---
title: "Open vs Closed AI Models: 5 Differences That Actually Matter"
url: "https://learnaitodayonline.com/open-vs-closed-models/"
description: "Open and closed AI models differ in licence, cost and control, not capability. What \"open weights\" really means, why Llama is not open source, and how to choose."
author: "Robert Waithaka"
published: "2026-06-04"
updated: "2026-09-04"
last_reviewed: "2026-09-04"
categories: ["AI News"]
tags: ["level-intermediate"]
site: "Learn Artificial Intelligence"
approx_tokens: 3190
---

# Open vs Closed AI Models: 5 Differences That Actually Matter

Open and closed AI models are usually explained as a capability gap, with closed models assumed to be better and open ones assumed to be cheaper. That framing is now largely wrong, and it was never the useful distinction. The differences that decide which one belongs in a project are the licence, where the data goes, and how the cost behaves as usage grows.

## Key takeaways
- Open weights means downloadable parameters. Open source, under the OSI definition, additionally requires the training code and enough detail about the training data to reproduce the run.
- Llama is not open source. Meta's licence requires a separate agreement above 700 million monthly active users, prominent "Built with Llama" attribution, and the Llama prefix on derived models.
- DeepSeek R1 ships under MIT and much of Qwen under Apache 2.0, both standard and unrestricted. For commercial work that matters more than a few benchmark points.
- Capability has largely converged. Licence, where the data goes, and how cost scales are the differences that still decide the choice.

## Most "Open" Models Are Not Open Source

The word "open" is doing two different jobs, and conflating them causes most of the confusion.

An **open weights** model publishes its trained parameters. Anyone can download the file, run it on their own [hardware](https://learnaitodayonline.com/what-hardware-do-you-need-to-run-ai-models-locally/), and inspect what it produces. That is a meaningful freedom, and it is the one most people mean when they say a model is open.

**Open source** is a stricter claim. According to the Open Source Initiative (2024), the Open Source AI Definition version 1.0 requires three things together. The first is the model parameters. The second is the complete source code used to train and run the system. The third is what the definition calls data information, meaning "sufficiently detailed information about the data used to train the system so that a skilled person can build a substantially equivalent system."

That third requirement is the one almost nothing satisfies. This is because publishing a full account of the training data invites both copyright scrutiny and competitive imitation, so most laboratories publish weights and stop there.

The practical result is a spectrum rather than two camps.

Table: how the three requirements of the Open Source AI Definition apply to models commonly described as open.

| Model | Weights published | Training code | Training data documented | Meets OSAID 1.0 |
| --- | --- | --- | --- | --- |
| Llama (Meta) | Yes | No | No | No |
| Qwen (Alibaba) | Yes | Partly | No | No |
| Mistral (open releases) | Yes | No | No | No |
| DeepSeek R1 | Yes | Partly | No | No |
| OLMo (Allen Institute) | Yes | Yes | Yes | Yes |

_Sources: Open Source Initiative, "The Open Source AI Definition v1.0"; the model cards and licences linked in the sources below._

Notably, the models most people name as open source examples sit in the "No" column, and the one that qualifies is the one almost nobody has heard of. OLMo, from the Allen Institute for AI, publishes its training corpus as well as its weights, which is what allows an outside researcher to reproduce the training run rather than merely use the result.

## The Licence Decides What You May Build

A downloadable model is not automatically a usable one, and the licence is where that gets decided.

Meta's Llama 3.3 Community License is the clearest example, because it is widely called open source while imposing conditions that no open source licence would allow. According to Meta (2024), any product with "greater than 700 million monthly active users in the preceding calendar month" must request a separate licence. The same licence requires that you "prominently display 'Built with Llama'" and that any model you derive from it carries "Llama" at the beginning of its name.

None of that is unreasonable for Meta to ask. It is simply not open source, because the Open Source Definition forbids terms that discriminate between users or restrict fields of use. A 700 million user threshold affects almost nobody in practice, and it is still a condition on who may use the software, which is the part that matters to the definition.

Other releases are genuinely permissive. DeepSeek R1 ships under the MIT licence and much of the Qwen family under Apache 2.0, both of which are standard, well understood, and impose no user threshold or naming requirement. For a commercial project, that difference is worth more than a few points of benchmark performance.

Figure below shows where the common licences actually sit, from most to least permissive.

![Chart comparing the licence terms attached to widely used open weight AI models, from MIT and Apache 2.0 through to custom community licences](https://cdn.sanity.io/images/gfihpee1/production/c887a31847c9cfeaa0ca07edc1681788fe747def-1362x826.png)

_Source: the model licences linked in the sources below, read September 2026._

For anyone building something commercial, the practical rule is short. Read the licence before the leaderboard, because a model you cannot legally ship is not a candidate whatever it scores.

Figure below shows how the two deployment shapes differ on the only axis that matters here, which is who holds the text.

![Diagram contrasting a closed API where text leaves for a provider's servers with an open weights model running locally where nothing leaves](https://cdn.sanity.io/images/gfihpee1/production/ecab60b7a9a7fc59634b35b59ee9fd9934dec32a-1350x624.png)

_Source: describes the two deployment shapes, not a measurement._

## Where the Data Goes

The most consequential difference has nothing to do with quality.

