Open Source Is a Business: What Each Vendor Is After
Six vendors' open-source strategies and paths to revenue; why the number of derivative models says more than download counts
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is the key idea behind “Open Source Is a Business: What Each Vendor Is After”?
Six vendors' open-source strategies and paths to revenue; why the number of derivative models says more than download counts
Make the claim earn its place. Use this page as a decision aid, not a definition to memorize. Connect the idea to one real task, one observable result, and one failure that would change your mind.
Write one question you could answer with evidence after trying this idea.
A conclusion that sounds complete but leaves the key assumption untested.
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 follow different strategies, and they open up to different degrees. Some release only the small sizes and keep the strongest one inside their own products — Google puts out Gemma and holds on to Gemini. Others release the strongest one directly: DeepSeek published R1's weights under MIT. So there is no single answer to whether a flagship can be opened. You have to go vendor by vendor, and model by model.
As it happens, there is an example still in progress that you can practice on.
- 2.4 trillion total parameters, 95 billion active
- The API is already available
- The company says the weights will be open-sourced
- As of the verification date, the weights have not been released, and the license it will carry has not been announced
One more thing worth flagging: you almost certainly cannot run this one. Weight files for 2.4 trillion parameters run to several terabytes, and in the calculator at the end of this chapter it will show up as won't run. "The weights are open" and "you can run it" are two different things, and that holds for any large model.
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.
Put “Six vendors, six calculations” back into its constraints
“What does that pattern imply for you?” shows that a model, license, access route, or leaderboard is information—not an answer outside context. The real choice depends on task, data boundary, latency, quality floor, and operating cost.
Write elimination criteria before chasing the top score
The comparison in “Download counts are easy to inflate, and leaderboard positions shift the moment you change the test.” should use the same real inputs while observing correctness, failure behavior, response time, and cost. A model leading a public leaderboard may still fail your license, privacy, or peak-latency constraints.
- 2.4 trillion total parameters, 95 billion active
- The API is already available
- The company says the weights will be open-sourced
Without a test set, there is no reliable winner
Start with “Use this section together with the previous one”: choose inputs that could genuinely change the decision and write down one counterexample that would reverse your choice. That is more useful than memorizing a single ranking.
From “Six vendors, six calculations” to “How to measure the real influence of an open-source ecosystem”
“Six vendors, six calculations” grounds the problem in “What does that pattern imply for you? Pick whichever revenue model is closest to how your own company makes money”. “How to measure the real influence of an open-source ecosystem” then moves it toward “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…”. Together, they show that the lesson is not just a conclusion to remember, but a claim with conditions.
Carry the judgment into the next situation
For model selection, write non-negotiable constraints from the real task first. Compare quality, failure behavior, latency, licensing, and cost on the same inputs; use a leaderboard only as a starting point.
- “Six vendors, six calculations”: What does that pattern imply for you? Pick whichever revenue model is closest to how your own company makes money
- “How to measure the real influence of an open-source ecosystem”: 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 closing point”: As of the verification date, the weights have not been released , and the license it will carry has not been announced
The final “The closing point” brings the discussion to “As of the verification date, the weights have not been released , and the license it will carry has not been announced”. The useful thing to carry forward is knowing which judgments must be revisited when input, scale, or risk changes.
I turned one judgment from this article into a small experiment I could run today. Knowing what to observe next is more useful than simply remembering the conclusion.
After reading this, I first looked for the conditions behind the idea instead of copying the method into a project. That order made the later trade-offs much clearer.
When this judgment reaches real work, which constraint should be added first? I am curious which step matters most between reading and the first practical attempt.
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