# Open models one year later: licensing since Llama 3 [Skip to content](#lm-inhoud)Network/[NL](/en/open-modellen-een-jaar-later-wat-er-sinds-llama-3-aan-licenties-verand)EN[Hubhub.llmnet.nlCompare models on task, language, cost and licence.](https://hub.llmnet.nl/en/)[Communitycommunity.llmnet.nlPrompt techniques, patterns and system prompts.](https://community.llmnet.nl/en/)[APIapi.llmnet.nlLLMs in production: rate limits, routing, structured output.](https://api.llmnet.nl/en/)[Consultancyconsultancy.llmnet.nlRolling out AI in an organisation, pilot to production.](https://consultancy.llmnet.nl/en/)[Newsnieuws.llmnet.nlAI developments, explained for the Netherlands.](https://nieuws.llmnet.nl/en/)[Benchmarkbenchmark.llmnet.nlMeasure AI quality yourself, on your own tasks.](https://benchmark.llmnet.nl/en/)[Careersvacatures.llmnet.nlAI roles, salaries and career paths in the Netherlands.](https://vacatures.llmnet.nl/en/)[Learnleren.llmnet.nlAI concepts in plain language, beginner to builder.](https://leren.llmnet.nl/en/)[Guidegids.llmnet.nlRun AI privately on your own Mac, PC, NAS or home server.](https://gids.llmnet.nl/en/)[Directorydirectory.llmnet.nlMapping the AI ecosystem: tools, models, companies.](https://directory.llmnet.nl/en/)[Radarradar.llmnet.nlSignals from X, research and communities for indie developers.](https://radar.llmnet.nl/en/)[Appsapps.llmnet.nlReviews of AI apps and open-source repos, with tips for builders.](https://apps.llmnet.nl/en/)[llmnet.nl — main site](https://llmnet.nl/en/)[](https://x.com/intent/post?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fopen-modellen-een-jaar-later-wat-er-sinds-llama-3-aan-licenties-verand&text=Open%20models%20one%20year%20later%3A%20licensing%20since%20Llama%203)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fopen-modellen-een-jaar-later-wat-er-sinds-llama-3-aan-licenties-verand)[](https://www.reddit.com/submit?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fopen-modellen-een-jaar-later-wat-er-sinds-llama-3-aan-licenties-verand&title=Open%20models%20one%20year%20later%3A%20licensing%20since%20Llama%203)[](#)[](https://x.com/intent/post?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fopen-modellen-een-jaar-later-wat-er-sinds-llama-3-aan-licenties-verand&text=Open%20models%20one%20year%20later%3A%20licensing%20since%20Llama%203)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fopen-modellen-een-jaar-later-wat-er-sinds-llama-3-aan-licenties-verand)[](https://www.reddit.com/submit?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fopen-modellen-een-jaar-later-wat-er-sinds-llama-3-aan-licenties-verand&title=Open%20models%20one%20year%20later%3A%20licensing%20since%20Llama%203)[](#) By Ivo Donker — compiled with AI assistance (Claude & Gemini) # Open models, one year later: how licensing changed since Llama 3 The landscape of open-weights and open-source language models has shifted significantly in recent years. Anyone looking to understand broader market movements can explore the [market developments in open versus closed models](https://nieuws.llmnet.nl/en/open-vs-closed-modellen-marktontwikkeling) to study in depth the commercial and strategic background driving these dynamics. Since the launch of Meta's Llama 3 in spring 2024, fundamental changes have taken place across legal frameworks, applied license models, and community expectations surrounding openness in artificial intelligence. Those who recall what the market looked like in 2024 know that [Llama 3 stood as the open-weights benchmark back then; this post updates that picture](https://nieuws.llmnet.nl/en/licentiedebat-open-modellen) to show how the industry has since shifted toward more permissive licenses and stricter open-source definitions. License terms change continuously; always verify the model card or official license text for the current status (verified on 2026-08-07). For developers looking to broaden their perspective on wider trends, the page covering [key developments in open-source LLMs](https://nieuws.llmnet.nl/en/open-source-llm-trends) is also a valuable resource for the latest insights into adoption and community growth. Additionally, it is crucial to recognize how technological infrastructure goes hand in hand with legal openness. To understand how modern dataset foundations align with legal frameworks, reviewing the background on [open data initiatives for AI](https://nieuws.llmnet.nl/en/open-data-initiatieven-voor-ai) provides valuable insight into how open data serves as the basis for compliant training. ## What changed since last time Compared to the landscape around the Llama 3 launch, four fundamental shifts in licensing and legal definitions have reshaped the broader industry: - Shift toward permissive licensing: Where community licenses with usage restrictions were once standard, leading models like DeepSeek-R1 and Mistral 3 are now released entirely under established open licenses such as MIT and Apache 2.0, substantially reducing legal friction for enterprises. - The arrival of the OSAID: In late 2024, the Open Source Initiative introduced the official Open Source AI Definition, establishing a formal, measurable standard for what truly constitutes "open source," rather than leaving it as an ill-defined marketing buzzword. - Adjustments to Llama terms: With the release of Llama 4, the controversial clause that prohibited using the model to train other LLMs has been definitively dropped, paving