While the term “open-weight AI model” is nothing new it has been getting more media attention lately especially with the release of Kimi-K3. But what does open-weight AI model mean and why should business be paying attention to them?
What is an Open-Weight AI Model?
According to Stanford University, the definition of an open-weight AI model is, “an AI model whose core components are publicly released, allowing anyone to download it. This lets users run the model on their own computers and even modify it for their own specific needs.”
Open-Weight vs. Open Source
However, it is important to note that open-weight is not the same as open-source. Open-weight models allow users to download the trained weights, run the model on their own infrastructure, and customize it via fine-tuning for it to meet their needs. Open-source goes further. It also includes the code used to prepare the data, train, test, and run the model, along with documentation about the data and methods used to create it. This gives users what they need to understand how the model was built, modify it, and potentially train a similar model themselves.
Open-Weight vs. Closed Models
While open-weight models are not nearly as transparent as open-source models, they provide some ability to customize versus closed models such as OpenAI’s GPT series, Anthropic’s Claude series, and Google’s Gemini series where the underlying code, parameter weights and training data are hidden from the public.
Why are Open-Weight Models Becoming Popular?
So, why are open weight models becoming popular? Two of the biggest reasons come down to cost and privacy control. Open-weight models allow users to self-host the models which drastically cuts down on cost and doesn’t send their data to a 3rd party company.
Another reason comes down to performance.
According to Stanford University’s 2025 AI Index, the performance gap between leading open-weight and closed-weight AI models is shrinking from 8.04% in early 2024 to just 1.70% by February 2025. Many industry experts believe open-weight models will continue to improve and become a practical alternative to closed models for a growing number of business use cases.
It’s this dramatic increase in performance from the open-weight models that is starting to turn heads. This doesn’t mean that open-weight models are the better choice as the right approach depends on taking the time to go through an AI discovery session and determining the use case and strategic approach first.
What are the Risks of Open-Weight Models?
Some of the biggest concerns when using an open-weight model is determining how safe they are to use and the infrastructure costs of self-hosting.
Open-weight models are not inherently unsafe, but they often place more responsibility on the organization using them. If the model is self-hosted, the organization may be responsible for providing the computing power and storage, securing the infrastructure, controlling access, installing updates, monitoring performance, maintaining availability, and managing costs.
Organizations must also consider cybersecurity, licensing, intellectual property, data governance, model monitoring, and responsible AI.
Before deploying an open-weight model, businesses should ask: Where did the model come from? What does its license allow? What data will it access? Do we have the infrastructure and expertise to run it reliably? How will we secure, update, and monitor it? What will it cost as usage grows? Who is responsible for the model’s operation and its outputs?