A harness is the execution layer around a model: it holds the task, manages context across a long run, calls tools, streams events, allows interruption, and routes approvals to a human. OpenAI released its Codex harness — the non-interactive CLI, the SDK and the app-server — under Apache-2.0, so it can be forked and embedded in commercial products. The model weights were not released; the harness still calls a paid API, so the licence cost is zero and the running cost is not.
Open weights means the trained model file can be downloaded and run yourself, under whatever licence the publisher chose. Open source is a stricter legal standard requiring freedom to use, study, modify and redistribute without restrictions on field of use. Many widely used models are open weights but not open source.
On common benchmarks the best open-weight models now sit close to frontier commercial models, and for many routine tasks the difference is not noticeable. The remaining gaps show up in long-horizon reliability, tool use, very long contexts and safety tuning — and in the operational work of running them yourself.