AI Model Topics APIDescribe the model. Define the evidence.

Use several dimensions instead of one ranking

A model can be described by its task, accepted inputs, produced outputs, and intended use. These dimensions answer different questions. A broad label such as language model does not say whether a particular deployment is suitable for a specific document workflow.

Keep the catalog’s topics separate from claims about an individual model. A page about image generation can carry that topic without asserting that every model mentioned supports it. Store the actual capability statement with its source and scope.

Attach claims to a version and a context

Model names can represent families, releases, or hosted aliases. Record the level you mean. If a capability was documented for a specific version, avoid silently transferring the claim to every member of the family.

Evaluation results also need context: task, dataset, language, configuration, and the conditions under which the work was tested. A score without those details is difficult to interpret. Use the topic vocabulary to support discovery, while preserving the original evaluation description.

Keep access and licensing out of assumptions

A topic label does not grant access to a model or establish rights to deploy it. Keep distribution method, usage conditions, and the date you checked them in separate metadata when they are relevant to the catalog.

Avoid translating broad marketing language into a verified technical feature. A claim such as general purpose may describe the provider’s positioning. A catalog should preserve attribution and distinguish an advertised capability from an independently assessed workflow.

Plan for updates and retired releases

A useful model knowledge base should explain what changes when a release is superseded. Keep older records available where they help interpret historical articles or evaluations. Deprecation of an identifier should not erase the context attached to past work.

Use the same discipline for emerging concepts such as AGI. Define whether an item is a research discussion, a benchmark proposal, or a capability claim. Readers should be able to distinguish the subject of an article from evidence about a deployed system.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

{
  "model_reference": "example-model-v1",
  "task_topics": [
    "text.classification"
  ],
  "input_modalities": [
    "text"
  ],
  "evidence_scope": "example evaluation only",
  "claim_status": "attributed"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about AI Model Topics API