Knowledge Cutoff Explained: What LLMs Know and When

A knowledge cutoff is the date after which a large language model has little or no information from its training data, so anything that happened later is unknown to the model unless it is supplied at run time through search, retrieval or the prompt. Providers publish the date under different labels, such as "knowledge cutoff", "reliable knowledge cutoff", "training data cut-off" or separate pre-training and post-training cutoffs, and some publish none. This page explains what each label means, lists the dates providers state for current models, explains why a model released in November can know nothing about the summer, and sets out how search grounding and retrieval close the gap. It is for anyone who uses chat assistants, and for the developers and buyers who build on model APIs.

Last checked: 2026-11-15. Providers are listed alphabetically; order is never a ranking.

This is an independent reference page from NHI Mgmt Group based on public provider documentation; it is not sponsored or endorsed by any provider.

Key takeaways

• Cutoff is not release date: Upstage states a February 2026 training data cut-off for Solar Pro 4, released on 6 August 2026; OpenAI states 30 April 2026 for GPT-6.1 Sol.
• Labels differ by provider: Anthropic uses "reliable knowledge cutoff", NVIDIA gives separate pre-training and post-training cutoffs, OpenAI, LG AI Research and Upstage give a single date.
• Some providers state no date on the model page: the Google Gemini 3.8 Flash page and the xAI Grok 4.7 page checked for this guide state no knowledge cutoff.
• Models can be unsure near the edge: Anthropic describes its cutoff as the date through which the knowledge base is "most extensive and reliable", which implies thinner coverage close to the date.
• Tools close the gap: web search, search grounding and retrieval-augmented generation (RAG) bring in current information at run time, each with its own cost and admin controls.

What does knowledge cutoff mean in plain language?

A language model learns from a fixed snapshot of text gathered up to some date. Once training ends, the model's internal knowledge is frozen. Ask it about an election, a product launch or a security advisory from after that date and it cannot know the answer from memory: it may say it does not know, or it may produce a confident but wrong answer based on older patterns.

Think of a printed encyclopaedia. The edition has a "correct as of" date. The book is still useful, but nobody would check today's exchange rate in it. A knowledge cutoff is the model's "correct as of" date.

The terms providers use

What is the knowledge cutoff of current models?

As of 2026-11-15. Values exactly as stated on each provider's own page; "Not stated" means the page checked gives no value. Providers alphabetical.

Older model generations carry their own cutoffs on the same provider pages. For other providers, see the individual provider pages such as Anthropic Models, Mistral AI Models, OpenAI Models and xAI Models.

Why is the cutoff months before the release date?

After data collection ends, a model still has to be trained, fine-tuned, safety-tested, documented and deployed. The published dates show the gap:

NVIDIA's two dates show a second reason: the general pre-training corpus for Nemotron 3 Super ends in June 2025, while the post-training (fine-tuning) data runs to February 2026. Knowledge from the general corpus may therefore be older than the latest fine-tuning examples.

Why does a model sometimes get its own cutoff wrong?

• Thin coverage near the edge: the internet keeps writing about events for months afterwards, so the last weeks before a cutoff are under-represented. Anthropic's "most extensive and reliable" wording reflects this.
• Mixed dates in one model: separate pre-training and post-training cutoffs mean different kinds of knowledge end at different times.
• System prompts and app layers: consumer apps often add the current date and search tools, so the same model can behave differently in an app than through the raw API.
• Self-reports are not sources: asking a model "what is your cutoff?" returns generated text, not a documented fact. Use the provider's page.

How do models answer questions about recent events?

Run-time tools bring current information into the context window. Providers alphabetical; each row states only what the provider's page says.

Retrieval-augmented generation does the same with your own documents: relevant passages are fetched from a search index or vector store and placed in the prompt. It answers questions about internal or recent material without retraining. See the RAG Security Guide and the Permission-Aware RAG Guide.

Options compared

How to work with knowledge cutoffs

1. Record the cutoff from the provider page: note the label and date checked, and write "Not stated by provider" where none is given.
2. Classify your use case by freshness: stable knowledge (grammar, maths, established concepts) is fine from memory; prices, versions, laws, vulnerabilities and news need a run-time source.
3. Turn on grounding deliberately: enable search or RAG only for the flows that need it, cap uses (Anthropic's max_uses, for example) and restrict domains where the provider allows it.
4. Show sources to users: Anthropic's docs require citations to original sources when web search output is shown directly to end users.
5. Put today's date in the system prompt: it helps the model reason about how stale its memory is.
6. Re-check after model upgrades: a new model version usually brings a new cutoff; see LLM Naming and Versioning Explained.

Where security and identity fit

• Stale security advice: a model may describe outdated product settings or miss recent vulnerabilities; verify against current vendor documentation.
• Search widens the attack surface: fetched web pages can carry indirect prompt injection. See the LLM Security Guide.
• Retrieval needs access control: a RAG index can leak documents a user should not see unless retrieval respects permissions; the service identity that reads the index needs least privilege.
• Admin controls are part of governance: where a provider offers organisation-level switches (such as Anthropic's Console setting for web search), record who may change them.

Common misunderstandings

• "Released in November, so it knows October": stated cutoffs are months before release.
• "The model told me its cutoff": self-reports are generated text; use the provider page.
• "No cutoff on the page means it is always current": it only means the page does not state one.
• "The app and the API know the same things": apps may add the date and search tools that a raw API call does not.
• "All providers mean the same thing by cutoff": labels and definitions differ; compare like with like.

Practitioner checklist

• Log each model's stated cutoff, label, source URL and date checked.
• Mark which workflows need current facts and require grounding or RAG for them.
• Configure search caps and domain restrictions where offered.
• Display citations whenever search results reach end users.
• Inject the current date into system prompts.
• Apply permission-aware retrieval and least privilege to RAG service identities.
• Re-check cutoffs when a model alias moves to a new version.

Standards and references

• Anthropic: Models overview (accessed 2026-11-15)
• Anthropic: Transparency Hub (accessed 2026-11-15)
• Anthropic: Web search tool (accessed 2026-11-15)
• Google: Gemini 3.8 Flash model page (accessed 2026-11-15)
• LG AI Research: EXAONE 4.5 33B model card (accessed 2026-11-15)
• NVIDIA: Nemotron 3 Super 120B-A12B model card (accessed 2026-11-15)
• OpenAI: GPT-6.1 Sol model page (accessed 2026-11-15)
• OpenAI: gpt-oss-120b model page (accessed 2026-11-15)
• Upstage: Solar Pro 4 model docs (accessed 2026-11-15)
• xAI: Grok 4.7 model page (accessed 2026-11-15)

Related NHI Mgmt Group resources: Model Cards and System Cards Explained · Context Windows Explained · RAG Security Guide · Foundation Model Selection Guide