Open-Source AI vs. Open-Weight AI Models: What’s the Difference?

AI has an “open source” problem.

Open-source and open-weight AI models have moved into the mainstream, fueled in part by the rise of Chinese AI models from DeepSeek, Alibaba’s Qwen team, and others. Suddenly, the terms are everywhere.

We hear about open-source models. We hear about open-weight models. And increasingly, people use the two terms as if they mean the same thing.

They don’t.

Some of the best-known AI models that people routinely call open source are more accurately described as open weight. Their makers let developers download the model’s trained weights, run them on their own servers, fine-tune them, and build products around them. Yet much of what went into creating those models can remain behind closed doors.

The distinction has been years in the making.

The roots of today’s open AI movement stretch back to models such as OpenAI’s GPT-2, released in stages in 2019, followed by community-driven projects including EleutherAI’s GPT-J in 2021 and BigScience’s BLOOM in 2022. Meta’s LLaMA arrived in 2023 and helped push downloadable model weights into the AI mainstream.

Then came DeepSeek-R1 in January 2025.

The Chinese AI lab released the weights for its reasoning model under the MIT License, allowing commercial use and modification, after reporting performance competitive with leading models across several math, coding, and reasoning benchmarks. The release stunned Silicon Valley, challenged assumptions about the cost of building advanced AI, and prompted investor Marc Andreessen to call it “AI’s Sputnik moment.”

Yet DeepSeek-R1 raised another question that received far less attention:

Was it actually open source, or was it open weight?

That question now reaches far beyond DeepSeek. Models from Meta, Alibaba’s Qwen team, Mistral AI, Ai2, and other developers sit at different points on the openness spectrum. Goldman Sachs has begun discussing “open-source and open-weight models” separately when examining AI economics, suggesting the distinction is moving beyond developer circles.

And the difference isn’t academic. It can determine what developers can do with a model, how much researchers can inspect, whether businesses can modify and deploy it independently, and whether anyone outside the original developer can meaningfully reproduce how it was created.

So what exactly separates an open-source AI model from an open-weight one?

A good place to start is with a simple rule:

Don’t ask whether an AI company calls its model open. Ask what it actually opened.

What is an open-weight AI model?

To understand open-weight AI, you first need to know what model weights are.

During training, an AI model learns patterns from enormous amounts of data. That learning gets encoded into numerical parameters known as weights. A large language model can contain billions, and in some cases hundreds of billions, of these parameters. Together, they shape how the model processes an input and produces a response.

Think of the weights as the model’s learned state after training. They are a major part of what makes a trained model useful.

An open-weight model makes those learned parameters available for people to download. That gives developers far more control than a closed model accessible only through an API.

A company can take an open-weight model, run it inside its own data center or cloud account, adapt it to a particular task, build software around it, and keep sensitive prompts and data within infrastructure it controls. The exact rights depend on the model’s license.

The distinction has become important enough to reach Washington. In July 2026, Nvidia, Microsoft, Meta, IBM, Mistral AI, Hugging Face, Mozilla, Palantir, and more than 20 other organizations urged the Trump administration to avoid “premature restrictions” on open-weight AI. The coalition described open-weight models as “AI models that anyone can download, inspect, modify, and run on their own infrastructure.”

The same distinction is now showing up on Wall Street. In a recent Goldman Sachs discussion about AI investment, Chief Investment Officer Sharmin Mossavar-Rahmani asked Jim Covello, Goldman’s head of global equity research, to explain the difference between open-source and open-weight models.

“At its purest form, open source means everybody has access to the code. Everybody can share, everybody can edit, and you can use it for free,” Covello said. Open weight, he added, is “a derivation of that where you can use the output, but the source and the code are proprietary to the designer.”

Goldman’s explanation captures the broad economic distinction, but the technical definition gets more complicated. Open-weight releases can provide access to a model’s trained parameters, not merely its output, and the rights to modify, redistribute, or commercially deploy those weights depend on the license. Open-source AI faces a higher bar under the Open Source Initiative’s definition.

