01In plain English
An open-weight model is one whose trained parameters, its "weights", are published for anyone to download and run. That is not always the same as open source: the training data and code may still be private, and the license may limit how you use it.
02How it works
A model is a very large file of numbers, called weights, that holds what training taught it. A lab such as Meta or Mistral can publish that file so anyone can download it. You then run it on your own computer or on rented servers, using free software that loads the weights and answers prompts. Because the file is on your side, you decide where data goes, and you can adjust the model with your own examples. The trade-off is that you also supply the hardware and the technical know-how. It is like receiving a finished engine you may install in your own car, rather than renting a ride from a taxi company.
03Why it matters when you are choosing
Open weights let a company run a model on its own servers, keep data in-house and avoid depending on one provider.
04What to check
Read the license for commercial limits, and check what hardware you need to run the model well.
05Where you will meet it
06Common questions
Is open weights the same as open source?
No. Open weights means the trained model file is public. Open source usually means the code, and often the training data, is public too, under a license that allows broad reuse. Many open-weight models keep the data private and add license limits, so read the license before relying on one.
Can I use an open-weight model for free in my business?
Often, but not always. The download is free, yet running it costs money for hardware or hosting, and some licenses limit commercial use or large companies. Check the license text, and budget for the people and servers needed to run it.
Are open-weight models as good as paid ones?
Some come close on many everyday tasks, while the very best closed models usually keep a lead on hard problems. The gap changes over time. Test an open model on your own work, since quality matters less than whether it is good enough for your use.