The difference between an open-weight AI model, a genuinely open-source model and a closed model comes down to what developers can download, inspect, change and legally redistribute. The Open Source Initiative’s October 2024 standard offers a practical test.

“Open model” is a broad, sometimes loosely used description; open-weight means the trained parameters are available, while open-source AI has stricter technical and legal requirements.

Open Source Initiative executive director Stefano Maffulli discusses the Open Source AI Definition and the challenges of transparency in a January 2025 interview. Video: OpenObservability Talks.

Open-weight models: control without the complete recipe

Downloadable weights let a developer run inference on local GPUs, choose a cloud provider and sometimes fine-tune a model. OpenAI’s gpt-oss-20b and gpt-oss-120b are Apache 2.0 open-weight examples; Meta’s Llama models also distribute weights, but their community licenses impose restrictions that OSI says disqualify them as open source. The training datasets or full training pipeline need not accompany an open-weight release.

That distinction matters for teams following Reflection AI’s open-weight model or open-weight video-generation releases: local deployment is not proof that independent researchers can reproduce the original training process.

The Linux Foundation’s October 2024 discussion examined the draft Open Source AI Definition shortly before OSI finalized version 1.0. Video: The Linux Foundation.

Open-source AI: weights, code, information and freedoms

Under OSI’s definition, users must be free to use, study, modify and share the system, with access to its parameters, training and inference code, and sufficiently detailed training-data information. It does not require publishing every raw training file. Ai2’s OLMo 2 family illustrates the fuller approach through released weights, code, data, training recipes and evaluations.

Closed models: convenience in exchange for control

Proprietary models such as the hosted versions of OpenAI’s GPT, Anthropic’s Claude and Google’s Gemini typically expose chat products or APIs rather than downloadable production weights. Providers handle serving and upgrades, while customers surrender underlying model control. Tools that route coding tasks among multiple hosted models may offer choice without making those models open.

Which approach wins? It depends on the job

For sensitive documents, a properly secured self-hosted model may offer stronger data-residency control. For startups, a hosted API may avoid GPU purchases and maintenance. Researchers seeking to test how a model was trained need more than weights. The final decision should compare license terms, computing costs, measured task accuracy, security and deployment support—not the word “open” alone. Techniques such as retrieval-augmented generation work across all three approaches.

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