That brings us to the harder question: What actually makes an AI model open source?

What open weight does not necessarily give you is the complete process that created the model.

The original training dataset may remain unavailable. The developer might release little information about how they selected, filtered, or labeled that data. Parts of the training code and data-processing pipeline may stay private. The license may limit commercial use, redistribution, or certain applications.

That creates an important distinction.

You can possess and modify the trained model without having everything needed to recreate how the model was trained.

The Open Source Initiative describes AI weights as the learned parameters that work with a model’s architecture to produce outputs. Under OSI’s stricter Open Source AI Definition, releasing those parameters alone is not enough to make an AI system open source. An open-source system must provide broader freedoms and access to the preferred form needed to modify it.

This is why open weights and open source should not be treated as synonyms.

Open weights can still give developers and businesses substantial control. They can reduce dependence on hosted APIs, enable private deployment, and allow customization. For many companies, those benefits may matter more than having every ingredient used during pretraining.

But access to the finished weights answers only one question about openness.

The next question is much bigger: What would an AI developer have to release for the model to truly qualify as open source?

What is an open-source AI model?

Open source has a much longer history than generative AI. For decades, open-source software has allowed developers to inspect source code, modify it, redistribute it, and build new software on top of it.

AI makes that idea harder to define.

Programmers largely write traditional software. Modern machine-learning models are trained. Their behavior emerges from code, data, model architecture, training methods, and vast collections of learned parameters. Opening the source code alone does not reveal everything that went into creating the finished system.

The Open Source Initiative, the organization behind the widely used Open Source Definition for software, spent years working with researchers, developers, lawyers, and other participants to address that problem. Its Open Source AI Definition 1.0 sets out four basic freedoms: people must be able to use, study, modify, and share an AI system.

Those four words sound simple. The requirements behind them are much more demanding.

For machine-learning systems, OSI says users need access to what it calls the “preferred form to make modifications.” That includes three broad pieces: data information, code, and parameters.

Data information means sufficiently detailed information about the material used to train the system so that a skilled person could build a substantially equivalent system. That includes details such as the provenance and characteristics of the data, how it was obtained and selected, labeling procedures, and the methods used to process and filter it.

Code means the complete source code used to train and run the system. That can include data-processing code, training and validation code, inference code, supporting libraries, model architecture, and the settings used during training.

Parameters include the model weights and other configuration information produced through training.

There is an important nuance here. An AI developer does not necessarily have to publish every byte of its original training dataset for a system to meet OSI’s definition. Some training data cannot legally be redistributed. What matters is providing enough information about the data, including where obtainable data can be found, for a skilled person to build a substantially equivalent system.

That sets a much higher bar than simply placing model weights online.

Under this framework, the question changes from “Can I download the model?” to “Do I have the freedoms and materials needed to meaningfully study, modify, share, and build upon the system?”

That is why a model can be remarkably open in practice, useful to millions of developers, commercially valuable, and still be more accurately described as open weight rather than open source.

The difference becomes much easier to see with a simple analogy.

A simple way to think about open source vs. open weight

One easy way to picture the difference is to think about a finished product and the process used to create it.

With an open-weight AI model, you get the trained model itself. Depending on the license, you can often download it, run it, adapt it, and build on top of it. But you may not receive the full training code, detailed information about the training data, or every part of the process that produced those weights.

With open-source AI, the goal is broader. Under the Open Source Initiative’s definition, users must have the freedom to use, study, modify, and share the system, along with access to the preferred form needed to make meaningful modifications. For machine-learning systems, that includes data, code, and model parameters such as weights.

A food analogy gets close.

An open-weight model is a little like receiving the finished cake and permission to alter it. An open-source AI system is closer to receiving the cake, the recipe, the preparation instructions, and enough information about the ingredients and process to make a substantially equivalent version yourself.