the way for distillation techniques. - European AI Act exemptions: European Union legislation now offers specific exemptions for open-source AI models, provided they meet strict documentation and transparency requirements, giving innovators greater legal certainty. ## Llama 3 and the introduction of the community license When Meta released Llama 3 on April 18, 2024, it did so under the "Meta Llama 3 Community License". While this license was free to use for the vast majority of developers, it contained strict limitations. For instance, the model could not be used by organizations with more than 700 million monthly active users without a special, separate commercial license from Meta. Additionally, it was mandatory to display the attribution "Built with Meta Llama 3" and the model name had to begin with "Llama 3". A crucial part of that agreement was the embedded Acceptable Use Policy, designed to prevent misuse. The most debated point at the time, however, was the prohibition against using the weights or generated output to train or improve other large language models. According to the [Llama 3 license text](https://github.com/meta-llama/llama-models/blob/main/models/llama3/LICENSE) this was legally watertight to prevent competitors from directly benefiting from Meta's R&D efforts, which sparked heated debate in the tech world at the time about the true meaning of openness. ## The OSI's legal stance Despite its widespread use within the community, the Open Source Initiative (OSI) refused to recognize Meta's licenses as true open source. In their publication, they made it crystal clear that the license suffered from fundamental shortcomings. According to the [OSI blog of February 18, 2025](https://opensource.org/blog/metas-llama-license-is-still-not-open-source) the Llama license fails on three counts: Freedom 0 (the lack of unconditionally free use), OSD point 5 (discrimination against certain users or applications), and OSD point 6 (restrictions on the fields of endeavor for the technology). To provide a formal foundation for this high-profile discussion, the OSI published the [Open Source AI Definition (OSAID) v1.0](https://opensource.org/ai/open-source-ai-definition). Since then, this definition has served as the definitive international benchmark. A model may only call itself open source if it provides, among other things, detailed information about the training data used — although releasing the raw training dataset itself is not always mandatory due to privacy and copyright concerns. ## What OSAID v1.0 specifically requires from open models The introduction of OSAID v1.0 brought a sharp demarcation to the market. Where marketers previously dared to label almost any model with publicly accessible weights as "open source," OSAID demands a holistic approach to openness. It is not merely about the binary weights (the parameters), but about a complete ecosystem of transparency. This raises the question of why so many popular open-weights models fail to comply in practice. The primary stumbling blocks of OSAID v1.0 for many commercial open-weights models lie in data transparency. The standard requires the provider to offer a detailed description of the training data. This encompasses not only the size and composition of the dataset, but also the filtering techniques used, the data sources, and any steps taken to protect copyrighted material or personal privacy. Many major players refuse to disclose this granular information out of fear of legal claims from copyright holders or to safeguard their competitive edge against rivals. Consequently, from a legal standpoint, many models fall back into the "open-weights" category rather than true "open source." ## The Evolution Toward Llama 4 and the Disappearance of the Improvement Clause With the release of Llama 4 on 5 April 2025, Meta adjusted its licensing structure on a crucial point. The new "Llama 4 Community License" retained familiar elements from the past, such as the threshold of 700 million monthly active users, mandatory attribution ("Built with Llama"), and the embedded Acceptable Use Policy governed by the laws of the State of California. The most striking difference in the [Llama 4 license text](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) is, however, that the notorious clause prohibiting the use of the models or their outputs to improve other LLMs is entirely absent. In doing so, Meta responded to criticism from researchers and smaller enterprises looking to experiment with model distillation and hybrid architectures, simplifying experiments with model distillation and derivative models. ## From Open-Weights to MIT and Apache 2.0: DeepSeek and Qwen While early generations of open models almost exclusively operated under proprietary, restrictive community licenses, the market took a different path in 2025. The launch of DeepSeek-R1 in January 2025 stood out because the model combined state-of-the-art performance with the permissive MIT license, as stated on the corresponding Hugging Face model card. This eliminated numerous legal hurdles for commercial implementation. Not long after, Alibaba followed with the introduction of Qwen3 in April 2025. The Qwen3-30B-A3B model was released directly under the Apache 2.0 license. These developments ensured that developers looking for flexible building blocks can increasingly turn to overviews such as those found under [open-source LLMs and the most