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

# Create Moderation

> Classifies if text and/or image inputs are potentially harmful

## Overview

The moderation endpoint classifies text and image content to detect potentially harmful material across multiple categories. Learn more in the [moderation guide](https://platform.openai.com/docs/guides/moderation).

## Method

```python theme={null}
client.moderations.create(
    input="Sample text to moderate",
    model="omni-moderation-latest"
)
```

## Parameters

<ParamField path="input" type="string | array" required>
  Input to classify. Can be:

  * A single string
  * An array of strings
  * An array of multi-modal input objects (text and images)
</ParamField>

<ParamField path="model" type="string">
  The content moderation model to use. Available models:

  * `omni-moderation-latest` - Latest omni-modal model
  * `omni-moderation-2024-09-26` - Specific omni-modal version
  * `text-moderation-latest` - Latest text-only model
  * `text-moderation-stable` - Stable text-only model

  Learn more in the [moderation guide](https://platform.openai.com/docs/guides/moderation) and about [available models](https://platform.openai.com/docs/models#moderation).
</ParamField>

## Response

Returns a `ModerationCreateResponse` object containing:

<ResponseField name="id" type="string">
  Unique identifier for the moderation request
</ResponseField>

<ResponseField name="model" type="string">
  The model used to generate the moderation results
</ResponseField>

<ResponseField name="results" type="array">
  List of moderation objects, one for each input

  <Expandable title="Moderation object">
    <ResponseField name="flagged" type="boolean">
      Whether any category was flagged
    </ResponseField>

    <ResponseField name="categories" type="object">
      Boolean flags for each category:

      * `harassment` - Harassing language
      * `harassment/threatening` - Harassment with violence/harm
      * `hate` - Hate speech based on protected attributes
      * `hate/threatening` - Hateful content with violence/harm
      * `illicit` - Instructions for wrongdoing
      * `illicit/violent` - Violent wrongdoing instructions
      * `self-harm` - Self-harm promotion
      * `self-harm/instructions` - Self-harm instructions
      * `self-harm/intent` - Self-harm intent expression
      * `sexual` - Sexual content
      * `sexual/minors` - Sexual content involving minors
      * `violence` - Violence depiction
      * `violence/graphic` - Graphic violence
    </ResponseField>

    <ResponseField name="category_scores" type="object">
      Confidence scores (0-1) for each category with the same keys as categories
    </ResponseField>

    <ResponseField name="category_applied_input_types" type="object">
      Input types (text/image) that each score applies to
    </ResponseField>
  </Expandable>
</ResponseField>

## Examples

### Text Moderation

```python theme={null}
from openai import OpenAI

client = OpenAI()

response = client.moderations.create(
    input="I want to hurt someone"
)

result = response.results[0]
if result.flagged:
    print("Content flagged!")
    print(f"Violence score: {result.category_scores.violence}")
    print(f"Categories flagged: {[k for k, v in result.categories.model_dump().items() if v]}")
```

### Multi-Modal Moderation

```python theme={null}
response = client.moderations.create(
    model="omni-moderation-latest",
    input=[
        {
            "type": "text",
            "text": "Check this image"
        },
        {
            "type": "image_url",
            "image_url": {
                "url": "https://example.com/image.jpg"
            }
        }
    ]
)
```

### Batch Moderation

```python theme={null}
response = client.moderations.create(
    input=[
        "First text to check",
        "Second text to check",
        "Third text to check"
    ]
)

for idx, result in enumerate(response.results):
    print(f"Input {idx}: Flagged={result.flagged}")
```

## Async Usage

```python theme={null}
from openai import AsyncOpenAI

client = AsyncOpenAI()

response = await client.moderations.create(
    input="Text to moderate"
)
```