Open Source AI
The model and accompanying information grant the freedoms described by the current Open Source AI Definition.
Terminology and licensing guide
Open Source vs Open Weight AI Models
“Open source model” is common search language, but AI model openness has multiple layers. This guide separates open source AI, open-weight releases, weights available under restricted terms, and closed API-only models so teams can evaluate licenses and deployment risk accurately.
Open Weight
Weights are published, but license terms, training data transparency, code, redistribution, or usage freedoms may be narrower.
Restricted Weights
Weights are available but gated or governed by custom terms, acceptable-use policies, or redistribution limits.
Closed/API-only
The model is accessed through a provider API and the underlying weights are not published for independent use.
What To Review
Code license
Is inference, training, tokenizer, or serving code included and reusable?
Weight license
Can you use, modify, fine-tune, redistribute, or host the weights commercially?
Data transparency
Does the release provide enough information to understand training data and limitations?
Reproducibility
Can another party inspect or reproduce meaningful parts of the system?
Auditability
Can security, privacy, and compliance teams evaluate model behavior and supply chain?
Procurement fit
Do license obligations, usage restrictions, indemnity gaps, or export controls affect your deployment?
Registry Examples
These examples mirror the current open-weight model set highlighted on the main model ranking page. They illustrate terminology and license review, not a separate ranking.
| Artifact | Classification | License | Caveat |
|---|---|---|---|
| GLM 5.2 | open-weight | MIT | Current Z.ai open-weight coding option in Kilo; verify artifact terms before redistribution or fine-tuning. |
| MiniMax M3 | open-weight | Open weights | Current MiniMax frontier open-weight coding model; treat provider catalogue status separately from artifact rights. |
| Kimi K2.7 Code | open-weight | Modified MIT | Coding-focused Kimi release; custom terms should be reviewed for commercial deployment and redistribution. |
| DeepSeek V4 Pro / Flash | open-weight | MIT | Current DeepSeek V4 coding family in Kilo; Pro and Flash differ by serving profile and cost/performance tradeoff. |
| Qwen3 Coder Next | open-weight | Apache 2.0 | Current Qwen coding-agent example; permissive license does not remove the need to verify exact artifact and provider route. |
Primary References
FAQ
Is an open-weight model automatically open source?
No. Open-weight means model weights are available under stated terms. Open source AI requires broader freedoms and transparency; code, data information, license terms, and redistribution rights still matter.
Do open source AI definitions require publishing the raw training dataset?
No. Follow the current Open Source AI Definition rather than assuming raw dataset publication is always required. The definition focuses on the information and freedoms needed to understand, use, modify, and share the system.
Why does procurement care about the difference?
Commercial use, redistribution, fine-tuning, acceptable-use restrictions, data transparency, and auditability can differ even when weights are downloadable.