Moderation

Identify potentially harmful content in text and images.

Use the moderations endpoint to check whether text or images are potentially harmful. If harmful content is identified, you can take corrective action, like filtering content or intervening with user accounts creating offending content. The moderation endpoint is free to use.

You can use two models for this endpoint:

• omni-moderation-latest: This model and all snapshots support more categorization options and multi-modal inputs.
• text-moderation-latest (Legacy): Older model that supports only text inputs and fewer input categorizations. The newer omni-moderation models will be the best choice for new applications.

Quickstart

Use the tabs below to see how you can moderate text inputs or image inputs, using our official SDKs and the omni-moderation-latest model:

Moderate text inputs

python

from openai import OpenAI
client = OpenAI()

response = client.moderations.create(
    model="omni-moderation-latest",
    input="...text to classify goes here...",
)

print(response)

python

from openai import OpenAI
client = OpenAI()

response = client.moderations.create(
    model="omni-moderation-latest",
    input="...text to classify goes here...",
)

print(response)

python

from openai import OpenAI
client = OpenAI()

response = client.moderations.create(
    model="omni-moderation-latest",
    input=[
        {"type": "text", "text": "...text to classify goes here..."},
        {
            "type": "image_url",
            "image_url": {
                "url": "https://example.com/image.png",
                # can also use base64 encoded image URLs
                # "url": "data:image/jpeg;base64,abcdefg..."
            }
        },
    ],
)

print(response)

Here’s a full example output, where the input is an image from a single frame of a war movie. The model correctly predicts indicators of violence in the image, with a violence category score of greater than 0.8.

{
  "id": "modr-970d409ef3bef3b70c73d8232df86e7d",
  "model": "omni-moderation-latest",
  "results": [
    {
      "flagged": true,
      "categories": {
        "sexual": false,
        "sexual/minors": false,
        "harassment": false,
        "harassment/threatening": false,
        "hate": false,
        "hate/threatening": false,
        "illicit": false,
        "illicit/violent": false,
        "self-harm": false,
        "self-harm/intent": false,
        "self-harm/instructions": false,
        "violence": true,
        "violence/graphic": false
      },
      "category_scores": {
        "sexual": 2.34135824776394e-7,
        "sexual/minors": 1.6346470245419304e-7,
        "harassment": 0.0011643905680426018,
        "harassment/threatening": 0.0022121340080906377,
        "hate": 3.1999824407395835e-7,
        "hate/threatening": 2.4923252458203563e-7,
        "illicit": 0.0005227032493135171,
        "illicit/violent": 3.682979260160596e-7,
        "self-harm": 0.0011175734280627694,
        "self-harm/intent": 0.0006264858507989037,
        "self-harm/instructions": 7.368592981140821e-8,
        "violence": 0.8599265510337075,
        "violence/graphic": 0.37701736389561064
      },
      "category_applied_input_types": {
        "sexual": [
          "image"
        ],
        "sexual/minors": [],
        "harassment": [],
        "harassment/threatening": [],
        "hate": [],
        "hate/threatening": [],
        "illicit": [],
        "illicit/violent": [],
        "self-harm": [
          "image"
        ],
        "self-harm/intent": [
          "image"
        ],
        "self-harm/instructions": [
          "image"
        ],
        "violence": [
          "image"
        ],
        "violence/graphic": [
          "image"
        ]
      }
    }
  ]
}

The output has several categories in the JSON response, which tell you which (if any) categories of content are present in the inputs, and to what degree the model believes them to be present.

Output category	Description
flagged	Set to true if the model classifies the content as potentially harmful, false otherwise.
categories	Contains a dictionary of per-category violation flags. For each category, the value is true if the model flags the corresponding category as violated, false otherwise.
category_scores	Contains a dictionary of per-category scores output by the model, denoting the model’s confidence that the input violates the OpenAI’s policy for the category. The value is between 0 and 1, where higher values denote higher confidence.
category_applied_input_types	This property contains information on which input types were flagged in the response, for each category. For example, if the both the image and text inputs to the model are flagged for “violence/graphic”, the violence/graphic property will be set to ["image", "text"]. This is only available on omni models.

We plan to continuously upgrade the moderation endpoint’s underlying model. Therefore, custom policies that rely on category_scores may need recalibration over time.

Content classifications

The table below describes the types of content that can be detected in the moderation API, along with which models and input types are supported for each category.

Categories marked as “Text only” do not support image inputs. If you send only images (without accompanying text) to the omni-moderation-latest model, it will return a score of 0 for these unsupported categories.

Category	Description	Models	Inputs
harassment	Content that expresses, incites, or promotes harassing language towards any target.	All	Text only
harassment/threatening	Harassment content that also includes violence or serious harm towards any target.	All	Text only
hate	Content that expresses, incites, or promotes hate based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste. Hateful content aimed at non-protected groups (e.g., chess players) is harassment.	All	Text only
hate/threatening	Hateful content that also includes violence or serious harm towards the targeted group based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste.	All	Text only
illicit	Content that gives advice or instruction on how to commit illicit acts. A phrase like “how to shoplift” would fit this category.	Omni only	Text only
illicit/violent	The same types of content flagged by the illicit category, but also includes references to violence or procuring a weapon.	Omni only	Text only
self-harm	Content that promotes, encourages, or depicts acts of self-harm, such as suicide, cutting, and eating disorders.	All	Text and images
self-harm/intent	Content where the speaker expresses that they are engaging or intend to engage in acts of self-harm, such as suicide, cutting, and eating disorders.	All	Text and images
self-harm/instructions	Content that encourages performing acts of self-harm, such as suicide, cutting, and eating disorders, or that gives instructions or advice on how to commit such acts.	All	Text and images
sexual	Content meant to arouse sexual excitement, such as the description of sexual activity, or that promotes sexual services (excluding sex education and wellness).	All	Text and images
sexual/minors	Sexual content that includes an individual who is under 18 years old.	All	Text only
violence	Content that depicts death, violence, or physical injury.	All	Text and images
violence/graphic	Content that depicts death, violence, or physical injury in graphic detail.	All	Text and images