Pineprompt AI search visibility benchmark 2026

Pineprompt — Thu, 15 Oct 2026 14:25:23 +0000

Pineprompt analyzed 18,792 responses to 200 non-branded buyer prompts across 10 categories, run daily on seven AI search engines from September 30 to October 13, 2026. This is how the engines cite sources and name brands.

Key findings

Perplexity cites a source in 100% of its answers (Gemini: 90.7%).
The average answer names 6.2 tracked brands.
12.9% of brands are named by every engine; 46.2% by only one.
Perplexity averages 10.1 citations per answer.
Perplexity shows the highest day-to-day brand churn, at 68.8%.

The data is licensed under CC BY 4.0, so you may share and adapt it with attribution. Source: Pineprompt AI Search Visibility Benchmark 2026, pineprompt.com/research/ai-search-visibility-benchmark. The CSV is available.

Results by engine

Engine	Answers with citations	Avg citations per answer	Avg brands named per answer	Day-to-day brand change
ChatGPT	99.5%	9.3	5.5	36%
Claude (claude-haiku-4-5-20251001)	95.3%	4.4	6.7	46.4%
Claude (claude-haiku-5-5)	96.4%	5	8.5	41.1%
Copilot	98%	4.8	7.6	51.3%
Gemini	90.7%	6.7	6	55.8%
Google AI Mode	94.8%	5.9	6.5	45.6%
Google AI Overview	96.3%	7.1	5.7	45.1%
Perplexity	100%	10.1	3.9	68.8%

Top cited domain types by engine

Engine	Most cited domain types, share of citations
ChatGPT	Vendor and brand sites (48.8%); Editorial and review sites (44.5%); Other (5.6%)
Claude (claude-haiku-4-5-20251001)	Editorial and review sites (51.9%); Vendor and brand sites (47.6%); Other (0.3%)
Claude (claude-haiku-5-5)	Editorial and review sites (52.7%); Vendor and brand sites (46.9%); Other (0.3%)
Copilot	Editorial and review sites (56.6%); Vendor and brand sites (41.9%); Other (0.7%)
Gemini	Vendor and brand sites (59.8%); Editorial and review sites (38.4%); Forums and communities (1.2%)
Google AI Mode	Vendor and brand sites (34.5%); Editorial and review sites (27.5%); Video platforms (18.5%)
Google AI Overview	Vendor and brand sites (39.4%); Editorial and review sites (28.5%); Video platforms (18.5%)
Perplexity	Vendor and brand sites (46.3%); Editorial and review sites (43.9%); Other (5.4%)

Brand overlap between engines

12.9% of the 8,269 brands seen are named by every engine.
46.2% are named by only one engine.
Google AI Overview and ChatGPT share 45.6% of named brands (Jaccard similarity).
Google AI Mode and ChatGPT share 45.2% of named brands (Jaccard similarity).

Results by category

Category	Answers with citations	Avg citations per answer	Avg brands named per answer	Day-to-day brand change
Accounting	97.1%	6.9	6.2	41.4%
CRM	96.5%	6.6	5.9	48.9%
Cybersecurity and VPN	97.1%	6.9	5.4	43.1%
Ecommerce platforms	97.9%	7.3	6.7	44.1%
Email marketing	97.3%	6.8	6.1	40.9%
HR and recruiting	98%	6.5	6.7	47%
Project management	95.7%	6.6	6.6	44.1%
Running and fitness	91.2%	6.5	5.1	46.8%
Travel insurance and travel	94.1%	6.8	5.9	43.9%
Web hosting	97.7%	6.8	6.9	47.4%

Methodology

We wrote 200 non-branded buyer prompts across 10 categories and ran each against 7 engines: ChatGPT, Gemini, Perplexity, Claude, Google AI Overview, Google AI Mode, and Copilot. The 18,792 responses were collected from 2026-09-30 to 2026-10-13. Prompts were scheduled daily on each engine for 14 days (US, English); some runs returned no answer. Pineprompt analysed the responses to extract citations and named brands. Metric definitions are on the methodology page.

Limitations

The prompts are a fixed sample of buyer questions in US English, so results may not hold for other markets, languages, or query styles. AI answers are non-deterministic, and engines change without notice, so the numbers describe a 14-day window rather than a lasting property of each engine. Brand stats cover brands seen with 2 or more mentions that were assigned to category segments by 2026-10-13. Claude is split by model: the previous Haiku until 2026-10-09 around 18:40 UTC, claude-haiku-5-5 after. Domain type uses the app's source classification; missing source types land in Other.

Originally published on pineprompt.com.

How to measure share of voice in AI search

Pineprompt — Thu, 15 Oct 2026 14:23:43 +0000

Share of voice in AI search is your brand's portion of all brand mentions in AI answers to a fixed set of prompts. If assistants name four brands a total of 400 times and yours 60 times, your share is 15%. The number only means something if the prompts, competitors and method stay fixed.

This guide covers the definition, how to build the prompt and competitor sets, three ways to count, how many runs you need, why per-engine and blended figures differ, how country and language change results, a worked example and common mistakes.

The definition

Share of voice = your brand mentions divided by all brand mentions in a fixed prompt set, across the brands you chose to compare.

Two things follow. The result depends on who is in the denominator, so the competitor set is part of the definition. And it is relative: your share can fall while your mentions rise, if competitors grow faster. For the short version, see share of voice in AI. Pineprompt's share of voice scoring feature and the metric docs describe how it is calculated there.

