Open Source Is a Business: What Each Vendor Is After
Training a flagship model burns an enormous amount of compute, and once the weights are out they can never be pulled back. So why do so many companies still open-source? The answer has nothing to do with charity. Understand what each of them is counting on and your selection decisions get a lot steadier.
What does that pattern imply for you? Pick whichever revenue model is closest to how your own company makes money.
Download counts are easy to inflate, and leaderboard positions shift the moment you change the test. What the industry weighs more heavily is the number of derivative models: how many developers actually took the weights, trained something new, and released it. That number cannot be faked, because behind every derivative model sits a real investment of compute.
What this set of numbers really shows is that developers vote with their feet. When they choose which base to keep training on, they weigh whether the license is clean, whether the size lineup is complete, and whether the community already has a working toolchain — none of which has much to do with marketing. So the derivative model count is essentially a long-term record of user choice.
The six vendors above are counting on very different things, but on one point they were strikingly consistent: you can open-source the small models, but not the strongest one. Google released Gemma and kept Gemini. Meta gave Llama to everyone and kept its strongest capability inside its own products. OpenAI does not even publish architecture details. The reason never needed explaining: the flagship is the moat, and nobody fills in their own moat.
That rule held for more than three years. Then came this summer.
For the first time, a flagship's weights were released
Qwen3.8-Max was open-sourced. It is the first Max-tier flagship model with open weights, and there is no prior precedent for it.
What that means is that anyone can download a model at the current frontier scale, take it apart to see how it was built, modify it, and keep training on it, without asking anyone for permission. The capability gap between the open camp and the closed camp, the one everyone had assumed was structural, does not hold anymore.
Now a bucket of cold water: you almost certainly cannot run this. Weight files for 2.4 trillion parameters run to several terabytes, and the calculator at the end of this chapter will tell you it won't run. So its practical significance is not whether you can open it on your own machine. It lies in three other things.
Researchers can dissect a model at this scale for the first time
Models this large could previously only be guessed at from behind an API. Now the weights can be read directly, and for a lot of questions about how large models actually work, the conditions for verifying an answer exist for the first time.
Real on-premise deployment now has a much higher ceiling
Organizations whose data cannot leave the internal network used to be choosing among small models only. Now, as long as they can afford the hardware, their capability ceiling is in the same league as using the strongest API.
It can be a teacher
This is the one that matters most to you. A top-tier model with open weights means the teacher for distillation no longer has to be somebody else's API. The second half of this chapter covers distillation; come back to this then and weigh what it is worth.
Use this section together with the previous one:
Then look at the business logic to judge whether the vendor will keep open-sourcing. Companies that earn directly from the model can tighten their open-source policy at any time; companies for which open source is a customer-acquisition channel tend to be more stable.
Finally look at ecosystem depth. Plenty of derivative models and a complete set of community tools means you can find answers when you run into trouble, and that someone has already hit the bug ahead of you.