The analogy has limits, but it captures the central point: having the finished model is not the same as having access to how that model was created.

Open-source AI vs. open-weight AI: The key differences

The distinction becomes clearer when you place the two approaches side by side.

Feature	Open-weight AI	Open-source AI
Model weights available	Yes	Yes
Can be downloaded and self-hosted	Often	Yes, where the system is distributed as a model
Can be modified or fine-tuned	Usually, subject to license terms	Yes
Training code available	Not necessarily	Yes
Inference code available	Often, but varies by release	Yes
Detailed training-data information available	Not necessarily	Yes
Full original training dataset required	No	No
Can be studied in depth	Partially, depending on what is released	Yes
Redistribution rights	Depend on the license	Must allow sharing under open-source terms
Commercial use	Depends on the license	Must permit use for any purpose
Ability to recreate a substantially equivalent system	Often limited	The required information is intended to make this possible

The last row is one of the most important.

The Open Source Initiative does not require publishing every original training file. Its definition calls for sufficiently detailed information about the training data so that a skilled person can build a substantially equivalent system. That includes information about where the data came from, what it contained, how it was selected, how it was labeled, and how it was processed or filtered.

OSI requires the complete source code used to train and run the system, including relevant data-processing, training, validation, inference, and model-architecture code. It requires the model parameters, including the weights, under terms that preserve the freedoms to use, study, modify, and share the system.

That is a far higher bar than simply making a checkpoint available for download.

It is also why the label attached to a model can be misleading.

Two models may both be described online as “open,” yet one may expose little beyond its weights and inference code, while another publishes the training code, extensive data documentation, model parameters, and the information independent researchers need to build on the original work.

The difference is not how easy the model is to download.

The difference is how much of the system, and the freedom to work with it, is actually open.

Which AI models are actually open source?

This is where the terminology gets messy.

Meta, DeepSeek, Mistral AI, Alibaba’s Qwen team, Ai2, EleutherAI, and other developers have all released models that give outsiders far more access than closed systems. But calling them all “open-source AI” hides major differences in what each developer actually releases.

The Open Source Initiative’s validation work offers a useful starting point. While developing its Open Source AI Definition, volunteers evaluated a group of AI systems to see whether the definition could be applied in practice.

Five passed that validation phase: EleutherAI’s Pythia, Ai2’s OLMo, LLM360’s Amber and CrystalCoder, and Google’s T5.

Others did not.

The versions of Meta’s Llama 2, Microsoft’s Phi-2, xAI’s Grok, and Mistral’s Mixtral examined during the process did not pass because they lacked required components or their legal terms conflicted with open-source principles. OSI stresses that the exercise was part of developing and testing the definition, not a permanent certification program for AI models.

That distinction matters. A model that failed the exercise in 2024 does not automatically tell us how to classify every future model from the same company. Each release has to be judged by what was actually published and the terms attached to it.

Ai2’s OLMo shows what the more open end of the spectrum can look like. Ai2 releases OLMo 2 with accessible training data, training code, model weights, training recipes, evaluations, and intermediate checkpoints. The organization makes a point of distinguishing that approach from models that release only their final weights.

DeepSeek-R1 illustrates why the labels can get confusing.

DeepSeek released the R1 code repository and model weights under the MIT License, which permits commercial use, modifications, and derivative works. That makes R1 unusually permissive from a licensing standpoint. Yet releasing weights under an open-source software license does not by itself establish that the entire AI system meets the Open Source AI Definition. The broader question is whether the required data, information, training code, parameters, and freedoms are available.

This is why a simple table needs some caveats:

Model/system	Weights available	Broader training materials	Best description for this comparison
OLMo / Ai2	Yes	Extensive data, code, recipes and checkpoints	Open source / fully open
Pythia / EleutherAI	Yes	Extensive training artifacts	Open source
Llama 2 / Meta	Yes	Required components not fully available in OSI review	Open weight
Mixtral / Mistral	Yes	Required components not fully available in OSI review	Open weight
DeepSeek-R1	Yes, MIT licensed	Some methods and code disclosed, but not the complete training stack and data	Open weight under the stricter OSI framework

The safest rule is to classify the release, not the brand.

