Model snapshot
A pinned, versioned model release that will not silently move when a provider updates its floating model alias.
See it
What it is
A model snapshot is a named, fixed release of a model, usually identified by a date or version number. A floating alias can move to a newer release, while a snapshot lets your application keep targeting the same weights and behavior until you deliberately change it. The date in the ID is the tell: gpt-4.1-2025-04-14 is a snapshot, plain gpt-4.1 is an alias that can move under you.
Pin a snapshot when output shape, tool calls, safety behavior, or benchmark scores matter enough that a silent upgrade would be risky. Record the snapshot beside every eval result and production trace, then test a newer snapshot against the same cases before moving traffic.
Gotcha: fixed does not mean byte-for-byte deterministic. Sampling, backend changes, and parallel tool execution can still vary a response. Snapshots can also be retired, so pinning buys controlled migration time, not a promise that the release will be served forever.
Ask AI for it
Pin this OpenAI Responses API client to the model snapshot gpt-4.1-2025-04-14 instead of a floating alias. Put the snapshot ID in configuration, attach it to every trace and eval result, reject startup when it is missing, and add a migration command that runs the golden dataset against both the current and candidate snapshots before changing the production value.