Are some AI platforms more accurate about brands than others?

AI platforms do get facts wrong at different rates. The problem is that nobody has published a census of what they get wrong about commercial brands. The studies that exist test other things: news citations, research reports, or summaries of supplied documents.

Those numbers also expire. A rate measured on one model version says little about the version a vendor ships three months later. As of October 2026, the consumer apps change their default models and routing often, and the vendors do not always say which model a free user gets.

So the honest answer to "which platform is most accurate about brands" is: nobody knows in general, and the answer for your brand can differ from the answer for a competitor. What you can know is how each platform describes you today. That means checking your own facts, platform by platform, and repeating the check after model updates.

For the broader picture of error rates, see how often AI search engines get facts wrong.

Each widely quoted figure answers a narrower question than the headline suggests.

• News citations. The Columbia Journalism Review's Tow Center found that AI search tools answered more than 60% of news queries incorrectly, with Perplexity at 37% and Grok 3 at 94%. That is a test of attributing news articles, not of stating a company's pricing or headcount.
• Research agents. PwC measured deep-research agents at 39% to 77% factual accuracy, while more than 94% of their links were valid. The links resolved and looked relevant, yet often did not support the claim.
• Summarization. The Vectara hallucination leaderboard scores models on summarizing short documents they are given. It is binary per summary and excludes refused responses, so a model that declines often cannot look accurate by staying quiet.

The research methods differ too. FActScore splits a response into atomic facts and scores the share supported by a source, giving every fact equal weight. None of these benchmarks asks whether a model knows your founding year or your current plans.

What this means: use these studies to understand that errors are common and uneven. Do not use them to pick a "safe" platform for your brand.

Four things change a platform's answers about you without anyone telling you.

1. Model updates and routing. "ChatGPT" is not one knowledge state. As of OpenAI's model pages, the GPT-5.6 family has a February 16, 2026 knowledge cutoff, while the Instant path carries an August 31, 2025 cutoff. Which one answers depends on plan and routing.
2. Retrieval on or off. ChatGPT search is model-discretionary, and OpenAI says it rewrites a prompt into one or more targeted queries. Claude.ai search is a user toggle. An answer from training data alone can be months stale, which is why web search changes accuracy for some questions and not others.
3. Different indexes. In Otterly's 2026 test of two AI-generated sites, Microsoft Copilot (which grounds on Bing) produced 2,718 of the 3,934 citations tracked for one site, while ChatGPT produced effectively none. Each engine answers from what its own index holds.
4. Different surfaces from one vendor. Google states that AI Overviews and AI Mode "may use different models and techniques" and return different links. Treat them as separate platforms.

The next move: record the date, platform and retrieval state with every answer you check. Without that, two checks cannot be compared.

Some wrong answers start with you or with other sites. If your pricing page, about page and footer state different figures, any platform that retrieves your site can repeat the wrong one. That is a fixable inconsistency, not a platform accuracy problem.

Third-party pages matter as well. When engines cite other sites instead of yours, an outdated directory listing or review profile can become the answer. Research on search agents also shows that manipulated pages with injected instructions can sway what an agent endorses once that content is in its results. That is a defensive risk to monitor, never a tactic to copy.

An old fact and an invented one need different fixes. Telling a hallucination from an outdated answer decides whether you update a source or correct a fabrication. And severity matters more than counts: one serious error outweighs many small ones, such as a false acquisition versus a wrong headcount.

Build your own per-platform accuracy check. It takes an afternoon to start.

1. Write down your verified facts. Pricing, plans, founding year, headquarters, ownership, product names and integrations. Make sure your own site states each one consistently, in HTML that loads before JavaScript runs.
2. Pick buyer questions. Use the questions prospects actually ask, not just "what is [brand]".
3. Ask each platform separately. Note the date, plan, and whether the answer showed citations. No visible citations usually means you cannot tell if it searched.
4. Score each answer against your facts. Flag wrong, outdated and unverifiable claims separately, and mark severity.
5. Repeat after model updates. A clean result this month does not carry over to the next model.

If you want a starting snapshot, the free checker on this site scores up to three buyer questions. For working through what you find, the hallucination triage playbook sets the order of fixes. Nothing here controls what a model says, but it tells you where it is wrong and which sources to correct.