Input of text and images
AI Models & Assistants / Knowledge library
omni-moderation
Identify potentially harmful content in text and images
Why learn omni-moderation?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text / image inputs.
Where it came from
omni-moderation: Identify potentially harmful content in text and images Published aliases: omni-moderation-2024-09-26. Input: text, image. Output: text. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what omni-moderation does.
- Identify a use case for omni-moderation.
- Describe a limitation of omni-moderation.
- Name a compatible tool for omni-moderation.
- Compare omni-moderation against an alternative.
Documentation and sources
- Primary source / model card (developers.openai.com)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Global Marketplace Visual Integrity
A peer-to-peer marketplace platform facilitating the exchange of used household goods.
The business problem
The platform struggles to manually review millions of user-uploaded listing images and descriptions, leading to the accidental publication of prohibited or harmful content that violates community safety guidelines.
How this topic solves it
By integrating the omni-moderation-latest model, the platform can programmatically analyze both text descriptions and image uploads in a single request to identify policy-violating content before it reaches the public feed.
- Configure the application backend to intercept user-submitted listing payloads containing text and image assets.
- Format the request to the v1/moderations endpoint using the omni-moderation-latest model identifier.
- Process the returned moderation scores to determine if the content triggers safety flags.
- Automate the rejection of flagged items and route them to a human review queue for final verification.
This diagram illustrates the path of user content through the moderation system.
omni-moderation-latest model
Logic for pass/fail
Approved content
Manual verification
- User Upload → sends payload → Moderation API
- Moderation API → returns scores → Decision Engine
- Decision Engine → if safe → Public Feed
- Decision Engine → if flagged → Human Review
Expected business outcome
A more secure user environment where harmful content is filtered out automatically, reducing the burden on human moderators and increasing trust in the marketplace.
Limitations and checks
The model may produce false positives on ambiguous content, requiring developers to implement a secondary human-in-the-loop review process to verify flagged items before permanent deletion.