AI Models & Companies · Open-Source AI Models

What's the difference between 'open-source' and 'open-weight' AI models

A truly open-source AI model shares its training data, code, and methodology in addition to its final parameters, while an open-weight model releases only the trained parameters needed to run it — a meaningful difference for anyone trying to understand or reproduce how a model was built.

Key takeaways

Open-weight models release the trained parameters needed to run the model, but not necessarily the training data or full methodology.
Fully open-source models additionally share training data and code, allowing genuine reproduction and deeper inspection of how the model was built.
Most widely used 'open' AI models today are technically open-weight rather than fully open-source in this stricter sense.
The distinction matters most for research reproducibility and understanding training data provenance, less so for simply using the model.

Why These Terms Get Used Almost Interchangeably

In everyday conversation, ‘open-source AI model’ and ‘open-weight AI model’ are often used as though they mean the same thing, but there’s a real technical distinction between them that matters for anyone trying to genuinely understand or reproduce how a specific model was built.

What ‘Open-Weight’ Actually Means

An open-weight model releases the trained parameters — the model’s actual ‘weights’ — needed to run it yourself, which is enough to use, fine-tune, and deploy the model, but doesn’t necessarily include the training data, the training code, or a full description of the methodology used to produce it.

What Fully Open-Source Adds

A fully open-source model, in the stricter sense, additionally shares the training data, training code, and methodology — the components needed to genuinely reproduce the model from scratch, not just run the finished result, which enables a much deeper level of research inspection and independent verification.

Why Most ‘Open’ Models Today Are Open-Weight, Not Open-Source

Most of the widely used models commonly described as ‘open-source’ — including major releases from large AI labs — are, in this stricter technical sense, actually open-weight rather than fully open-source, since companies frequently keep the training data and exact training process proprietary even while releasing the resulting weights freely.

Bottom Line

Open-weight models release the finished parameters needed to run them, while fully open-source models additionally share the training data and process needed to reproduce them — most models casually called ‘open-source’ today are technically open-weight, which matters most for research reproducibility rather than everyday use.

Go deeper

Sources

[1]Hugging Face — Hugging Face
[2]Llama — Meta

Written by Editorial Team

Last updated August 7, 2026

Get one well-sourced answer a week

No spam. Unsubscribe anytime.