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Is your LLM model deprecated?
Type a model id to check whether it’s retired, deprecated, or legacy — and see the recommended replacement. A silent model shutdown is one of the fastest ways to break an AI feature in production.
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Kimi K2.5 · MoonshotAI
Deprecated→ Use see provider migration guide
Shutdown date published by the provider.
A community guide, not gospel — providers change lifecycles often, so verify against the provider’s own deprecation page. retired = no longer callable · deprecated = scheduled for shutdown · legacy = still works but superseded.
Never ship on a dead model again
VernaOne syncs each provider’s live model catalog and automatically pauses — or falls back — any prompt whose model went inactive, then alerts you. The decommission is caught before your users see it.
See model-sync & alerts →Frequently asked
Is my LLM model deprecated?
Type the model id above to check it against a guide of well-known deprecated, retired, and legacy models with their recommended replacements — for example, gpt-4-0314, claude-2, gemini-1.5-pro, and text-davinci-003. Providers update lifecycles often, so always confirm on the provider’s own deprecation page.
What is the difference between retired, deprecated, and legacy?
Retired means the model is no longer callable and requests fail. Deprecated means it still works but is scheduled for shutdown — you should migrate. Legacy means it still works but has been superseded by a better or cheaper model.
What happens if I keep calling a deprecated model?
When the provider removes it, every request pinned to that model starts failing — usually a 404 or invalid-model error — and you often find out from an error spike or a customer, not the provider. Migrating early, and having a fallback, prevents the outage.
How do I stop a model shutdown from breaking production?
Don’t rely on manual checks. VernaOne syncs each provider’s live model catalog and runs a health-check that automatically pauses any prompt whose model went inactive (with no active fallback) and alerts you — or falls back to a working model if you configured one. A silent decommission never reaches your users.