When a closed model is used, the text sent to it leaves the building. It travels to a provider's servers, is processed there, and is subject to that provider's retention policy and jurisdiction. Enterprise agreements usually promise that inputs will not be used for training, and that promise is contractual rather than technical.

When an open weights model runs on your own hardware, nothing leaves. This is because the computation happens locally, so there is no third party to trust, no retention policy to read, and no jurisdiction question to answer. For a law firm handling client files, a clinic handling patient records, or a company handling unreleased financials, that distinction can decide the question on its own regardless of cost or capability.

The trade is real and worth stating plainly, and our comparison of [local AI against cloud AI](https://learnaitodayonline.com/local-ai-vs-cloud-ai/) works through it in more detail. Running locally means owning the hardware, the updates, the security patching, and the failure modes. For most individuals and small teams, a closed API is simpler and the privacy exposure is acceptable. The point is that this is a decision about data governance, not a decision about model quality, and it is frequently made on the wrong axis.

## Cost Behaves Differently, Not Just Lower

Open models are widely assumed to be cheaper. The more accurate statement is that their cost has a different shape.

A closed API costs nothing to start and scales linearly with use. Every request is billed, so the bill grows with adoption and never stops. An open weights model reverses that. The upfront cost is hardware, which is substantial, and the marginal cost per request afterwards is close to the electricity it consumes.

Caption for the figure below, showing how the two cost curves cross rather than one being uniformly cheaper.

![Line chart showing API cost rising linearly with usage against the flat cost of self-hosted hardware after an upfront investment, crossing at moderate volume](https://cdn.sanity.io/images/gfihpee1/production/980421a957d581dbcc536bbcb4ffe5e2ac5a6088-1147x817.png)

_Source: illustrative shape based on published API pricing and the hardware requirements covered in our hardware guide. Not a measured comparison, since the crossing point depends entirely on model size, hardware and request volume._

Where the lines cross depends on volume, and for light or occasional use they may never cross at all. This is because the hardware capable of running a large model well costs more than most people will ever spend on API calls. The crossover argument only holds at sustained, heavy, predictable usage, which describes a production service rather than a person experimenting.

## Capability Is No Longer the Dividing Line

The assumption that closed models are simply better held firmly until around 2024 and has weakened considerably since.

Open weight releases now trade places with closed models near the top of public leaderboards, and the gap that remains is narrower than the licensing and privacy differences described above. Anyone choosing on capability alone is optimising the variable that has converged, while ignoring the ones that have not.

There is an important caveat, and it cuts against the leaderboards rather than against open models. A benchmark score measures performance on a fixed test, and those tests suffer from contamination and saturation problems severe enough that a score difference of a few points frequently means nothing at all. Our guide to [what AI benchmarks actually measure](https://learnaitodayonline.com/ai-benchmarks-explained/) covers why.

For a beginner deciding where to start, the honest recommendation is to begin with a closed API, because setup is trivial and the cost of experimenting is near zero. Our guide to the [best free local AI models](https://learnaitodayonline.com/best-free-local-ai-models/) covers which open weight models are worth starting with. Move to open weights when a specific reason appears, and the reason will usually be one of three things. Sensitive data that cannot leave your control, a usage volume that makes per-request billing painful, or a need to modify the model itself. Absent one of those, the extra work buys very little.

The framing worth keeping is that "open" is a property of the licence and the deployment, not a rank in a quality table. Anyone who treats it as the latter will pick the wrong model for the right reasons.

## Common questions

### Is Llama open source?

No, although Meta describes it that way. The Llama Community License restricts who may use it, requiring a separate agreement from any product above 700 million monthly active users, and the Open Source Definition does not permit terms that discriminate between users. Llama is open weights.

### Which models actually meet the Open Source AI Definition?

Very few. OLMo from the Allen Institute for AI is the clearest example, because it publishes its training corpus alongside the weights and code. Most releases described as open publish weights only, which the definition treats as insufficient.

### Are open models less capable than closed ones?

Not meaningfully, at the top of the range. The gap that existed until around 2024 has narrowed to the point where it is smaller than the licence and privacy differences, so choosing on capability alone optimises the variable that has converged.

---

## Sources

- [The Open Source AI Definition v1.0 (Open Source Initiative)](https://opensource.org/ai/open-source-ai-definition)
- [Open Weights: not quite what you've been told (Open Source Initiative)](https://opensource.org/ai/open-weights)
- [Llama 3.3 Community License Agreement (Meta)](https://developer.meta.com/ai/llama3_3/license/)
- [DeepSeek R1 model card and licence (Hugging Face)](https://huggingface.co/deepseek-ai/DeepSeek-R1)
- [Qwen model releases and licences (Hugging Face)](https://huggingface.co/Qwen)
- [OLMo, an open language model with open training data (Allen Institute for AI)](https://allenai.org/olmo)
- [Dolma, the open corpus behind OLMo (arXiv 2402.00159)](https://arxiv.org/abs/2402.00159)
- [EU AI Act, Article 53: obligations for general-purpose AI providers](https://artificialintelligenceact.eu/article/53/)

_Last reviewed: 4 September 2026. Model licences change with each release, and the OSAID compliance column reflects the licences as published in September 2026. Re-checked quarterly._