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A proprietary language model is controlled by its provider, which generally keeps the model’s trained weights private and offers access through its own app or API. The label describes control and access—not how capable, safe, private, or costly the model is.
What “proprietary language model” means
A language model learns patterns from data and uses them to generate or interpret text. Its trained weights—the numerical parameters that encode what it learned—are central to how it works. In a proprietary model, the provider retains control of those weights and does not ordinarily make them available for users to download, inspect, or modify. NVIDIA summarizes the role of weights this way: “At the core of any AI model are weights.” (NVIDIA’s explanation of open models.)
Users typically access a proprietary model through the provider’s application or API rather than running the weights themselves. The exact access, disclosures, and permitted uses depend on the particular model and provider; “proprietary” is not a guarantee that every component is secret or that access is limited to one specific interface.
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How it differs from open-weight and open-source models
The most useful first question is whether the model’s weights are available for users to obtain. But “open,” “open-weight,” and “open source” are not interchangeable labels.
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| Category | What is generally available | What to check |
|---|---|---|
| Proprietary model | Provider-controlled weights; use is typically through the provider’s app or API. | What access the provider offers, what information it discloses, and the terms governing use. |
| Open-weight model | Weights are available to download, but training data, training code, and documentation may not be. | Whether the release includes other artifacts and what its license and usage policy allow. |
| Open-source AI under OSI’s definition | Model parameters, complete training and inference code, and sufficient information about training data to recreate a substantially equivalent system. | Whether the release meets the definition’s requirements, rather than relying on its label alone. |
The Open Source Initiative’s summary of its Open Source AI Definition sets out the broader requirements. A downloadable model is not automatically open source: weights alone do not provide all the information and tools needed to study, modify, or recreate the system.
What the label does—and does not—tell you
“Proprietary” identifies who controls access to important model components. It does not, by itself, establish a model’s quality, privacy protections, safety, price, or suitability for a task. Openness is also multidimensional: weights, code, data information, documentation, licensing, and access can vary independently. A 2023 paper by Liesenfeld, Lopez, and Dingemanse discusses these dimensions in its analysis of instruction-tuned text generators (paper on openness, transparency, and accountability).
To choose between specific models, test them against the work you need them to do and examine the relevant deployment and terms. Do not infer a performance or privacy advantage from the category name alone.
Deployment changes who does the work
A managed proprietary service can leave the provider responsible for operating the infrastructure. With downloadable weights, an organization may instead run the model on infrastructure it controls or use a hosting provider. That can increase control over deployment, but it also shifts operational tasks—such as providing compute and storage and maintaining the service—to the operator.
OpenAI’s gpt-oss example makes the distinction concrete: OpenAI describes gpt-oss-120b and gpt-oss-20b as open-weight reasoning models that can run on user-controlled infrastructure or through hosting providers. Its Help Center says they are not served through the OpenAI API and are not available in ChatGPT. It also says users are responsible for compute, storage, and third-party hosting costs when self-hosting. These details are specific to gpt-oss and may change; consult the current gpt-oss information.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions to ask when comparing models
- Artifacts: Are the weights downloadable? Are training code, data information, and evaluation materials also available?
- Rights: What do the specific license and usage policy allow for use, modification, and redistribution?
- Deployment: Must the model be accessed through a provider, or can it run on infrastructure your organization controls?
- Operations: Who handles hosting, updates, scaling, and maintenance? What compute, storage, and staff effort will be needed?
- Task fit: How does the specific model perform on your intended workload, and what safety requirements apply to that deployment?
Licensing is separate from downloadability. For example, OpenAI says Apache 2.0 applies to gpt-oss subject to its usage policy; that is a provider-specific arrangement, not a rule for open-weight models generally. Check the terms for the exact release you plan to use.
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