Open Source Series · What Open Source Actually Opens

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.

Six vendors, six calculations
MetaOpen weights
It does not make money selling models; its core business is social advertising. Turning the model into a free commodity pulls the pricing floor out from under its rivals. Economists call this commoditizing your complement: the cheaper the complement, the more valuable your core business.
GooglePartially open
Small models go open source to win developers, while the flagship stays closed and monetizes through the API and cloud services. Two legs to stand on: it keeps a foot in the ecosystem without giving up pricing power over its strongest capability.
MistralGenuinely open
Apache 2.0 on the base models builds technical credibility, while revenue comes from enterprise customization and on-premise deployment contracts. It earns from services, not from API lock-in.
Alibaba · QwenFully open
Open source establishes a de facto standard: developers fine-tune and deploy, and eventually a large share of that compute demand comes back to the cloud. Free models, paid compute. That path only works if the ecosystem is large enough, which is why it has stayed aggressive on size coverage and release cadence.
DeepSeekMIT licensed
It uses MIT, about as permissive as licenses get, and explicitly allows distilling from its output. Very little marketing spend buys technical credibility worldwide: one release draws more attention than an equivalent budget spent on advertising.
OpenAIFully closed
It started as a nonprofit doing open research and moved to a closed commercial model. The strongest capability is the moat, and open-sourcing would fill the moat in, which conflicts directly with a subscription and API business.
The pattern: whether a company open-sources depends on where the model sits in its revenue structure. If the model is the product, it tends to stay closed. If the model is a customer-acquisition channel or a complement, it tends to be opened.

What does that pattern imply for you? Pick whichever revenue model is closest to how your own company makes money.

How does your company make 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 weights, trained something new, and released it. That number cannot be faked, because behind every derivative model sits a real investment of compute.

200K
Derivative models
The first open-source model family in the world to reach this order of magnitude
1B
Cumulative downloads
About 1.1 million per day
~400
Models open-sourced to date
Since 2023, covering text, multimodal, code, and other directions
Figures are Hugging Face platform statistics for the Qwen family as of 2026-01-21, reported by Sina Finance, Yicai, East Money, and others. For comparison, the family's derivative model count stood at roughly 90,000 in February 2025 and passed 100,000 during 2025. Verified 2026-08-07.

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.

An example that hasn't played out yet

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.

Qwen3.8-Max · released 2026-08-03
The release date and parameter counts are as reported by multiple outlets. Verified 2026-08-07: the model has not yet appeared under Qwen's official Hugging Face account, and no license has been announced. This page will be updated once the weights are actually released.
"Announced as open source" and "already open source" are two different things. This is not splitting hairs. Until the weights are out, nobody knows what license will come with them — a standard permissive one that allows commercial use with no strings attached, or a custom agreement with user-count caps and restrictions on how it may be used. That single detail decides whether it counts as open source for you.
Back to the three-tier table from the previous section — this time you make the call. Right now it does not fit into any tier: the weights are not out and the license has not been announced, so both pieces of information you would need are missing. When the weights do land, open the license and check whether it restricts commercial use, whether it caps user counts, and whether it allows training other models on the output. If you can file it yourself at that point, you have learned what this section is teaching.

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.

For the people making the selection

Use this section together with the previous one:

Start with the license to confirm whether you can use it and what conditions apply.
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.
Concentration has a price. When two hundred thousand derivative models all grow out of a handful of bases, they inherit the same biases, the same phrasing habits, and even the same mistakes together. That is the problem the second half of this chapter deals with specifically.