Open, Open-Weight and Closed AI Models
Definition
AI models differ in what users can inspect and control. Open-weight models make trained parameters available under a license; closed models are typically accessed through hosted products or APIs.
What does open-weight mean?
An open-weight model makes its trained parameter files available for others to download under a license. That is narrower than full open source: the training data, training code or complete development process may still be unavailable.
What is a closed model?
A closed model is typically accessed through a hosted application or API while the provider keeps the weights private. Users get less control over the underlying model but avoid operating the full inference infrastructure themselves.
Why the distinction matters
Open weights allow local deployment, fine-tuning and independent research, but the operator inherits more security, compliance and maintenance work. Closed APIs centralize updates and safeguards with the provider. Licensing terms must be checked independently of technical availability.
Related terms, defined
Reference guide and primary sources
Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.