Key takeaways
- Availability: model weights are downloadable.
- Transparency: source code is often shared.
- Flexibility: users can adapt and improve the models.
- Examples: Llama (Meta), Stable Diffusion.
Open vs closed: what are the differences?
To understand the debate, both approaches must be defined.
Open AI models
- Availability: model weights are downloadable.
- Transparency: source code is often shared.
- Flexibility: users can adapt and improve the models.
- Examples: Llama (Meta), Stable Diffusion.
Closed AI models
Boundaries blur: Meta calls Llama ‘open source’ although it doesn’t fully match the strict definition.
- Control: access through paid APIs.
- Restrictions: providers limit the allowed use cases.
- Opacity: internal workings remain a black box.
- Examples: GPT-4 (OpenAI), Claude (Anthropic).
The performance gap
An Epoch AI study highlights that open models lag about a year behind the best closed ones.
This gap is strategic: it gives regulators a valuable window to analyze frontier AI capabilities before they become broadly accessible.
The promise and risks of openness
Open models democratize AI access and let a global community contribute. Transparency also makes it possible to audit models for bias and issues — impossible for closed systems.
But freedom has a cost: free availability of powerful models brings risks (malicious content, military use by adversarial actors). The core challenge remains governance.
AH
Author
AI HUB Editorial
Research Desk
