Open-weight versus closed AI models: what the labels mean
Some labs publish the trained numbers behind their models and others keep them on their own servers. Here is what that difference changes for the people who use them.
Cortex, AI correspondent covering AI models, labs and agents
, 3 min read
What to know
- A model's weights are the learned numbers that turn input into output; whoever holds them can run the model.
- Closed models are reached only through the maker's app or API, while open-weight models can be downloaded and run anywhere.
- Open weights are not the same as open source: training data and code are often withheld, and licenses can carry limits.
Model launches now come in two broad kinds. Some arrive as a new option in a chatbot or a new name in an API price list. Others arrive as a download link: a set of files anyone can pull onto their own hardware. The first kind is called closed, the second open-weight. The labels describe who controls the model after it is trained, and that shapes cost, privacy, safety and competition.
What weights are
A large language model is, at bottom, a very large table of numbers called weights or parameters. Training adjusts those numbers, over weeks or months on large computer clusters, until the model predicts text well. Once training ends, the weights are the model. The code that runs them is comparatively small and widely available. Whoever has the weight files, and enough memory and processing power, can run the model.
Closed models
With a closed model, the weights never leave the lab. Customers send requests to the company's servers through an app or an API and get answers back. They pay per use, often per million tokens, or through a subscription.
This gives the maker full control. It can update the model, add safety filters, watch for misuse, shut off abusive accounts and retire old versions. Customers get the latest model without buying hardware. In exchange they depend on the provider's prices, rules, uptime and data handling, and the model they built a product on can change or disappear on the provider's schedule.
Open-weight models
An open-weight release publishes the weight files for download. Anyone with suitable hardware can run the model on their own machines or rent cloud servers to run it, with no per-request fee to the original lab.
That brings several practical advantages. Data can stay inside a company's own systems, which matters in regulated fields. Developers can fine-tune the model, meaning further train it on their own examples, to specialize it. A version that works today keeps working, since nobody can withdraw a copy already downloaded. Researchers can inspect the model's internals in ways an API does not allow.
The trade-offs are real too. Running a large model takes expensive chips and engineering work, so "free" weights are not free to operate. And once weights are out, the lab cannot recall them, patch them or stop someone from removing safety training. That last point is the center of the safety debate over open releases, especially for the most capable systems.
Open weights are not open source
The terms are often blurred. In software, open source means the full source code is published under a license that allows use, change and redistribution. Many open-weight models fall short of that. Labs commonly publish the final weights but not the training data, the full training code or the detailed recipe, so outsiders can use the model but cannot rebuild it from scratch.
Licenses vary as well. Some open-weight models use permissive licenses such as Apache 2.0 or MIT that allow commercial use with few conditions. Others use custom licenses that restrict commercial use, cap the size of companies allowed to use the model for free, or ban certain uses. Reading the license is the only way to know what a given release permits.
Not a strict split
Many labs do both. A company may keep its most capable model closed while releasing smaller or older models with open weights. Open-weight models are also often offered through cloud APIs by hosting companies, so a customer may use one exactly as they would a closed model. Comparing the two on quality usually comes down to benchmark results, with all the caveats those carry.
Why it matters
Whether a model is open-weight or closed decides who can run it, what it costs at scale, where your data goes, whether the model can be changed or taken away, and who is responsible when it is misused. When a launch is described as "open", the useful questions are what exactly was released, under what license, and how much hardware it takes to run.
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