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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →A closed AI model—more precisely, a closed-weight model—is one whose learned weights are not publicly available to download and remain under the developer’s control. You may still use it through a website, app, or API: access to the model is not the same as access to its weights.
What does “closed AI model” mean?
Weights are the learned numerical parameters a model uses to produce outputs. Code interprets and applies those parameters. In ordinary usage, a model is closed-weight when its developer does not publicly release the weights for download.
“Closed AI model” is common shorthand, but “closed-weight” identifies the specific thing that is closed. The label does not by itself tell you whether the developer has published documentation, code, training data, or a description of how the model behaves.
Does API access make a model open?
No. An API lets software send requests to a model operated by its provider; it does not necessarily give customers the weights or the ability to run the model themselves. A hosted chatbot works similarly: you can use the model while the provider retains control of the underlying weights.
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Stanford HAI’s release framework treats access as a range of distinct arrangements, from no external access through hosted services and APIs to fine-tuning access, downloadable weights, and releases that include data and code. Those arrangements should not be collapsed into a single open-or-closed label. Stanford HAI’s explanation of open-weight models describes public weights as components people can download; its framework for governing open foundation models places different release and access choices on a broader continuum.
Open-weight is not automatically open-source
Publicly downloadable weights establish that users can obtain the model’s parameters, but they do not establish that the training data, complete codebase, or full development process is public. Nor does the phrase settle what users may do: licenses and usage policies can restrict commercial use, redistribution, or certain applications.
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Stanford HAI’s discussion of China’s open-weight ecosystem distinguishes the release of weights from the release of training data and code. For a concrete example of why terms matter, OpenAI says its gpt-oss models have publicly available weights under Apache 2.0 alongside a separate usage policy. OpenAI also says they can run on infrastructure controlled by the user or through hosting providers. Check the gpt-oss documentation for the current terms and details.
Examples: a closed service and an open-weight release
OpenAI’s hosted frontier models
OpenAI describes its most powerful models as services, says it does not distribute their weights beyond OpenAI and Microsoft, and says third parties access them through APIs. That is a provider’s description of its own closed-weight service arrangement, not a rule that applies to every model from every developer. OpenAI’s frontier-risk approach provides that account.
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OpenAI’s gpt-oss models
OpenAI describes gpt-oss-120b and gpt-oss-20b as open-weight models, with weights available under Apache 2.0 and the gpt-oss usage policy. The company says these models are not served through the OpenAI API or ChatGPT; users can run them on infrastructure they control or through hosting providers. These are product-specific details that can change, so consult the linked documentation for current availability and terms.
In an August 5, 2025 article, OpenAI argued that open-weight and closed models can complement one another, pointing to local control and data-residency needs as reasons open weights may matter. This is the company’s position, not a universal finding about which model type is preferable. OpenAI’s article on open weights presents that view.
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How to evaluate whether a model is open or closed
Instead of relying on a single label, check the specific assets and permissions that matter for your use:
- Weights: Can anyone download them, are downloads restricted, or are they unavailable?
- Access channel: Is the model available only in a hosted app, through an API, through a fine-tuning endpoint, or for self-hosting?
- Other released assets: Are training data, training code, inference code, documentation, or evaluation materials available?
- License and policy: What do the license and usage rules allow for commercial use, redistribution, and applications?
- Deployment control: Who operates the model, and where is input data processed?
These questions separate technical access from legal permission and operational control. A public behavior specification, for example, describes intended behavior but does not prove that weights are downloadable; OpenAI’s Model Spec dated April 11, 2025 is a published specification, not a weight release. For an explanation of weights and code, see OpenAI’s overview of how ChatGPT and its foundation models are developed.
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