A company can publish one model with extensive training artifacts and another with little more than downloadable weights. A newer release can carry a different license from its predecessor. Terms can change. So can the amount of training information a developer chooses to disclose.

Calling Meta, DeepSeek, Mistral, or any other lab simply an “open-source AI company” can obscure those differences.

The better question remains the one at the heart of this article: What did they actually open?

Why do AI companies release weights but keep other parts closed?

If full openness gives researchers and developers greater visibility into how a model was built, why stop at the weights?

One reason is that the ingredients behind a frontier AI model can be enormously valuable.

The training corpus may represent years of collection, cleaning, filtering, deduplication, labeling, and human review. A lab may regard its data mixture as intellectual property. Some datasets carry copyright, privacy, contractual, or redistribution restrictions that make releasing the original material difficult or impossible.

The training process can also carry competitive value. Data-processing methods, model recipes, post-training techniques, reinforcement-learning systems, and internal tooling can reveal how a company achieved a particular level of performance.

Open weight offers a middle path.

A developer can give outsiders meaningful control over the finished model without publishing every part of the factory that produced it. Developers can run the model themselves, optimize it for different hardware, fine-tune it for specialized tasks, and build an ecosystem of applications around it. The model maker can benefit from that adoption without giving competitors a complete blueprint of its training operation.

Both sides have economic incentives.

Closed API models keep the provider at the center of every interaction and can generate revenue each time customers use them. Open-weight models shift more control to the customer. The developer may earn money elsewhere through cloud partnerships, enterprise services, hosting, support, premium products, or a larger ecosystem built around the model.

Open weights can create another advantage: distribution.

Once weights are downloadable, developers can quantize them, fine-tune them, optimize them for new hardware, package them into local applications, and create specialized derivatives. A model can spread far beyond the infrastructure of the company that trained it.

For businesses adopting AI, that can be highly attractive. They may care less about recreating a multibillion-dollar pretraining run and more about deploying a capable model privately, customizing it, controlling where their data goes, and avoiding dependence on a single API provider.

That helps explain why open weight has become such an important category in AI.

It sits between two very different models of access: a closed system you can use but not own, and a fully open system whose inner workings others can study and build on.

Why open source vs. open weight matters to businesses

For many companies, the debate over whether a model qualifies as open source can sound academic. The business consequences are anything but.

A company using a closed AI model through an API is relying on someone else to operate the model. The provider controls the underlying system, sets access terms, determines pricing, and can change or retire models. Customers send requests to the service and receive outputs.

Open-weight models change that relationship.

Once a company can download the weights, it can potentially run the model inside its own cloud environment or data center. Sensitive prompts, customer records, proprietary documents, source code, or internal data do not necessarily have to leave infrastructure the company controls.

That can matter in industries where privacy, security, data residency, and regulatory requirements shape technology decisions.

Cost is another factor.

A hosted API typically charges according to usage. Self-hosting shifts the economics. Instead of paying a model provider for each unit of inference, a company takes on its own compute, storage, engineering, maintenance, and energy costs. At sufficient scale, that trade can become attractive, but open weight does not automatically mean cheaper AI. The economics depend on workload, hardware utilization, model size, staffing, and how efficiently the system is operated.

Customization may be an even bigger draw.

Developers can fine-tune or otherwise adapt downloadable models for particular tasks, optimize them for specialized hardware, compress them to run on smaller machines, or build internal systems around them. The degree of freedom depends on the license, but organizations have more control than a closed API may provide.

Open-source AI takes the idea further. Access to training code, model parameters, and detailed training-data information can give researchers, companies, and governments greater ability to study how a system was built, modify it more deeply, and develop new systems from the work.

