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Start with the Open Source Initiative’s AI-specific definition, then check the exact license, acceptable-use policy, and access terms for each model version. Downloadable weights can be useful without meeting every part of the OSI definition, so this reading list treats “open-source AI” and the broader phrase “open models” as related but not interchangeable.
First, learn what “open-source AI” means
The Open Source Initiative’s Open Source AI Definition 1.0 sets out criteria for openness in AI systems. It addresses the model parameters, the code used to derive them, and sufficiently detailed information about training data. The definition says that model parameters, including weights, must be available under OSI-approved terms.
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That makes weights one part of the picture, not a shortcut to a verdict. A release with downloadable weights may still leave important questions about derivation code or training-data information unanswered. Read the OSI FAQ alongside the definition for explanations of its scope and terminology.
Read the sources in this order
- OSI’s definition and FAQ: Use these to establish the criteria you will apply. Ask whether parameters, derivation code, and training-data information are available, and whether the terms meet the definition’s requirements.
- The model publisher’s license and acceptable-use policy: Find the documents that apply to the exact model and version, rather than relying on a family name or a third-party catalog. Check commercial use, redistribution, attribution, restrictions, and any obligations that apply to derivatives or outputs.
- The exact model listing: Confirm which version is available and how to access it. A catalog can help locate models, but it does not replace reading the license or policy. Hugging Face’s Meta Llama organization lists multiple Llama families; some repositories require gated access and agreement to terms.
- An ecosystem overview: Hugging Face’s Summer 2026 analysis offers context about activity on its Hub during the first seven months of 2026. Treat it as an account of that platform and period, not a census of all open models.
Compare release terms, not just model names
These examples illustrate different licensing approaches. They are not a complete catalog, a legal interpretation, or a quality ranking.
#1 Best Overall
| Example | Terms identified by the publisher | What to check before use |
|---|---|---|
| Meta Llama | The applicable Community License and Acceptable Use Policy govern Llama models. | Read the terms for the particular version and confirm any access gate, attribution requirement, and use restriction. |
| Google Gemma 4 | Google announced Gemma 4 under Apache 2.0. | Check the applicable release materials and the deployment needs of the specific variant. Google describes Gemma 4 variants spanning edge devices to 31B parameters; actual resource needs depend on the configuration and workload. |
| DeepSeek R1 | DeepSeek’s R1 announcement identifies MIT for its code and models. | Check the announcement and applicable materials for the specific component and release you plan to use. |
Do not transfer permissions from one model family to another. Even within a family, the relevant terms can depend on version. Meta describes its Llama license as allowing broad commercial use and redistribution of additional work, but that description does not remove the need to read the applicable Community License and Acceptable Use Policy.
Use this checklist to evaluate a model
- Disclosure: Are parameters, derivation code, and sufficiently detailed training-data information available under terms consistent with the OSI definition?
- Permissions: What exact license applies, and what does it require for commercial use, redistribution, attribution, or modified versions?
- Use restrictions: Is there a separate acceptable-use policy, and does it limit your intended application?
- Access: Can you download the repository directly, or must you accept terms first? If you use a hosted service, check its terms separately from the model’s.
- Capability and modality: Does the release support the task and inputs you need? The sources summarized here do not provide a common evaluation across these examples, so they do not establish a universal benchmark winner.
- Deployment: What model size and compute resources fit your workload? A parameter count alone does not determine resource needs across configurations and uses.
- Output reuse: If you plan to train or improve another model using generated outputs, check the version-specific terms first.
Check output-reuse rules by Llama version
Meta describes different rules across Llama generations. For Llama 3.1 and later, Meta says outputs may be used to train or improve other models if attribution requirements are met. Meta describes restrictions on that practice for Llama 2 and Llama 3. Verify the terms for the exact version rather than assuming a rule applies to the whole family.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep the ecosystem evidence in perspective
Hugging Face’s Summer 2026 observations describe activity on its own Hub during the first seven months of 2026. They can help readers understand what was happening on that platform over that period, but they do not establish the size or composition of the entire model ecosystem.
Model releases, repository access, licenses, acceptable-use policies, and hosting terms can change. For an adoption decision, use the publisher’s current terms and the exact model repository; a reading list or catalog is a starting point, not a substitute for that check.
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