Core answer
Start here. This is the direct spoken answer to practice first.
Overview
Output variation is expected model behavior, but the application still needs stable contracts and measurable quality.
An LLM produces probabilities for possible next tokens, and the decoding process may sample among them. That means the same visible prompt can lead to different token choices and different answers. Settings such as temperature influence the variation, but provider-side model updates, routing, numerical behavior, hidden system instructions, and changing retrieved context can also matter. A seed or low temperature may improve repeatability on some services, but it should not be treated as a universal guarantee.