# Open vs closed weights

URL: https://justuju.org/concept/open-weights/
Layer 5 · Foundation Models

Some models' weights can be downloaded and run anywhere; others can only be reached through the provider's service.

## D1 · Story

Some cake recipes are secret: you can only buy the cake at the shop. Others are published so anyone can bake the cake at home.
Closed AI models are like the shop cake. Open-weight models are like the published recipe.
Like: A secret recipe versus a published one.

## D2 · Picture

Closed models are used through an API or app. The provider controls updates, safety measures and how data is handled.
Open-weight models publish the trained weights. Anyone can download them, run them locally, fine-tune them or inspect them, under the terms of their licence, which ranges from permissive to restrictive.
Open weights aren't the same as open source: the training data and full training code are often not released.
Like: A secret recipe versus a published one. Where it breaks: A published recipe lists every ingredient. Most open-weight models publish the finished weights but not the data or full process, so you can bake the cake but not recreate how the recipe was developed.

## D5 · Frontier

D2 separates open and closed releases. The disputes are about how much extra risk open weights add, whether safeguards can survive, and what open should mean.
Marginal risk. A broad review found research insufficient to judge the extra risk open models add over existing technology (Kapoor et al., 2024); a US government report recommended monitoring rather than restricting for now (NTIA, 2024).
Safeguards that survive. Released safeguards can be fine-tuned away. One method resisted hundreds of fine-tuning steps, but durable protection is unsolved (Tamirisa et al., 2024).
What counts as open source. The main definition requires detailed information about training data, not the data itself, a compromise for data that cannot legally be shared (OSI, 2024).
How far behind. Open models have lagged the best closed models by about a year; if the gap narrows, closed providers must compete on something other than raw capability (Cottier et al., 2024).

## Examples as of 2026-10-30

- Open weights: Llama, Qwen, DeepSeek, Mistral, Gemma, gpt-oss
- Closed weights: GPT, Claude, Gemini
- Fully open (weights, data and code): OLMo

## Sources

- The Open Source AI Definition 1.0 (Open Source Initiative): https://opensource.org/ai/open-source-ai-definition
- Dual-Use Foundation Models with Widely Available Model Weights Report (NTIA): https://www.ntia.gov/issues/artificial-intelligence/open-model-weights-report
- On the Societal Impact of Open Foundation Models (Kapoor, Bommasani et al.): https://arxiv.org/abs/2403.07918
- Tamper-Resistant Safeguards for Open-Weight LLMs (Tamirisa et al.): https://arxiv.org/abs/2408.00761
- How far behind are open models? (Cottier et al., Epoch AI): https://epoch.ai/publications/open-models-report


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