Does Claude recommend brands differently from ChatGPT? How to test your own brand

People now ask AI assistants questions like "what is the best accounting software for a small shop" or "which dentist in Bend takes new patients", and the answer is a short list of names. If your brand is on the list, that can matter. If it is not, it is natural to ask whether a different assistant would have named you. This guide answers the question honestly, because a lot of the advice on this topic is sold with precise-looking numbers that nobody can verify.

The short answer

Yes, Claude and ChatGPT can and do recommend different brands for the same question, and the same assistant can give different answers on different days or even on consecutive tries. We have not run a controlled study and we are not aware of a reliable public dataset that ranks one assistant as better or worse for any category, so we are not going to invent statistics. What we can do is explain why differences arise and give you a way to measure how the assistants describe your brand, which is the only number that matters to you.

Why the answers differ

• Different models and training data. Each assistant learned from a different mix of text, with a different cutoff date. A brand that was heavily discussed before one cutoff but not the other may be remembered by only one.
• Different web search behaviour. Both products can search the web in some modes or plans, and when they do, they use their own search sources and ranking and decide for themselves when to search. A recommendation drawn from live search results can differ sharply from one drawn from memory. Whether a search happens at all depends on the product, the plan, the settings and the question.
• Different defaults. Assistants differ in how willing they are to name a "best" option, how many choices they list, whether they hedge, and how they treat local businesses. A cautious answer may list categories of provider instead of names.
• Personalisation and context. Saved memory, custom instructions, your location and earlier messages in the chat all change the answer. Two people asking the same question may see different results.
• Randomness. Language models sample their output. Asking the identical question twice often yields a different order or a different final name.

What both tend to have in common

Although the specifics differ, the underlying logic is similar: assistants name brands they have seen described clearly, in many places, by sources they treat as credible. That tends to favour brands with a clear one-sentence description of what they do, consistent naming across their website and profiles, reviews and mentions on independent sites, and documentation or content that answers the questions customers ask. It also tends to favour large, long-established names over new ones, which is the main reason a young brand is easily overlooked. None of this is a guarantee and none of it is secret, but it is a useful checklist.

How to test your own brand

A fair test is repeatable. One-off screenshots prove very little. Use this procedure:

1. Write 10 to 15 prompts that real customers might ask. Mix types: broad category ("best project management tool for a small agency"), comparison ("X vs Y for freelancers"), problem-led ("how do I stop my invoices being paid late") and local ("web designer near me in Oregon" if you serve a local area). Do not mention your own brand in most of them.
2. Add 3 to 5 brand prompts that do name you, such as "what is Brand X and who is it for?", to see what the assistant says when asked directly, and whether it gets basic facts right.
3. Use fresh conversations. Start a new chat for each prompt so earlier messages do not influence the answer. If possible, test while logged out or in a clean profile so saved memory does not tilt results.
4. Run each prompt three times in each assistant. Record the answers at the same time of day, and note whether web search was used (both products usually show when they have searched).
5. Repeat monthly. Models and search behaviour change, so a single test is a snapshot. Trends over several months mean more than any one result.

A simple tracking sheet

A spreadsheet is enough. Record one row per prompt per run, using columns like these:

These two rows are an illustration of the layout, not real results. After a month you can calculate simple figures for yourself: in what share of runs each assistant named your brand, which competitors appear alongside you, and which facts are wrong or out of date. Wrong facts are often the most valuable finding, because they point to pages you can fix.

What you can influence

• A clear statement of what you do. Put one plain sentence at the top of your home page and your About page: who you serve, what you provide and where. Assistants quote and paraphrase clear statements.If the wording is the hard part, the guides to writing website copy at AI Website Prompts may help you draft it.
• Consistent naming. Use the same business name, address and description across your site, Google Business Profile, social profiles and directories.
• Structured data. Organization, LocalBusiness, Product and FAQ markup helps machines read your facts. The Schema Markup Generator creates valid JSON-LD without code.
• Pages that answer real questions. A helpful FAQ, pricing page and comparison page give both search engines and assistants something concrete to cite.
• Independent mentions. Reviews, industry directories, press and partner pages carry weight because they are not written by you. Earn them honestly. Paying for fake reviews is against platform rules and tends to backfire.
• A working technical foundation. Fast, crawlable pages with correct titles and descriptions. Check yours with the Meta Tag and SERP Preview and keep your sitemap current.

Letting AI crawlers in (or not)

AI companies run crawlers with their own user-agent names, and your robots.txt file can allow or block each one. As of writing, OpenAI documents GPTBot, OAI-SearchBot and ChatGPT-User, and Anthropic documents ClaudeBot, Claude-SearchBot and Claude-User, though names and purposes change, so confirm them in each company's current documentation before you edit anything. In broad terms, some crawlers collect content for model training while others fetch pages for search features or when a user asks the assistant to open a page. Blocking a crawler stops that company's bot from reading your pages, which may also reduce how well it can describe you, so decide deliberately rather than copying a block list. The Robots.txt Generator lets you choose AI crawlers one by one, and our robots.txt mistakes guide explains how to avoid accidentally blocking Google while you do it.

Limits of any test

Be wary of anyone promising a guaranteed way to get recommended, or a score that claims to show exactly how an assistant ranks you. Nobody outside the companies can see the full factors, results vary with location and account, and the products change often. Your best approach is the dull and reliable one: make your business easy to understand, make accurate information easy to find, earn independent mentions, and measure honestly with the test above.

Frequently asked questions