Knowledge Cutoff
Definition
The knowledge cutoff refers to the point in time up to which a model provider typically classifies the internal training knowledge of a model.
It depends on the model and does not mean that all information up to this date is contained or correct. With web, file, or retrieval tools, a system can obtain more current data.
Simply explained
Like the latest date of a built-in reference work – with gaps and no guarantee of completeness.
Why it matters in practice
Relevant to avoid falsely answering current questions, prices, laws, and events based solely on model knowledge.
Example from business
An assistant recognizes that for a question about current regulation, it needs live sources instead of just model knowledge.
Limits and pitfalls
- Confusing cutoff with complete knowledge; answering current facts without sources; continuing to omit source verification when tool access is available.
Origin & history
Model providers publish a knowledge cutoff as technical metadata for many models; the value differs by model version.
Use in mid-sized companies
- Possible application in SMEs: connect web, file, or system sources for time-critical tasks and provide evidence for answers.
Frequently asked questions
Is everything known before the cutoff?
No. Can a model use current data after the cutoff? Yes, if suitable tools or sources are available.
Sources
- Knowledge Cutoff · OpenAI API Models, Knowledge Cutoff
- current information · OpenAI Help, current information
Sources checked on 10.10.2026.
Editorially reviewed on: