AI Model Knowledge Cutoff Dates 2026: Complete Cheat Sheet
By Hans Kuepper · Founder of PromptQuorum · Discovery engine for open-weight & open-source AI
A knowledge cutoff date is the date after which an AI model has no training data — events, product releases, or facts after that date are invisible to the model. Cloud models like ChatGPT and Gemini partially compensate with built-in web search. Local LLMs (Llama, Qwen, Gemma, Phi) have no search layer, so their cutoff is an absolute hard limit.
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 — Gemini 4 Argon, Mistral Large 4, DeepSeek-V4 and Qwen3 — have not publicly disclosed exact cutoff dates; "Not publicly disclosed" below means we found no primary source stating one
- ✓For GEO strategy: appearing in cloud AI requires SEO/search optimization; appearing in local AI requires RAG pipelines built by the deployer
Quick Answer
Verified cutoffs: Claude Opus 5.5 June 2026 (reliable cutoff; supersedes Claude Opus 5, May 2026); GPT-6 Astra and GPT-6.1 Sol April 2026, GPT-6 Luna May 2026 (ChatGPT searches Bing by default; the GPT-6 family supersedes GPT-5.6, Feb 2026; GPT-4o is a legacy model, Oct 2023); Grok 4.7 May 2026 (searches X by default in the consumer app; supersedes Grok 4.6, Feb 2026). Google, Mistral, and DeepSeek state no cutoff for Gemini 4 Argon, Mistral Large 4, or DeepSeek-V4; the previous Gemini flagship, Gemini 3.1 Pro, lists January 2025 (native Google Search). Local LLMs (Llama, Qwen, Gemma, Phi) have no web search and their cutoff is absolute. Among open-weight models, Meta documents August 2024 for Llama 4 Scout, Google August 2024 for Gemma 3 27B and Microsoft June 2024 for Phi-4; we found no stated cutoff for DeepSeek-V3 and V4, Qwen2.5 and Qwen3, Mistral Small 3 or Gemma 4.
- ▸GPT-6 family (ChatGPT): Apr–May 2026 cutoff — partially offset by Bing search default
- ▸Claude (Opus 5.5): June 2026 reliable cutoff — web search requires explicit tool activation
- ▸Grok 4.7: May 2026 cutoff — searches X (Twitter) by default in the consumer app
- ▸Local LLMs (Llama, Qwen, Gemma, Phi): no search layer — cutoff is a hard frozen limit
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 5.5 | Anthropic | 2026-06 | No (tool-use only) | Tool-use only |
| Claude Opus 5 (previous generation) | Anthropic | 2026-05 | No (tool-use only) | Tool-use only |
| GPT-6 Astra / GPT-6.1 Sol (ChatGPT) | OpenAI | 2026-04 | Yes | Bing |
| GPT-5.6 (previous generation) | OpenAI | 2026-02 | Yes | Bing |
| GPT-4o (legacy) | OpenAI | 2023-10 | Yes | Bing |
| Gemini 4 Argon | Not publicly disclosed | Yes | ||
| Gemini 3.1 Pro (previous generation) | 2025-01 | Yes | ||
| Grok 4.7 | xAI | 2026-05 | Yes | X (Twitter) |
| Grok 4.6 (previous generation) | xAI | 2026-02 | Yes | X (Twitter) |
| Mistral Large 4 | Mistral AI | Not publicly disclosed | No | None |
| Mistral Large 3 (previous generation) | Mistral AI | Not publicly disclosed | No | None |
| DeepSeek-V4 | DeepSeek | Not publicly disclosed | No | None |
| DeepSeek-V3 / R1 (previous generation) | DeepSeek | Not publicly disclosed | No | None |
📍 In One Sentence
ChatGPT searches Bing by default, Gemini searches Google, and Grok searches X — but Claude's web search must be enabled as a tool, and DeepSeek's API models have no built-in search layer.
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 usual 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 | Meta | 2024-08 | ✓ Primary source | None — hard limit |
| Qwen3 14B / Qwen2.5 72B | Alibaba | Not publicly disclosed | Not disclosed | None — hard limit |
| Mistral Small 3 / Mistral 7B | Mistral AI | Not publicly disclosed | Not disclosed | None — hard limit |
| DeepSeek-V4 (open weights) | DeepSeek | Not publicly disclosed | Not disclosed | None — hard limit |
| DeepSeek-V3 (open weights, previous generation) | DeepSeek | Not publicly disclosed | Not disclosed | None — hard limit |
| Gemma 4 | Not publicly disclosed | Not disclosed | None — hard limit | |
| Gemma 3 27B (previous generation) | 2024-08 | ✓ Primary source | None — hard limit | |
| Phi-4 | Microsoft | 2024-06 | ✓ Primary source | None — hard limit |
💬 In Plain Terms
Running a local LLM is like asking an expert who has been completely offline since a specific date — brilliant about everything before that date, completely blind to everything after.
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 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 cannot reach the internet on its own unless you add that capability yourself, for example via a RAG pipeline or a search tool.
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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