Claude Haiku 4.5's knowledge cutoff is February 2025, according to Anthropic's own model documentation. Claude Sonnet 5's is January 2026. OpenAI's GPT-5.6 models cut off in February 2026, and its newest flagship, GPT-6 Astra, reaches April 2026. That's a fourteen-month spread between the oldest and newest cutoffs across just two vendors' current lineups, and it means the same brand event, a rebrand, a leadership change, a product recall, can already be common knowledge to one AI engine while a smaller or older model has never heard of it. Live web search closes some of that gap. It doesn't close all of it.
Why every AI engine runs on a different clock
AI knowledge cutoffs vary because vendors snapshot training data at different points and ship models on different schedules, not because of any coordinated freshness standard. A brand event from March 2026 sits inside GPT-6 Astra's training data and outside Claude Haiku 4.5's, purely because of when each model finished training.
Anthropic's model documentation and OpenAI's developer docs currently list:
Claude Haiku 4.5: February 2025
Claude Sonnet 5: January 2026
Claude Opus 5: May 2026
GPT-5.6 (Sol, Terra, Luna): February 2026
GPT-6 Astra: April 2026
Vendors keep smaller, cheaper models like Haiku in production long after a flagship model ships, because a lot of production traffic doesn't need the newest weights. That's efficient for the vendor and invisible to a customer who has no way of knowing which model is answering their question about your company, or how old its training data is.
What live search fixes, and what it still misses
Live web search lets ChatGPT and Claude retrieve current pages instead of relying only on frozen training weights, but neither vendor guarantees it happens automatically, and both warn the results can still be wrong.
Anthropic's own documentation says Claude decides whether to search based on the prompt, triggering on recent events, current statistics, and "information about specific organizations, people, or products that might have changed." OpenAI describes the same kind of judgment call for ChatGPT Search, noting it "may search the web automatically when your question would benefit from current information." Neither company publishes the exact logic behind that decision, which means a brand can't predict from the outside whether a given question about it will trigger a fresh lookup or fall back on stale training knowledge.
Even when a search does fire, OpenAI's help center is blunt about the ceiling on accuracy:
Search results and citations can be incomplete, outdated, or incorrect.
That caveat matters more for a brand fact than for most other queries. A wrong stock price is obviously wrong to whoever asked. A wrong claim about who your CEO is, or whether your product still does the thing it used to do, reads as confidently correct to someone who has never heard of your company before.
The Twitter-to-X lag shows how slowly public knowledge actually turns over
Elon Musk renamed Twitter to X in July 2023. Search behavior didn't catch up for close to two years. Sherwood News reported in May 2025 that Google searches for "X login" had only just begun to outweigh searches for "Twitter login," roughly 21 to 22 months after the rebrand, based on Google Trends data the outlet analyzed directly.
That gap describes humans, not AI models, but it's instructive for the same reason. Training data is a snapshot of the public record at a point in time, and if the public record itself takes nearly two years to flip from an old name to a new one, a model trained mid-transition inherits whichever version was dominant the day its data was collected. A brand that renamed, repositioned, or replaced its leadership six months ago is still mid-transition in exactly the window where an AI engine's stale answer does the most damage, because the change is too recent to have fully displaced the old story anywhere, including inside a model's weights.
What actually gets an AI engine to notice your brand changed
Volume and structure both move the needle, though neither works instantly. A freshness signal in GEO terms is any indicator an engine's retrieval layer uses to judge how current a piece of content is, and it's the same kind of signal whether the engine is doing live search or building the index that live search draws from.
Trade press coverage of a change, not just a company's own announcement, tends to move faster through the sources AI engines actually crawl and cite. A press release sitting only on a company's own domain is a weaker signal than the same news covered by an outlet a knowledge graph already trusts. Updated Organization schema and a current sameAs array pointing to a brand's verified social and reference profiles give a grounding system a structured, low-ambiguity place to check a fact against, which VizibleAI's guide to schema markup for GEO covers in more detail. None of this forces a specific model to re-train. It shortens how long a live search takes to find the current version of the story instead of an older one still ranking well from momentum.
Why this matters more now that AI answers arrive before someone visits your site
A growing share of commercial research happens inside an AI answer before it happens on a company's own website. AI Overviews appeared in 57.2% of commercial searches for a tracked core keyword twelve months earlier and had climbed to 95.9% by June 2026, according to Google Keyword Planner and Semrush data cited in MarTech's coverage of the shift. Separately, roughly a third of digital marketing leaders named generative engine optimization their top priority for 2026, up from an average of 12% of 2025 budgets, per research from Conductor reported in MarTech. Watching that shift happen engine by engine, rather than inferring it from traffic changes after the fact, is what VizibleAI is built to track.
That shift means a stale AI answer isn't a minor inconvenience buried on page three of search results. It's frequently the only answer a prospective buyer sees before deciding whether to click through at all. VizibleAI's research on how often AI engines get brand facts wrong found factual errors aren't rare, and a stale cutoff compounds that problem instead of causing a separate one: an engine can be both wrong and out of date about the same brand at the same time, for two different reasons that require two different fixes.
Monitoring which engines are current and which are lagging is a narrower problem than monitoring for outright errors, and it responds to different fixes. VizibleAI's guide to tracking brand mentions across ChatGPT, Gemini, and Perplexity walks through the mechanics of watching multiple engines at once, which is the only way to notice that Claude is still describing last year's positioning while ChatGPT has already picked up the change.
Frequently Asked Questions
Does ChatGPT know about my company's rebrand yet?
It depends on when the rebrand happened and whether the question triggers a live search. If your rebrand postdates the model's training cutoff, ChatGPT only knows about it when it decides to search the web for that specific question, which OpenAI's own documentation says isn't guaranteed to happen automatically.
Why does Claude mention outdated information about my brand while ChatGPT seems current?
Different vendors ship models with different training cutoffs, and a company often runs several model sizes in production at once. Claude Sonnet 5's cutoff is a full fourteen months newer than Claude Haiku 4.5's, so which specific model answers a question changes the answer's freshness even within one vendor's lineup.
Does updating my schema markup fix a stale AI answer immediately?
No, but it shortens the lag. Structured data like Organization schema and sameAs links doesn't retrain a model. It gives a live-search or grounding system a clearer, faster signal to find and trust the current version of a fact once it does look.
How long does it typically take for AI engines to fully catch up on a brand change?
There's no single published figure for AI engines specifically, but the closest real-world comparison, how long it took Google search behavior to shift from "Twitter" to "X," ran close to two years according to Sherwood News's analysis of Google Trends data. Structured data and trade press coverage can shorten that window; they can't eliminate it.
Can I force a specific AI engine to update what it knows about my company?
Not directly. You can't submit a correction to a model's training weights. What you can influence is how quickly a live search or grounding system finds accurate, current information when it does look, by keeping structured data current and making sure trade press and third-party sources reflect the change, not just your own site.
Does this affect smaller AI engines the same way it affects ChatGPT and Claude?
Every engine that relies partly on trained knowledge rather than pure live retrieval has some version of this gap. The size of the gap depends on that engine's specific training cutoff and how aggressively it defaults to live search, both of which vary by vendor and aren't publicly standardized.
See what each AI engine currently believes about your brand
Different engines can hold contradictory versions of the same brand fact at the same time, and there's no way to know which one a prospective customer will see without checking each engine directly. VizibleAI tracks brand mentions, sentiment, and citation accuracy across ChatGPT, Claude, Gemini, and Perplexity in one place, so a stale answer in one engine doesn't stay invisible while the others move on. Start a free trial to see what each engine is currently saying about your brand.