AI Model Knowledge Cutoff Dates 2026: Complete Cheat Sheet
Quick Answer
Verified cutoffs: GPT-5.5 August 2025 (ChatGPT searches Bing by default; GPT-4o legacy Oct 2023); Claude Opus 4.8 January 2026 (reliable cutoff); Grok 4.3 November 2024 (searches X); Gemini 3.1 Pro January 2025 (native Google Search); DeepSeek-V3 July 2024; Gemma 3 27B August 2024; Phi-4 June 2024; Qwen2.5 December 2023. Several major models — including Mistral Large, Llama 4, and Qwen3 — have not publicly disclosed exact cutoff dates. Local LLMs have no web search and their cutoff is absolute.
- ▸GPT-5.5 (ChatGPT): Aug 2025 cutoff — partially offset by Bing search default
- ▸Claude (Opus 4.8): Jan 2026 reliable cutoff — web search requires explicit tool activation
- ▸Grok 4.3: Nov 2024 cutoff — searches X (Twitter) by default
- ▸Local LLMs (Llama, Qwen, Gemma, Phi): no search layer — cutoff is a hard frozen limit
Updated: 2026-06-13
Key Takeaways
- ✓A knowledge cutoff is a hard date — the model has zero training data after it, and will confabulate or say it doesn't know about anything after that date
- ✓Cloud models (ChatGPT, Gemini, Grok) partially compensate via built-in web search; the search layer can override stale training data for factual queries
- ✓Local LLMs (Llama, Qwen, Gemma, Phi, Mistral) have NO search layer — their cutoff is an absolute frozen knowledge limit with no override
- ✓Several major models — Mistral Large, Llama 4, Qwen3 — have not publicly disclosed exact cutoff dates; "Not publicly disclosed" below means no primary source exists
- ✓For GEO strategy: appearing in cloud AI requires SEO/search optimization; appearing in local AI requires RAG pipelines built by the deployer
Cloud AI Models: Knowledge Cutoffs & Live Search Layers
These are the cloud models end users interact with. Where a search layer exists, the model can retrieve current information for some queries — but the underlying knowledge cutoff still matters for context not covered by search.
⚠️ "Default live search" means the model searches the web automatically for most queries, without any developer integration. "Tool-use only" means search must be explicitly wired by developers; end users without that setup see only the training cutoff.
| Model | Vendor | Cutoff Date | Default Live Search | Search Layer |
|---|---|---|---|---|
| Claude Opus 4.8 | Anthropic | 2026-01 | No (tool-use only) | Tool-use only |
| GPT-5.5 (ChatGPT) | OpenAI | 2025-08 | Yes | Bing |
| GPT-4o (legacy) | OpenAI | 2023-10 | Yes | Bing |
| Gemini 3.1 Pro | 2025-01 | Yes | ||
| Grok 4.3 | xAI | 2024-11 | Yes | X (Twitter) |
| Mistral Large 3 | Mistral AI | Not publicly disclosed | No | None |
| DeepSeek-V3 / R1 | DeepSeek | 2024-07 | No | None |
Local / Open-Weight LLMs: Hard Knowledge Cutoffs
Local LLMs run entirely on your device or a self-hosted server. They have no internet connection by default and no built-in search layer. Every entry in this table has "None" for search — because the only way to give a local LLM access to current information is to build a RAG pipeline yourself.
This means the cutoff dates below are HARD limits. Ask a locally-run Llama 4 Scout about something that happened after its training cutoff and it will either make something up or admit it doesn't know.
| Model | Vendor | Cutoff Date | Cutoff Verified | Search Layer |
|---|---|---|---|---|
| Llama 4 Scout / Llama 3.3 70B | Meta | Not publicly disclosed | Not disclosed | None — hard limit |
| Qwen3 14B / Qwen2.5 72B | Alibaba | 2023-12 | ✓ Primary source | None — hard limit |
| Mistral Small 3 / Mistral 7B | Mistral AI | Not publicly disclosed | Not disclosed | None — hard limit |
| DeepSeek-V3 (open weights) | DeepSeek | 2024-07 | ✓ Primary source | None — hard limit |
| Gemma 3 27B | 2024-08 | ✓ Primary source | None — hard limit | |
| Phi-4 | Microsoft | 2024-06 | ✓ Primary source | None — hard limit |
Why Local LLMs Are Fundamentally Different
Cloud AI models and local LLMs handle knowledge cutoffs differently in one critical way: cloud models can search the live web; local models cannot.
When ChatGPT can't answer from training data, it silently queries Bing and augments its response with current results. When Gemini 3.1 Pro is asked about a recent event, it searches Google. These search layers hide the cutoff from casual users — you get a current-sounding answer even though the base model's training data is months or years old.
A locally-run Qwen3 or Llama 4 on your machine has no such safety net. Ask it about a product launched last month and it has two options: confabulate (hallucinate a plausible-sounding but fabricated answer), or say it doesn't know. There is no third option — it physically cannot reach the internet unless you build that capability yourself via a RAG pipeline.
This distinction matters for three groups: users who need accurate current-events answers; businesses deploying AI internally on local hardware; and companies that want to appear in AI answers (GEO strategy — see the full GEO analysis).
Frequently Asked Questions About AI Knowledge Cutoff Dates
What is a knowledge cutoff date in AI?▾
What is the difference between a knowledge cutoff and live search?▾
Do local LLMs ever update their knowledge?▾
Which AI models can see today's news and current events?▾
Is the ChatGPT cutoff date the same as what it knows right now?▾
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