-tune your own models, ensure you only use outputs from models that permit this use.
Distillation pipelines: When creating smaller, specialized models from larger teacher models, filter for models that allow their outputs to be used for training.
Compliance workflows: Organizations with strict compliance requirements can enforce distillation policies programmatically across all API requests.

Provider Routing - Learn more about the enforce_distillable_text parameter and other provider routing options
Models - Browse all available models and their capabilities

​Why Distillation Compliance Matters

​Finding Distillable Models on the Model Page

​Using the Routing Parameter

​Example: Enforcing Distillable Models

​Use Cases

​Related Documentation

Why Distillation Compliance Matters

Finding Distillable Models on the Model Page

Using the Routing Parameter

Example: Enforcing Distillable Models

Use Cases

Related Documentation