Real vs. Fake Open Source: How to Read a License
Three questions that locate how open a model is; the same yardstick applied to Qwen, Mistral, DeepSeek, Llama, and API-only models
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is the key idea behind “Real vs. Fake Open Source: How to Read a License”?
Three questions that locate how open a model is; the same yardstick applied to Qwen, Mistral, DeepSeek, Llama, and API-only models
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.
You do not need to read the license end to end. Ask the following three questions in order and you will have placed almost any model on the openness spectrum.
Question 1Can you download the weight file?
Everything else depends on this. If a model is reachable only through an API, then whatever the marketing says, you never get the control described in the last section. This one question rules out more than half the field.
Question 2Can you use it commercially, and are there conditions attached?
This is where people get caught. Some licenses permit commercial use but cap your user base; others limit you to research. Skip this check before you build a product and you will be scrambling for a license right when your numbers start climbing.
Question 3Can you train new models on its output?
In other words, can you distill from it. This decides whether you can build a model of your own on top of it, which is what the next two sections unpack. Vendors disagree on this point more than any other.
Reading is not the same as knowing how to use it, so take a model you are actually considering and walk it through. The wizard below is the same three questions, one at a time, and it tells you which tier the model lands in.
Each vendor below gets the same four questions. The fourth one — whether the training data is public — is included to make one thing visible: in the world of large models, the word "open source" has already been redefined.
- YWeights downloadable
- YCommercial use, no user cap
- YCan be used to train new models
- NTraining data not disclosed
- YWeights downloadable
- YCommercial use, no user cap
- YCan be used to train new models
- NTraining data not disclosed
- YWeights downloadable
- YCommercial use, no application needed
- YDistillation explicitly permitted
- NTraining data not disclosed
- YWeights downloadable
- LSeparate license required above 700M monthly actives
- YCan be used to train new models
- NTraining data not disclosed
- NWeights not downloadable
- NNo local deployment
- NCannot be used for training
- NTraining data not disclosed
It is also worth noting that licenses change. In the 2.5 generation Qwen used a tiered license: most sizes were Apache 2.0, while 3B and 72B had separate terms. Only with Qwen3 did the whole family standardize on Apache 2.0. What matters during selection is the license of the specific version you plan to use, not your general impression of the company.
Next time you evaluate a model, walk through it in order:
2. Confirm the license for the specific size you intend to use; it can differ within one family.
3. If it is a custom license, look specifically for limits on user scale, use case, and geography.
4. Confirm whether training is permitted, since that determines whether fine-tuning and distillation stay open to you later.
The next section takes a different angle: why these vendors open-source at all. Open-sourcing costs money, released weights can never be taken back, and none of them is doing charity.
Put “Three questions, and you have your answer” back into its constraints
“You do not need to read the license end to end.” 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 “Everything else depends on this.” 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.
- Y Weights downloadable
- Y Commercial use, no user cap
- Y Can be used to train new models
Without a test set, there is no reliable winner
Start with “The next section takes a different angle: why these vendors open-source at all.”: 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 “Three questions, and you have your answer” to “Five vendors, one yardstick”
“Three questions, and you have your answer” grounds the problem in “You do not need to read the license end to end. Ask the following three questions in order and you will have placed almost any model on the openness spectrum”. “Five vendors, one yardstick” then moves it toward “Each vendor below gets the same four questions. The fourth one — whether the training data is public — is included to make one thing visible: in the world of large models, the word "open source" has already bee…”. 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.
- “Three questions, and you have your answer”: You do not need to read the license end to end. Ask the following three questions in order and you will have placed almost any model on the openness spectrum
- “Five vendors, one yardstick”: Each vendor below gets the same four questions. The fourth one — whether the training data is public — is included to make one thing visible: in the world of large models, the word "open source" has already bee…
- “The closing point”: Y Commercial use, no application needed
The final “The closing point” brings the discussion to “Y Commercial use, no application needed”. 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.
No discussion on this article yet.