Knowledge Cutoff Dates in Large Language Models: A Comprehensive Overview
Large Language Models (LLMs) have transformed how we interact with artificial intelligence, offering impressive capabilities in generating human-like text. However, these models come with important limitations, notably their knowledge cutoff dates.
Latest Updates
- OpenAIs ChatGPT-5.4 has a knowledge cut off of August 31, 2025 and was released on March 06, 2026.
- Google Gemini 3.1 Flash-Lite has a knowledge cut off in January 2025 and was released on March 03, 2026.
- Claude 4.7 Opus has a reliable knowledge cut off date in January 2026 and a training data cut off date in January 2026. The model was released on April 17, 2026.
What are AI Knowledge Cutoff Dates?
A knowledge cutoff date represents the point in time beyond which an LLM has no inherent knowledge of events, developments, or information. This date is determined by when the model’s training data was last updated before final training completion. Unlike humans who continuously learn, LLMs have their knowledge “frozen” at a specific temporal point unless deliberately updated or augmented with additional tools.
Knowledge cutoff dates exist primarily due to practical limitations in the training process. Training an LLM requires immense computational resources and time, often taking weeks or months to complete. This makes continuous training logistically challenging and economically impractical. Once trained, an LLM’s core knowledge remains static until a deliberate retraining or update occurs.
OpenAI and ChatGPT knowledge cut-off dates
| Model Name | Type | Knowledge Cut-off date | Public Release Date |
|---|---|---|---|
| GPT-5.4 | Reasoning | August 31, 2025 | March 05, 2026 |
| GPT-5.3 Instant | Reasoning | August 31, 2025 | March 03, 2026 |
| GPT-5.3 Codex | Agentic coding | August 31, 2025 | February 05, 2026 |
| GPT-5.2 | Reasoning | August 31, 2025 | December 11, 2025 |
| GPT-5.1 | Reasoning | September 30, 2024 | November 12, 2025 |
| GPT-5 | Reasoning | September 30, 2024 | August 7, 2025 |
| GPT-oss-20b | Cost-optimized Chat, Open-Source | June 01, 2024 | August 5, 2025 |
| GPT-oss-120b | Chat, Open-Source | June 01, 2024 | August 5, 2025 |
| GPT-4.5 (Research Preview) | Reasoning | October 01, 2023 | February 2, 2025 |
| o4-mini-high | Reasoning | June 01, 2024 | April 16, 2025 |
| o4-mini | Reasoning | June 01, 2024 | April 16, 2025 |
| o3 | Reasoning | June 01, 2024 | April 16, 2025 |
| o3-mini | Reasoning | October 01, 2023 | January 31, 2025 |
| o1 | Reasoning | October 01, 2023 | September 12, 2024 |
| o1-mini (deprecated) | Reasoning | October 01, 2023 | September 12 2024 |
| GPT-4 | Chat | September 01, 2021 | March 14, 2023 |
| GPT-4.1 | Chat | June 01, 2024 | April 14, 2025 |
| GPT-4o | Chat | October 01, 2023 | May 13, 2024 |
| GPT-3.5 Turbo | Chat | September 01, 2021 | January 24, 2024 |
| GPT-3.5 | Chat | September 01, 2021 | March 15, 2022 |
| GPT-3 | Chat | October 01, 2020 | November 01, 2021 |
| ChatGPT-4o | Chat | October 01, 2023 | May 13, 2024 |
| o4-mini | Cost-optimized Chat | June 01, 2024 | April 16, 2025 |
| GPT-4.1 mini | Cost-optimized Chat | June 01, 2024 | April 14, 2025 |
| GPT-4.1 nano | Cost-optimized Chat | June 01, 2024 | April 14, 2025 |
| o3-mini | Cost-optimized Chat | October 01, 2023 | January 31, 2025 |
| GPT-4o mini | Cost-optimized Chat | October 01, 2023 | July 18, 2024 |
| o1-mini (deprecated) | Cost-optimized Chat | October 01, 2023 | September 12, 2024 |
| GPT-4o Search Preview | Chat Completion with Web Search | October 01, 2023 | March 11, 2025 |
| GPT-4o mini Search Preview | Chat Completion with Web Search | October 01, 2023 | March 11, 2025 |
Antrophic (Claude) knowledge cut-off dates
| Model Name | Knowledge Cutoff Date | Release Date |
|---|---|---|
| Claude 4.7 Opus | January 2026 | April 16, 2026 |
| Claude 4.6 Sonnet | August 2025 | January 17, 2026 |
| Claude 4.6 Opus | May 2025 | February 5, 2026 |
| Claude 4.5 Opus | March 2025 | November 24, 2025 |
| Claude 4.5 Haiku | February 2025 | October 15, 2025 |
| Claude 4.5 Sonnet | January 2025 | September 29, 2025 |
| Claude 4.1 Opus | January 2025 | August 5, 2025 |
| Claude 4 Opus | January 2025 | May 22, 2025 |
| Claude 4 Sonnet | January 2025 | May 22, 2025 |
| Claude 3.7 Sonnet | October 2024 | February 2025 |
| Claude 3.5 Sonnet | April 2024 | October 2024 |
| Claude 3 Opus | March 2023 | August 2023 |
| Claude 3.5 Haiku | October 2024 | April 2024 |
GEMINI (Google / Alphabet) knowledge cut-off dates
| Model Name | Knowledge Cutoff Date | Release Date |
|---|---|---|
| Gemini 3.1 Flash Lite | January 2025 | March 03, 2026 |
| Gemini 3.1 Pro | January 2025 | February 19, 2026 |
| Gemini 3 | January 2025 | November 18, 2025 |
| Gemini 2.5 Pro | January 2025 | March 25, 2025 |
| Gemini 2.5 Flash | January 2025 | March 25, 2025 |
| Gemini 2.0 Flash | August 2024 | January 30, 2025 |
Mistral AI knowledge cut-off dates
| Model Name | Knowledge Cutoff Date | Release Date |
|---|---|---|
| Mistral Small 4 | June 2025 | March 2026 |
| Mistral Large 3 | December 2025 | December 2025 |