Choose the prompts

Use 30 to 100 prompts that a buyer would type. Mix three kinds:

Category prompts: "What are the best invoicing tools for freelancers?"
Comparison prompts: "Compare Tool A and Tool B for a small team."
Problem prompts: "How do I stop chasing late invoices?"

Keep branded and non-branded prompts apart. A prompt that names your brand will inflate your share. Measure share of voice on non-branded prompts, and use branded ones for a separate check on what assistants say about you.

Freeze the wording. If you change a prompt, start a new series. The prompt fan-out generator can help you find more phrasings for the same need.

Choose the competitor set

List 3 to 8 brands that buyers actually consider, not only the ones you consider rivals. Take them from the answers themselves: run the prompts first, and note which brands appear. Add the ones that show up often.

Decide up front how to treat brands outside your list. Either ignore them, or group them as "other". Pick one rule and keep it. Adding or removing a competitor changes everyone's share, so record the date of any change.

Three ways to count

Mention share. Every named brand counts once per answer. This is the simplest and the one used in the definition above. It treats the first recommendation and the fifth as equal.

Position-weighted share. Give more weight to brands listed earlier, for example 3 points for first, 2 for second and 1 for any later place. Share is your points divided by all points. It rewards being recommended first. See position in answer.

Citation share. When an engine shows sources, count how often your domain is cited compared with competitor domains. This measures who supplies the evidence, which is not the same as who is named. See mention vs citation.

Report all three if you can. They answer different questions: are we named, are we named early, and are our pages used.

Sampling and repeated runs

AI answers change from run to run, even for the same prompt on the same day. This is answer volatility. One run per prompt gives a noisy number.

Run each prompt at least three times per period, and more for the prompts that matter most.
Start each run in a new chat so earlier turns do not carry over.
Average across runs. Count the share per run, then average, or pool all mentions. Pick one method.
Treat small weekly moves as noise. A steady change over four weeks or more is a signal.

If you track daily, compare rolling averages, not single days.

Per-engine and blended figures

Engines name different brands. A brand can have a strong share in one assistant and almost none in another. A single blended figure hides that.

Report share for each engine first. Then, if you need one number, blend them with a stated rule: an equal average, or weights based on where your buyers actually search. Whichever you choose, keep it fixed and show the per-engine figures beside it. Engines that cite sources and engines that answer from memory also behave differently, so do not mix them up when reading citation share.

Country and language

Answers depend on location and language. A prompt in English asked from the US can name different brands than the same prompt in German asked from Germany. Local brands appear in their home market, and some engines show different answers or sources by region.

Treat each country and language pair as its own series, with its own prompts written natively, not machine translated. Do not average markets together unless that is the question you are asking.

A worked example

All names and numbers here are hypothetical. Larkspur is a fictional invoicing tool compared with three fictional rivals. The set is 50 non-branded prompts, run 3 times on each of two engines, A and B. That gives 150 answers per engine.

Mention counts across all answers:

Brand	Mentions	Mention share
Larkspur	60	15%
Orbitly	140	35%
Quillnet	120	30%
Tandem Works	80	20%
Total	400	100%

Now weight by position (3, 2, 1 points). Larkspur is usually listed late, so its points are 110 out of 810, a share of 13.6%. Orbitly, often listed first, has 330 points, or 40.7%. Quillnet has 240 (29.6%) and Tandem Works 130 (16.0%). Larkspur looks slightly worse once position counts.

Citation share, from 200 cited links to these four brands' domains: Larkspur 20 (10%), Orbitly 70 (35%), Quillnet 70 (35%), Tandem Works 40 (20%).

Per engine, Larkspur has 50 of 200 mentions on engine A, which is 25%, and 10 of 200 on engine B, which is 5%. The blended equal average is 15%, which matches the pooled figure but hides a ten-fold gap between engines.

What this tells Larkspur: it is named in some places but late, rarely cited, and nearly absent on engine B. That is three different problems.

Common mistakes

Changing the prompt or competitor set mid-series. The trend then reflects your edits.
Including branded prompts. They push your share up without telling you about new buyers.
One run per prompt. You will chase noise.
Only a blended number. It hides the engine where you are missing.
Mixing markets. Different countries and languages are different series.
Treating mention share as sales. It measures visibility in answers, not revenue.
Counting a brand name inside a negative or wrong sentence as a win. Check how you are described; a wrong claim is a brand hallucination.

How to act on the results

Start with prompts where competitors appear and you do not. Each is a content or coverage gap.
Look at the engines with the lowest share, then at the sources they cite. If the same review sites or directories keep appearing, get listed and described accurately there.
If your mention share is fine but position is low, improve how clearly your pages state who you are best for.
If mentions are high but citations low, publish pages that answer the prompt directly, with facts that are easy to quote.
Make one change at a time where you can, and compare after at least four weeks.

Doing this at scale

By hand, 50 prompts at three runs on several engines is hundreds of answers per period. Pineprompt tracks 8 engines daily: ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, Google AI Mode, Grok and Copilot. It stores each answer and calculates share of voice for you. The methodology page explains how runs are made and counted. There is no free trial; see pricing for plans.

Summary

Share of voice is your mentions divided by all brand mentions in a fixed prompt set. Fix the prompts and competitors, repeat runs, report per engine and per market, and look at mention, position and citation share together. Then act on the specific gaps the numbers show.