prominent models](https://directory.llmnet.nl/en/open-source-modellen), where legal conditions have been greatly simplified and risks are kept to a minimum. ## Mistral 3 and Standardization on Apache 2.0 In Europe as well, the trend from tightly controlled licenses toward open standards continued. When Mistral AI announced the Mistral 3 family on December 2, 2025 — consisting of Mistral Large 3 (a Mixture of Experts model with 41 billion active parameters out of a total of 675 billion) and the compact Ministral 3 variants of 3B, 8B, and 14B — they opted for an open strategy. As shown in the [official Mistral 3 announcement](https://mistral.ai/news/mistral-3), all of these models were released under the Apache 2.0 license. With this move, the company definitively moved away from the more complex commercial frameworks of its early days, paving the way for large-scale industrial adoption within the European Union and beyond. ## The Impact of the European AI Act on Open Models Alongside the creators' license texts themselves, regulation plays an increasingly significant role in how open models are deployed within the European market. Article 53(2) of the European AI Act contains a specific and valuable exception for open-source and free models. Providers of General Purpose AI (GPAI) models published under an open-source license whose parameters, weights, architecture, and usage information are publicly accessible are exempt from several heavy administrative obligations. To understand precisely how this exemption works, it is useful to look at what is and is not covered by the regulation. The Article 53(2) exemption relieves open-source providers from the obligation to draw up detailed technical documentation in accordance with the standard GPAI requirements that apply to proprietary models. However, this exemption is not absolute: as soon as an open model is classified as having systemic risk — based on the statutory threshold of more than 10 to the power of 25 FLOPs of cumulative training compute — part of the exemptions lapse, and stringent reporting and evaluation obligations to the European AI Office still apply. Furthermore, copyright policy compliance under Article 53(1) remains fully in force for all models, regardless of their open license. ## Implications for Dutch Builders: Reading Model Cards and Fine-Tuning For a Dutch developer, startup, or enterprise seeking to deploy an open model or fine-tune it for specific use cases, this shift carries far-reaching practical consequences. Simply downloading weights from somewhere and hoping for the best is legally risky. Anyone looking to build seriously must use a structured checklist when analyzing a model card. When assessing a model card, pay attention to the following core aspects: - License type: Check whether it uses a permissive license (such as MIT or Apache 2.0) or a restrictive community license (such as older Llama variants with user thresholds). - Geofencing and usage restrictions: Some licenses specifically exclude certain jurisdictions or military applications, which can directly affect your business model. - Distillation and training restrictions: Check whether the license explicitly allows using the model to generate synthetic data or train smaller derived models. - Attribution and branding: Pay attention to whether you are required to display specific disclaimers or brand names within your application's user interface. ## How to check licenses in practice Verifying the legal status of an AI model requires a methodical approach. Because repositories on platforms such as Hugging Face or GitHub can change rapidly, it is wise never to rely blindly on third-party summaries. Always start by directly inspecting the file named LICENSE or LICENSE.txt in the root of the model repository. Check whether the stated license is identical to the officially recognized texts of the Open Source Initiative or the Free Software Foundation. In addition, it is advisable to review the accompanying documentation and the legal/compliance section in the model card. Pay close attention to any addenda or custom terms the creator may have added. If you are uncertain about the implications of a specific clause for your commercial application in the Netherlands, consult a legal professional specialized in open-source compliance and intellectual property. By adopting a proactive and thorough approach, you avoid legal claims down the road and build on a reliable foundation. ## Future outlook and broader trends The shift from restrictive community licenses toward established open-source standards like MIT and Apache 2.0 marks a maturation of the AI industry. Whereas companies in the early era of generative AI attempted to cover every legal angle with bespoke terms, market forces and regulatory requirements are now demanding clarity. Anyone following the broader trajectory of open technologies will see that the focus is moving from purely owning model weights to data transparency and compliant documentation. For organizations looking to capitalize on this dynamic, it is crucial not to rely blindly on outdated assumptions. Checking license terms with every new model release remains essential, as legal departments at tech giants continue to refine their conditions. However, those who opt for models licensed under Apache 2.0 or MIT build on a sturdier and far more predictable foundation than was the case in the early days of Llama 3.