What's the real difference between an open-source LLM and a closed-source one?

The main difference comes down to access. With a closed-source model like ChatGPT or Claude, you use it through a website or an app. The company keeps the model's inner workings, training data, and code to themselves. An open-source model, like some versions of Llama or DeepSeek, shares its code and model weights publicly. You can download it, run it on your own computer, and even tweak how it works. It's a bit like the difference between streaming a movie on Netflix versus owning the DVD. One you just watch. The other, you can pause, rewind, and even remix if you have the right tools. But there's more to it than just access. Open-source models give you privacy. If you're handling sensitive data, running a model on your own machine means that information never leaves your control. I've found this is a huge deal for lawyers or doctors just starting with AI. Closed models, on the other hand, are generally more polished. They often have better safety filters and a smoother user experience right out of the box. You don't need to be a programmer to use them. A good example is DeepSeek-v3.2. It's an open model that's shown you can get performance close to the top paid models without spending a dime on API calls. But the trade-off is you need a pretty powerful computer to run it well. According to a 2025 O'Reilly report, 53% of companies are now exploring open-source models to avoid vendor lock-in and reduce costs. The choice really depends on whether you value control and privacy more than convenience and a polished interface. There's no single right answer for everyone.