If you follow AI news, you have seen the phrase “open source” attached to almost every major model release. But in the open weight vs open source AI debate, those two terms are not interchangeable, and the difference has real consequences for anyone building products on top of these models.
Most of today’s headline releases from Meta, Alibaba, and DeepSeek are open-weight models, not open source in the strict sense. Understanding that gap is essential if you are evaluating open-source AI models for commercial use.
This guide explains what each term actually means, what you get with each type of release, and how to read a model card before you ship anything.
What open weights actually means
An open-weight release gives you the final artifact of training: the model’s parameter files. You can download them, run inference locally, fine-tune them, and usually deploy them commercially under the license terms.
What you do not get is the recipe. The training dataset, the data-cleaning pipeline, and often the exact training code remain proprietary. You can use the model, but you cannot reproduce it.
Most popular “open” releases fall into this category. Meta’s Llama family, Alibaba’s Qwen series, and DeepSeek’s R1 and V3 checkpoints are all open-weight releases with varying degrees of license permissiveness.
What true open source AI means
A genuinely open-source model releases the full stack. That means the weights, the training data (or complete documentation of it), the training code, and the evaluation pipeline, all under a license that permits unrestricted use, modification, and redistribution.
The flagship example is AI2’s OLMo family. OLMo ships its weights, the Dolma training dataset, the full training and evaluation code, and detailed logs. Anyone can audit or reproduce the model from scratch.
Other projects push in the same direction. EleutherAI’s Pythia models, BigScience’s BLOOM, and AllenAI’s earlier work all released far more of their pipeline than the typical corporate release. Fully open models remain the exception, not the rule.
What you get with each type of release
The table below summarizes the practical differences between a typical open-weight release and a fully open-source model.
| Component | Open-weight model | Open-source model |
|---|---|---|
| Model weights | Downloadable | Downloadable |
| Training data | Not released, often undisclosed | Released or fully documented |
| Training code | Rarely released | Released |
| Evaluation pipeline | Partially described | Released |
| License | Custom terms, usage restrictions common | OSI-compliant, no field-of-use limits |
| Reproducibility | Impossible without the data | Possible end to end |
The middle rows are where the real divergence lives. Weights alone tell you almost nothing about what a model was trained on, which matters for compliance, bias audits, and legal risk assessment.
Why the distinction matters commercially
License restrictions and revenue thresholds
Open-weight licenses frequently carry strings attached. Meta’s Llama Community License, for example, requires companies with roughly 700 million monthly active users to negotiate a separate commercial license. That clause exists precisely because the license is custom, not a standard open-source one.
Other common restrictions include acceptable-use policies that prohibit certain applications, and clauses that restrict using model outputs to train competing models. None of these would be permitted under a true open-source license.
Derivatives and redistribution
If you fine-tune an open-weight model, your derivative usually inherits the original license terms. That can limit how you distribute the result, especially if you plan to sell access or embed it in a commercial product.
With a genuinely open-source model under an Apache 2.0 or MIT license, derivatives are yours. There are no revenue thresholds, no acceptable-use clauses, and no naming obligations beyond standard attribution. For a startup, that difference can decide which base model you bet the product on.
Auditing and compliance
Regulated industries increasingly need to document what their models were trained on. With an open-weight release, that documentation does not exist publicly, so your legal team is trusting the vendor’s summary. With an open-source model, the data pipeline can be inspected directly.
The OSI definition of open source AI
In late 2024, the Open Source Initiative published its Open Source AI Definition (OSAID), version 1.0. It applies the classic open-source principles to machine learning systems.
Under the OSAID, an open-source AI system must grant four freedoms: use the system for any purpose, study how it works, modify it, and share it with or without modifications. Crucially, studying a model requires access to the data information and training code, not just the weights.
By this standard, most open-weight releases fail. The OSAID explicitly notes that weights alone do not satisfy the “study” freedom because you cannot meaningfully understand or modify a model’s behavior without knowing how it was trained. This is why the OSI has pushed back on vendors marketing open-weight models as “open source.”
Real-world examples across the spectrum
The landscape is a spectrum rather than a binary. Here is where popular releases sit, from most closed to most open.
- Meta Llama 3.1 and 3.2: weights open, custom license with a revenue threshold and acceptable-use policy, no training data released.
- Alibaba Qwen 2.5 and Qwen 3: weights open, most sizes under Apache 2.0, training data and pipeline not released.
- DeepSeek V3 and R1: weights open under MIT, detailed technical reports, but training data remains private.
- Mistral models: mixed, some Apache 2.0 weights releases, others under restrictive research licenses.
- AI2 OLMo 2: weights, Dolma dataset, training code, and logs all released under permissive terms.
If you are comparing specific models in this space, our guide to the best open-source LLMs ranks the leading options by capability and license terms. For a head-to-head on the two most-discussed open-weight families, see our breakdown of DeepSeek V3 vs Llama 3.
How to read a model card before shipping
A model card on Hugging Face or a vendor’s site is your first line of due diligence. Here is a checklist to run through before committing to a model in production.
Check the license field first
Look for a recognized license identifier like Apache 2.0 or MIT rather than a custom vendor license. If the license is custom, read the full text and flag revenue thresholds, acceptable-use clauses, and output restrictions for your legal team.
Look for training data disclosure
Ask whether the card names the training datasets or links to documentation. Vague phrases like “a mixture of publicly available data” mean you cannot audit the data for copyright or privacy exposure.
Verify the checkpoint actually runs
Before integrating anything, pull the weights and run a smoke test locally. With Ollama, that takes one command:
ollama run llama3.1Confirm the model card’s claimed context length, quantization options, and tokenizer behavior match what you observe. Cards sometimes describe the research version rather than the released checkpoint.
Note what the card does not say
Missing sections are signal. If the card omits data sources, known biases, or evaluation methodology, assume those areas were not rigorously documented. Weight that against your use case’s risk profile.
Bottom line
Open-weight models give you the finished engine; open-source models give you the engine, the blueprints, and the factory. Most leading releases today are open-weight, which is genuinely useful but is not open source by the OSI definition.
For most builders, open-weight models under permissive licenses like Apache 2.0 or MIT are perfectly workable. Just read the license, check the revenue and use restrictions, and know whether your compliance requirements demand a fully open pipeline like OLMo’s before you ship.