AiHubMix Documentation Hub

API Information

Endpoint: https://aihubmix.com/v1/moderations
Request Method: POST
Authentication: Authorization: Bearer <AIHUBMIX_API_KEY>
Content-Type: application/json
This endpoint supports two models:
1. omni-moderation-latest: This model and all snapshots support more classification options and multimodal input.
2. text-moderation-latest: Supports only text input, with fewer classification options.

Quick Start

Usage Example

import openai

client = openai.OpenAI(
  api_key="AIHUBMIX_API_KEY",  
  base_url="https://aihubmix.com/v1"
)

response = client.moderations.create(
    model="text-moderation-latest",
    input="The Yangtze River rolls eastward, its waves washing away heroes. Right and wrong, success and failure, all seem empty; the green hills remain, though the sun sets many times. The white-haired fisherman and woodcutter on the riverbank, accustomed to watching the autumn moon and spring breeze. A pot of turbid wine brings joy in meeting, how many events through time are all laughed off.",
)

print(response)

import openai

client = openai.OpenAI(
  api_key="AIHUBMIX_API_KEY", 
  base_url="https://aihubmix.com/v1"
)

response = client.moderations.create(
    model="omni-moderation-latest",
    input=[
        {"type": "text", "text": "The image depicts a male, with his arms raised, body tense, head tilted back, mouth open, showing extreme agitation or anger."},
        {
            "type": "image_url",
            "image_url": {
                "url": "https://thumbs.dreamstime.com/b/violent-man-furious-straining-arms-looking-up-concept-person-35012557.jpg",
                # can also use base64 encoded image URLs
                # "url": "data:image/jpeg;base64,abcdefg..."
            }
        },
    ],
)

print(response)

Output Example

Here is a complete output example, where the model correctly predicts self-harm and violence elements in the image.

{
  "id": "modr-5175",
  "model": "omni-moderation-latest",
  "results": [
    {
      "flagged": true,

      "categories": {
        "harassment": false,
        "harassment_threatening": false,
        "hate": false,
        "hate_threatening": false,
        "illicit": false,
        "illicit_violent": false,

        "self_harm": true,
        "self_harm_instructions": false,
        "self_harm_intent": false,

        "sexual": false,
        "sexual_minors": false,

        "violence": true,
        "violence_graphic": true
      },

      "category_applied_input_types": {
        "harassment": ["text"],
        "harassment_threatening": ["text"],
        "hate": ["text"],
        "hate_threatening": ["text"],
        "illicit": ["text"],
        "illicit_violent": ["text"],

        "self_harm": ["text", "image"],
        "self_harm_instructions": ["text", "image"],
        "self_harm_intent": ["text", "image"],

        "sexual": ["text", "image"],
        "sexual_minors": ["text"],

        "violence": ["text", "image"],
        "violence_graphic": ["text", "image"]
      },

      "category_scores": {
        "harassment": 0.00507676338091392,
        "harassment_threatening": 0.0008967480822931635,
        "hate": 8.830458477845481e-05,
        "hate_threatening": 1.0720880092159908e-05,
        "illicit": 3.740956047302422e-05,
        "illicit_violent": 2.868540823874629e-05,

        "self_harm": 0.6967791744783793,
        "self_harm_instructions": 0.00027978227581033677,
        "self_harm_intent": 0.0003781080988395418,

        "sexual": 0.0007007652612809208,
        "sexual_minors": 2.5071593847983196e-06,

        "violence": 0.5236158587905301,
        "violence_graphic": 0.4213528687243541
      }
    }
  ]
}

The output result includes several categories in the JSON response, which inform you about the types of content present in the input (if any) and the extent to which the model believes they are 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.

Content Categories

The table below describes the types of content that the moderation API can detect, along with the models and input types supported for each category.

Categories labeled “text-only” do not support image input. If you send only images to the model (without text) using omni-moderation-latest, the model will return a score of 0 for these unsupported categories.

Category	Description	Model	Input
`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`	The same types of content flagged by the `illicit` category, but also includes references to violence or procuring a weapon.	Omni only	Text only
`illicit/violent`	Similar to the content type marked `illicit`, but also includes mentions of violence or acquiring weapons.	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

Last updated: 2026-06-01

Getting Started

Gateway Features

Model Capabilities

Protocol Reference

Platform Management

Privacy & Terms

Moderation

API Information

Quick Start

Usage Example

Output Example

Content Categories

​API Information

​Quick Start

​Usage Example

​Output Example

​Content Categories

API Information

Quick Start

Usage Example

Output Example

Content Categories