That difference can matter for technological independence.

A government building sovereign AI infrastructure, a university conducting model research, and a startup trying to ship an AI feature do not necessarily need the same degree of openness. The startup may simply want inexpensive weights it can deploy privately. The researcher may need training code and data information. A government may care about both.

That is why open source isn’t automatically better than open weights for every user.

The better question is what kind of control an organization actually needs.

For some businesses, access to the weights is enough. For others, the missing pieces matter most.

AI has an “open” label problem

That brings us back to where we started.

“Open source,” “open weight,” and the broader phrase “open model” are often used loosely across AI announcements, news coverage, research papers, model repositories, and online discussions. A model becomes downloadable, the word “open” gets attached to it, and the distinction can disappear.

That was easier to overlook when downloadable models trailed the most capable closed systems by a wide margin. It becomes harder to ignore as open-weight models compete more closely with proprietary systems and businesses make real infrastructure decisions around them.

The label can create false assumptions.

A developer may hear “open source” and assume the training code is available. A business may assume commercial use is unrestricted. A researcher may expect detailed information about the training data. Someone else may simply mean that the model weights can be downloaded.

Those are very different claims.

Licensing makes the issue harder. A model can have publicly downloadable weights yet impose restrictions that would conflict with traditional open-source principles. Another release may use a permissive software license for its weights but still withhold major parts of the training process. Calling both simply “open source” tells users very little about what they actually receive.

The Open Source Initiative created its Open Source AI Definition partly to bring greater precision to that conversation. The technology industry’s increasing use of the term open weight, including the July 2026 coalition backed by Nvidia, Microsoft, Meta, and others, gives the market another useful distinction.

Neither label should replace reading the license and examining the release itself.

A better test is surprisingly simple.

Are the weights available? Is the training code available? What information is provided about the training data? What does the license permit? Can you study, modify, and share the system?

Those questions reveal far more than an “open” badge on a model page.

And they lead to a rule worth remembering:

Don’t judge an AI model by how open its maker says it is. Judge it by what its maker actually opens.

FAQ: Open-source AI vs. open-weight AI

What is an open-source AI model?

Under the Open Source Initiative’s definition, an open-source AI system gives users the freedom to use, study, modify, and share it. For machine-learning systems, that requires more than downloadable weights. Users need access to the preferred form for making modifications, including model parameters, relevant code, and sufficiently detailed information about the training data.

What is an open-weight AI model?

An open-weight AI model makes its trained parameters, or weights, available for download. Developers can often run the model themselves, modify or fine-tune it, and deploy it on infrastructure they control. The training data, complete training code, and other parts of the development process may remain private.

What is the biggest difference between open-source and open-weight AI?

Open weight primarily gives you access to the trained parameters. Open source goes further by giving users the materials and freedoms needed to study, modify, and share the system.

Put another way, open weight opens the model. Open source opens much more of what went into building it.

Is Meta’s Llama open source or open weight?

Llama is better described as an open-weight model family rather than as an open-source family.

Meta has made weights for numerous Llama releases available under its own licenses, giving developers substantial freedom to run and modify the models. But Meta’s licensing and the materials released with Llama have not always met the stricter requirements associated with open-source AI. Llama 2, for example, did not pass the Open Source Initiative’s validation exercise during development of its Open Source AI Definition.

Check the exact Llama release and its license rather than assuming every model carrying the Llama name has identical terms.

Is DeepSeek open source?

DeepSeek-R1 is a good example of why the terminology is disputed.

DeepSeek licenses the R1 code repository and model weights under the MIT License. The company permits commercial use, modifications, derivative works, and distillation. Those are unusually permissive rights.

Yet an open-source license on the weights and repository does not automatically mean the complete AI system meets the Open Source Initiative’s definition. Under that stricter framework, the question extends to training code and sufficiently detailed training-data information needed to study and meaningfully modify the system.

