Open Source Model is an AI model whose design and trained parameters are made publicly available, so anyone can download, use, modify, and run it themselves — often for free.
What it means in plain English
AI models fall broadly into two camps. Open source (or “open weight”) models are released publicly: you can inspect them, run them on your own hardware, fine-tune them for your needs, and use them without depending on a single provider. This transparency and control contrasts with closed models, which you can only access through a company’s API. Open models have driven a huge amount of innovation and made powerful AI more accessible.
The trade-off is that running them yourself requires more technical effort and hardware.
A simple example
A developer who wants full control and privacy might download an open source language model and run it on their own servers — customising it and keeping all data in-house — rather than sending requests to a closed model’s API.
Why it matters
Open source models democratise access to AI, enabling research, customisation, privacy, and independence from any single vendor. The balance between open and closed models is one of the defining debates shaping the AI landscape.
Related terms
- Closed Model — the proprietary, API-only counterpart.
- Foundation Model — many open models are foundation models.
- Fine-Tuning — a common use of open source models.
Frequently asked questions
What is an open source model?
It is an AI model whose weights (and often code and details) are publicly released, so anyone can download, run, inspect, fine-tune, and often self-host it.
What are the benefits of open models?
Transparency, control, privacy (self-hosting), customisation, and no per-call API cost — though you supply the compute and expertise to run them.
