# Model deprecation

**Source:** https://promtable.com/glossary/model-deprecation

> Model deprecation is the lifecycle event where a provider announces and eventually removes an LLM snapshot — production apps must migrate to a newer model or pinned version before the deprecation date or queries start failing.

---
Model deprecation is the lifecycle event where a provider announces and eventually removes an LLM snapshot — production apps must migrate to a newer model or pinned version before the deprecation date or queries start failing.

Every major LLM provider deprecates older snapshots: OpenAI typically gives 6-12 months notice (GPT-3.5 turbo legacy snapshots, instruct-davinci-003), Anthropic gives 6-12 months on Claude snapshots, Google on Gemini. The deprecation cycle: announcement → grace period → soft-fail (warnings) → hard-fail (404). Production playbook: monitor provider changelog, track which app code paths use which pinned model, eval the candidate replacement against your golden set, migrate with a feature flag, decommission old pin. Skipping any step risks production fires. By 2026 model deprecation is a quarterly recurring task for any production AI team. [[Model version pinning]] and a clean migration story prevent fire drills.

## When to use

- Production AI teams must track deprecations.

## Common mistakes

- Not monitoring provider changelog — surprise hard-fail.
- Migrating without re-evaluating — quality regression slips into prod.

## Related terms

- [model-version-pinning](https://promtable.com/glossary/model-version-pinning)
- [model-registry](https://promtable.com/glossary/model-registry)

*Last updated: 2026-06-01*
---

Original page: https://promtable.com/glossary/model-deprecation
Maintained by Promtable (https://promtable.com). Content: CC BY 4.0. Cite as "Promtable — https://promtable.com/glossary/model-deprecation".
Contact: info@vibecodingturkey.com.