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Choose by the exact model version and the terms attached to it—not by its family name, an “open” label, or the fact that its weights can be downloaded. Before deploying commercially, verify that version’s license, any policies incorporated into it, and whether the conditions fit your use. If you will use a hosted API or managed inference service, check that provider’s terms separately.
What “license-safe” means for your use
There is no single commercial-use answer for every release in a model family. A license may permit commercial use while imposing obligations on redistribution, branding, derivative models, or certain large-scale users. Another release from the same family may have different terms. “License-safe” therefore means that you have checked the terms for the particular artifact and deployment you intend to use, and can meet every applicable condition.
Start by describing the planned use precisely. Will the team run the weights internally, offer an application or hosted service, distribute the weights, publish a fine-tuned model, or use outputs to train or improve another model? Those activities can raise different licensing questions. Downloadability alone does not answer them.
Where to find the terms that govern a model
- Identify the exact artifact. Record the full model name, variant, version or repository revision, and where the weights came from. A family label is not enough to identify the applicable terms.
- Read the license file for that version. Confirm that it belongs to the exact artifact you plan to evaluate or deploy. Save a copy with the deployment record.
- Read the model card and repository instructions. Look for license distinctions, use restrictions, and references to separate agreements. A repository may distinguish the license for code from the terms for model weights.
- Open every incorporated policy or agreement. If the license makes an acceptable-use policy or another document part of the terms, review that document too. Record its version or date where available.
- Check the service contract if inference is hosted. The weights’ license and the provider’s terms are separate layers. Review the actual provider and plan terms for the service you will use.
Terms can change. Preserve the version-specific documents you reviewed, and repeat the check before moving to a new model revision.
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Compare candidates against your deployment
Use a comparison sheet for each candidate. “Commercial use allowed” is only one entry; record the source and exact revision for every answer. If a term is unclear or not stated in the materials you reviewed, mark it for direct verification rather than assuming permission.
| What to check | Question to answer | What to record |
|---|---|---|
| Identity | Which exact variant, version, repository, and revision will be used? | Full identifier, revision, and the location of the matching license file and model card. |
| Commercial grant | Does the agreement authorize the revenue-generating activity planned, or require separate permission or an application? | The relevant grant and any approval condition; do not infer permission from weight availability. |
| Modification and redistribution | Can you modify or distribute the weights, a fine-tuned version, or a product that includes them? What notices, attribution, or modification markings are required? | Applicable restrictions and the exact notices or other obligations. |
| Branding and naming | Must a product display specified wording, or must a distributed derivative model use a prescribed name? | When the rule applies and what wording or naming it requires. |
| Scale or business triggers | Do user counts, revenue, product category, or another threshold change the permission or obligations? | The threshold, how it is measured, the relevant period, and any permission to request. |
| Use policies | Does the license incorporate an acceptable-use policy or impose separate restrictions on uses? | The policy, its version, and restrictions relevant to the planned application. |
| Outputs and derivative training | Do output use, model improvement, or training another model create conditions? | The exact provision addressing outputs or downstream model use; do not assume output ownership settles it. |
| Deployment route | Will you run self-hosted weights or use hosted inference or an API? | For hosted services, the provider, plan, and applicable service terms in addition to the model terms. |
| Geography and compliance | Are there territory exclusions, sanctions, export-control requirements, or applicable-law terms relevant to the operator? | Any relevant geographic or compliance conditions and who in the organization has assessed them. |
Why the exact version matters: Llama 3.1 and Qwen
Llama 3.1 Community License Agreement
Meta’s Llama 3.1 Community License Agreement, released July 23, 2024, grants a non-exclusive, worldwide, non-transferable, royalty-free limited license under specified Meta rights to use, reproduce, distribute, copy, create derivative works of, and modify the Llama Materials. That grant is not the whole agreement: the license also incorporates compliance with the Llama Acceptable Use Policy and specifies conditions for redistribution.
For this release, the agreement includes requirements such as notices on redistribution, “Built with Llama” display in specified cases, and naming requirements for certain distributed derivative models. It also sets a release-specific scale trigger: a licensee or its affiliates that exceeded 700 million monthly active users in the preceding calendar month as of the Llama 3.1 release date must request a separate license from Meta. These conditions describe the Llama 3.1 agreement; do not apply them automatically to other Llama releases or model families.
Qwen repository guidance
Qwen’s repository README distinguishes code licensing from model-weight licensing and directs commercial users to the agreement accompanying each model. It identifies legacy Qwen variants for which commercial use required applying or contacting the provider. That guidance is a reason to inspect the agreement for the specific model—not a complete statement of terms for every Qwen release, past or current.
What registry summaries can—and cannot—tell you
OpenWeightModels.eu reported that its 2026 snapshot recorded 23 of 32 reviewed models as commercially allowed, one as specially conditioned, and eight as conditional. Those are counts from that registry’s reviewed sample, not a market-wide estimate or a ruling on a model’s suitability for your use. The registry itself says the official license, incorporated policies, provider terms, and applicable law remain authoritative. Use summaries to identify candidates for closer review, not as a substitute for reading the governing terms.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Pre-deployment checks
- Pin the exact repository and revision used in evaluation and production.
- Keep the matching license file and model card with the deployment record.
- Identify and review every policy or agreement incorporated by reference.
- Match each condition to the real deployment: internal use, hosted service, distribution, fine-tuning, or embedding in a product.
- Check for thresholds, product-specific conditions, or obligations connected to model improvement.
- Review the provider’s terms separately if using hosted inference or an API.
- Repeat the review before upgrading; a family name does not guarantee unchanged terms.
This is a practical screening guide, not legal advice. If a term is ambiguous, a threshold may apply, or the deployment has significant commercial or compliance impact, have qualified counsel review the exact documents and proposed use.
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