Despite the company’s name, OpenAI hasn’t dropped an open version of its AI models since GPT-2 in 2019. That changed on Tuesday, as CEO Sam Altman shared two new open-weights, reasoning AI models, named gpt-oss-120b (120 billion parameters) and gpt-oss-20b (20 billion parameters).
If open-weights is a new piece of AI jargon to you, don’t worry. In the simplest possible terms, open-weights is a category of AI models that power products like chatbots, image and video generators. But they are philosophically different from the technology underpinning the AI tools you might use now. ChatGPT, Gemini and Copilot are all powered by closed models, which means we have no real insights into how those black box machines work. Open-weights models give us a peek at the mechanical wizard behind the curtain, so to speak.
gpt-oss is a big deal; it is a state-of-the-art open-weights reasoning model, with strong real-world performance comparable to o4-mini, that you can run locally on your own computer (or phone with the smaller size). We believe this is the best and most usable open model in the…
— Sam Altman (@sama) August 5, 2025
You don’t need to be a developer or machine learning expert to understand how these open models work or even to run them yourself. Here’s everything you need to know about open-weights and open-source AI models.
What is an open-weights AI model?
All AI models have weights, which are characteristics or elements. Models are trained to give certain connections more weight, or value.
An open-weights model does exactly what its name implies — the weights are publicly available, as defined by the Federal Trade Commission. Developers can see these weights and how they’re used in the creation of AI models.
“Arguably, the most valuable thing in large [language] models is actually the weights. You can do a lot if you have the weights, which is somewhat different from traditional software,” Omar Khattab, assistant professor of computer science at MIT and researcher in its computer science and artificial intelligence lab (CSAIL), told CNET.
For example, a chatbot is built to be really good at predicting the next logical word in a sentence. It’s trained to string together words in its outputs that frequently show up next to each other in its training data, presumably in a logical order. Words that show up next to each other more frequently can be given more weight than words that don’t often appear next to each other.
These weights are just numbers but open-weights models also come with a map.
“In open-weights [models], you get the weights, which are these numbers, and you get how to map those weights into the neural network structure, so the layers of the neural network, in order to actually be able to run it,” said Khattab. The architecture of the model shows how a company structures its models, which is “incredibly valuable.”
Open-weights models are primarily aimed at developers, who can integrate the model into existing projects, like helping to build AI agents. For the “committed hobbyist,” as Khattab put it, you can use the specs to run the model locally on your laptop, which could help alleviate privacy concerns that come, for example, with using AI through a company’s mobile app. Researchers will also have a clearer look at how the AI works internally.
How do OpenAI’s new open-weights models stack up?
The new open-weights models come in two sizes, 120 billion parameters (128 experts and 128k context window) and 20 billion parameters (32 experts but the same 128k context window). Experts refer to the number of sub-neural networks a model has, and context windows describe how much information a model can process and include in its responses. Bigger numbers for both indicate a model is capable of more sophisticated answers and has more firepower.
In terms of performance, OpenAI is reporting that the 120B model “achieved near-parity” with its latest reasoning model, o4-mini on core reasoning benchmarks while running on a single 80 gigabyte GPU. The 20B open-weights model performed similarly to o3-mini and ran on a 16 gigabyte device — meaning, this smaller open-weights model could be run fairly well on laptops and some smartphones. (Like all AI models run locally, your speed will depend on your device’s firepower.)
The models will be available under the Apache 2.0 license, a type of open-source-friendly license. You can check out the more in-depth specs in the model card and paper on safety training, get tips from OpenAI’s developer guidelines and see the weights for yourself now on HuggingFace and Github.
CNET’s Imad Khan installed gpt-oss-20B on a PC specifically built to run local AI models.
Is open-weights the same as open-source AI?
Open-weights models are related to open-source AI, but they aren’t exactly the same. Open source as a concept refers to software that has no proprietary owners, whose source code is publicly available and can be used by most anybody under open-source licenses. The Open Source Initiative, a nonprofit that advocates for open-source software, defines open-source AI as “a system made available under terms that grant users the freedom to use, study, modify and share [it].”
An open-weights AI model isn’t the same as an open-source model. One way to think about the difference between the two is like with baking, Suba Vasudevan, chief operating officer at Mozilla.org and senior vice president at Mozilla corporation, told CNET.
“An open-weights model is someone giving you this baked cake and saying, ‘Oh, it’s made of flour, sugar and eggs.’ Those are the weights. But if someone gave you the entire recipe, and all the instructions and the exact details of how much of each ingredient went into it, that’s open source,” said Vasudevan.
For open-weights models, the types of things not disclosed are the data the model was trained on and the code used to train it. Training data is a point of contention between AI companies and the humans creating content; AI companies are ravenous for high-quality, human-generated content to refine and improve their models. Some companies collect this data through licensing agreements, but some publishers and creators have filed lawsuits alleging that AI companies are illegally acquiring their copyrighted content. (Disclosure: Ziff Davis, CNET’s parent company, in April filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.)
Training data, regardless of its origin, is one of the most valuable things an AI company has. But it likely won’t be included in any open-weights model release.
“I think because of sheer scale [of data], because of liability, because a lot of it is licensed and you are not supposed to share it, I think it’s probably fair to assume that no companies, for-profit at least, are going to be releasing that anytime soon, maybe ever,” said Khattab.
Truly open-source AI comes with more publicly available information, Vasudevan said. Open-weights models can be harder to retrain or inspect for bias without the additional information. “You’re still a fair bit in the dark about how it was built or what data shaped it,” Vasudevan said.
Watch this: How You Talk to ChatGPT Matters. Here’s Why
Why should I care about open-source AI?
These differences between open-source, open-weights and closed models likely won’t affect your average experience using an AI chatbot. But they’re important qualities for developers to consider when selecting and using certain AI models, and they’re important more broadly for us to understand the technology that is infiltrating our online lives.
There’s no guarantee that the inner workings of its open-weights models will reflect what’s inside its closed models, Khattab said. But for a company whose product is synonymous with gen AI, any light shed on how it works is sure to have an impact on the people who use it and those who design it behind the scenes. As people start to get into the weeds with the new models, we’ll learn more and see what effect it has on the industry going forward.
Overall, the underlying philosophy of open source is that technology gets better when more people can access it, like academics who can study it and security experts who can expose weaknesses. “You have to let people build, scale and innovate and be able to poke holes and make it better,” said Vasudevan.
For more, check out our beginner’s guide to ChatGPT and how AI is changing our search experience.