| Mistral Large 2 | October 2023 | November 2024 (current version) |
| Mistral Small 3.1 | October 2023 | March 2025 (current version) |
Other AI Model cut-off dates
| Model Name | Provider | Knowledge Cutoff Date | Release Date |
|---|---|---|---|
| Llama 4 | Meta | August 2024 | April 5, 2025 |
| Llama 3 | Meta | December 2023 | April 18, 2024 |
| DeepSeek R1 | DeepSeek | October 2023 (presumed) | January 25, 2025 |
| DeepSeek V3 | DeepSeek | July 2023 (presumed) | December 26, 2024 |
Perplexity knowledge cut-off dates
All Perplexity Sonar models integrate real-time web search capabilities, allowing them to provide information beyond their training data’s cutoff dates.
The Distinction Between Knowledge and Retrieval
An important distinction exists between an LLM’s inherent knowledge (limited by its cutoff date) and its ability to access current information through integrated tools like web browsing capabilities. For instance, while ChatGPT’s built-in knowledge might end at a specific date, when equipped with browsing capabilities, it can retrieve and process current information beyond its cutoff date.
This distinction helps explain why some users experience confusion when an LLM claims it doesn’t know about events after its cutoff date but can still answer questions about recent events when given web access.
Effective vs. Reported Cutoffs
Different sub-resources within an LLM’s training data may have varying effective cutoff dates. This means that an LLM’s knowledge isn’t uniformly cut off at a single point in time across all domains or topics. Instead, knowledge about different subjects might be current up to different dates.
This variation occurs due to two primary factors:
- Temporal misalignments in CommonCrawl data, where newer data dumps contain significant amounts of older information
- Complications in deduplication schemes involving semantic duplicates and lexical near-duplicates
These findings suggest that users should approach cutoff dates with nuance, understanding that an LLM’s knowledge might be more current in some domains than others, regardless of the officially reported cutoff date. However, the officially reported cutoff date as reported above are still a great guidance for marketers aiming to understand which training data is considered in LLM responses about their company.
Why knowledge cut off dates matter for LLMO/GEO
LLMO, GEO and ALLMO are all terms that describe the emerging practice of optimizing brand visibility inside AI-generated answers rather than traditional search engines.
Knowledge cut-off dates are crucial to consider because language models like ChatGPT often only know information up to a certain date (their training cut-off). This means content you optimize and publish today might not appear in a model’s answers if the model was trained before your content existed, however that does not mean you are out of the game. Based on our research at ALLMO.ai, a leading platform helping companies analyze and improve their visibility in AI-generated answers, here is why it’s still important:
- Todays’ content is used to train tomorrows LLMs. You may not see immediate impact, but strategic positioning is important to find your way into the training data of future versions, potentially boosting your visibility in AI-generated answers going forward.
- Many modern AI chatbots can perform a live web search to access more recent information: whenever their built-in knowledge is outdated or limited, so having current, well-structured content on the web makes it more likely the AI will find and reference your material in its responses.
- Spot and fill gaps in your current content marketing efforts by understanding what static models can’t answer due to outdated knowledge, which increases the chance your site will be cited as a source either when the model’s training data is updated.
Most AI companies release one to two major updates per year, along with several smaller updates throughout the year—sometimes including refreshed knowledge based on more recent training data. While the immediate impact of these updates may seem small, the long-term benefit is significant: once your content finds its way into a large language model (LLM), it can influence responses for months. This stands in contrast to traditional SEO or LLMs with web-browsing capabilities, where indexed information can change daily. As a side note, it’s important to remember that even static models can produce different answers due to parameters like “temperature,” which introduce a degree of randomness. Producing relevant, high-quality content helps both immediate visibility in AI search and ensures a more consistent, reliable presence in static AI models.
Sources:
- https://platform.openai.com/docs/models
- https://openai.com/news/
- https://en.wikipedia.org/wiki/Gemini%5F%28language%5Fmodel%29
- https://docs.mistral.ai/getting-started/models/models%5Foverview/
- https://ai.google.dev/gemini-api/docs/models?hl=de
- https://platform.claude.com/docs/en/about-claude/models/overview
- Own experimentation & prompts with LLMs.
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