For that reason, DeepSeek-R1 can be accurately described as MIT-licensed and open weight without assuming that every component of the AI system is open source.

Is Qwen open source?

There is no single answer for every Qwen model.

Alibaba’s Qwen family includes numerous models and releases, and their licenses and accompanying materials have varied. Some Qwen releases have used permissive licenses such as Apache 2.0. The safest approach is to identify the exact Qwen model, check its license, and review which training artifacts were released before deciding how open that model is.

Is Mistral open source?

It depends on the model.

Mistral AI has released model weights under different terms across its portfolio. Some releases use permissive licenses, but that does not automatically mean the full AI system meets the Open Source Initiative’s definition.

Mixtral, for example, was one of the systems examined during OSI’s validation work and did not pass that exercise because required components were missing. That result applies to the system examined at the time, not every model Mistral has released since.

Can open-weight AI models be used commercially?

Some can. Others may carry restrictions.

Commercial rights depend on the license attached to the specific model. DeepSeek-R1, for example, explicitly permits commercial use under its MIT License. Other models may impose usage conditions or additional requirements.

Downloadable does not automatically mean unrestricted. Read the license before deploying a model commercially.

Can I run an open-weight AI model locally?

Yes, in many cases. Downloadable weights let developers run models on their own workstations, servers, data centers, or cloud infrastructure.

Hardware requirements vary dramatically. A small model may run on a consumer computer, while a very large model can require multiple high-end accelerators and substantial memory.

Are open-weight AI models free?

Free to download and free to operate are different things.

A model may be available without a licensing fee yet still carry substantial inference costs. GPUs, cloud infrastructure, electricity, storage, engineering, security, and maintenance all cost money.

An open-weight model can reduce dependence on per-token API pricing, but it does not make compute free.

Does open-source AI require companies to publish their entire training dataset?

No.

The Open Source Initiative requires sufficiently detailed information about the training data so a skilled person can build a substantially equivalent system. That includes descriptions of the data, its provenance and characteristics, how it was selected, labeling procedures, processing and filtering methods, and information about where available or obtainable training data can be found.

Some underlying data may not legally be redistributable. OSI’s definition accounts for that distinction.

Is open-source AI safer than closed AI?

Neither label guarantees safety.

Open systems can let outside researchers inspect, test, modify, and study models in ways closed systems may not permit. The same accessibility can raise concerns about misuse or the removal of safeguards.

Safety and openness are related debates, but they are not the same question. A model does not become safe simply by being open, nor does a closed model become safe simply by keeping its weights private.

Why does open-source AI matter?

Open-source AI can give researchers, developers, businesses, and governments greater autonomy over technologies that are becoming part of critical digital infrastructure.

It can make independent research easier, reduce dependence on individual model providers, encourage competition, allow deeper customization, and give outsiders greater visibility into how AI systems are built.

Open-weight models can deliver many of those benefits without exposing the entire development process. That’s why knowing the difference matters.

Look beyond the “open” label

The AI industry is moving toward a future where “open” may describe many very different levels of access.

A model might let anyone download its weights but keep its training data and training pipeline private. Another might publish code, parameters, data documentation, training recipes, and intermediate checkpoints. Both may be called open in casual conversation, yet they give developers very different rights and very different visibility into how the technology was created.

That makes the label itself a poor substitute for asking what is actually available.

The next time an AI company announces an “open” model, ask five questions:

Are the weights available? Is the training code available? What information is provided about the training data? What does the license allow? Can people meaningfully study, modify, and share the system?

Those answers tell you far more than the word “open” in a model announcement.

Open-weight AI has already changed who can access and deploy advanced models. Open-source AI goes further by asking whether people can meaningfully study, modify, share, and build upon the systems themselves.

As the gap between closed and downloadable models continues to narrow, that distinction will matter more, not less.

The future of AI may be open. The bigger question is: how open?

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