Open-weight usually means you can download a model’s learned parameters. Open-source AI, as defined by the Open Source Initiative (OSI), calls for more: the information needed to understand how the model was made, the complete code used to train and run it, and the parameters—made available under terms that support the freedoms associated with open source. A weight download is useful, but it does not by itself establish that a model is open source.
What’s the difference between open-source and open-weight AI models?
Weights are the numerical parameters a model learns during training. They are not source code: code is the set of instructions that carries out tasks, while weights are one result of running those instructions through training and, often, fine-tuning. The OECD makes this distinction in its 2025 primer on AI foundation models.
“Open-weight” is therefore a claim about access to one important artifact: the trained parameters. Depending on the release terms, tools, and available compute, those weights may let developers run a model themselves, adapt it, or fine-tune it. The phrase alone does not tell you whether the training data is documented, whether training code is available, or what you are permitted to do with the model.
What does OSI mean by open-source AI?
The Open Source Initiative’s Open Source AI Definition 1.0 describes a system in terms of the materials people need to study, use, modify, and share it. Its preferred form for making modifications includes three broad components: information about the training data, the complete source code used to train and run the system, and the model parameters. Those materials must also be available under appropriate terms. Read the Open Source AI Definition 1.0 for the full wording.
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This is a fuller standard than simply being able to download weights. OSI also says its definition does not require one specific legal mechanism to ensure parameters are freely available to everyone. In other words, assess the release as a whole—including its terms—rather than treating the word “open” as a guarantee about every component.
What do open weights let you do—and what don’t they prove?
When a release makes weights available, developers may be able to run the model on infrastructure they control or through a hosting provider, and to fine-tune or otherwise optimize a pretrained model. Whether that is practical depends on the particular model, hardware, runtime, and workload; a download alone does not guarantee that a device can run it efficiently.
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Weights do not, by themselves, reveal the training method or provide the code and data information needed to inspect or reproduce it. Nor does public access settle whether a use, modification, or redistribution is permitted. Check the license and any separate usage policy attached to the specific release.
How to compare what a model release actually makes open
Evaluate the specific version you intend to use across these dimensions. OSI’s definition and the OECD’s discussion of release options provide the basis for the checklist below.
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| What to check | Questions to ask | Why it matters |
|---|---|---|
| Training-data information | Does the release explain the data’s provenance, scope, selection, labeling, processing, and sources in enough detail? | It helps people understand what material shaped the model and can support efforts to build an equivalent system. |
| Training code | Is the complete training and data-processing code available, including relevant settings and supporting components? | It makes the method more inspectable and can help others reproduce or modify it. |
| Inference code and architecture | Can you access the code needed to run the model and information about its architecture? | These materials support practical use and understanding of how the system is structured. |
| Parameters or weights | Are the learned parameters available, and under what terms? | They can enable local use, adaptation, or fine-tuning when tools and compute are suitable. |
| Legal terms and policies | Do the license and any separate policies permit your intended use, study, modification, and sharing? | A public download does not establish unrestricted permission. |
| Release scope | Is access public, gated, hosted only, or downloadable with conditions? | “Open” can describe different degrees of access and disclosure. |
Example: what OpenAI says about gpt-oss
OpenAI describes gpt-oss as an open-weight model family. Its gpt-oss Help Center documentation says the weights are available under Apache 2.0, subject to a separate gpt-oss usage policy, and that the models can run on infrastructure users control or through hosting providers. It lists self-managed GPU environments and common inference stacks as deployment options.
This example shows the practical value of available weights, but it does not establish that every open-weight release has the same license, deployment requirements, or access conditions. Read the terms and documentation for the exact model version you plan to use.
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How to check a model before using it
- Identify the exact release. Find the version or checkpoint you want, not just the model family name; documentation and terms can differ across releases.
- Locate its license and policies. Read the license alongside any separate usage policy, and check whether they cover your planned use, modification, and redistribution.
- Check the available artifacts. Confirm which of the weights, inference code, architecture information, training code, and training-data documentation are actually published.
- Assess access and practical requirements. Determine whether the release is public, gated, hosted only, or downloadable with conditions. For local use, check the chosen model’s requirements against your hardware and inference software.
- Describe the release precisely. If you verified only that weights are downloadable, call it open-weight rather than assuming that the full system meets OSI’s open-source definition.
OSI’s FAQ names Pythia, OLMo, Amber, CrystalCoder, and T5 as models that passed its validation phase, while stating that those results are not certifications. The FAQ also explains that the definition does not specifically guide or enforce ethical, trustworthy, or responsible AI development practices. See the OSI Open Source AI Definition FAQ for those qualifications.
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