Generative engine optimization (GEO) tools track how often a brand appears in answers from AI assistants such as ChatGPT, Gemini and Perplexity. This comparison covers nine of them: Profound, Peec AI, Otterly.AI, Scrunch, AthenaHQ, Rankscale, Semrush's AI Visibility Toolkit, Ahrefs Brand Radar and Genezio.
On the surface, these tools look easy to compare. Most of them report a visibility percentage and a share of voice. But a visibility score of 40% in one tool and 40% in another can describe different things. The answers behind them may be collected in different ways, and the plans are sold in different units.
So the useful question is not which tool shows the best numbers. It is how to compare GEO tools on equal terms. A fairer comparison sets the headline numbers aside and looks at three things:
- Where answers are collected from. Does the tool read the consumer app, or call the model directly?
- How each metric is calculated. Same name, different formula.
- How many answers a plan actually buys. Prompts, credits, checks and conversations are different units.
How the facts were checked
Every fact below comes from the vendor's own pricing pages, documentation or changelog, checked on 10 October 2026. Each one falls into one of three kinds of evidence:
- Documented capability: the vendor states it as a product fact in its docs, pricing page or changelog.
- Vendor claim: a marketing statement that can't be checked from outside, such as Profound's line that "the data reflects what real users actually see."
- Independently verified finding: confirmed by a source with no commercial stake.
No capability of any of the nine vendors has been independently verified. We found no independent evaluation of any tool's accuracy or completeness. Everything in the tables is therefore documented capability, not proof of performance.
"Not documented" means the vendor publishes nothing on that point. It does not mean the feature is missing.
What each tool documents
The comparison is split into two tables with the same vendor rows. The first covers what is tracked and how. The second covers metrics, limits and price. A few terms come up in both:
- Answer engine: an AI product that answers questions directly, such as ChatGPT or Google's AI Overviews and AI Mode.
- Share of voice (SoV): broadly, your brand's portion of all brand mentions.
- API (application programming interface): a way for your own systems to pull data from the tool.
- MCP (Model Context Protocol): a way for AI assistants to query the tool.
- SSO (single sign-on): logging in through your company's identity provider. SAML and OIDC are standards for it, and SCIM keeps user accounts in sync with that provider.
Table 1: Coverage, collection method, integrations and updates
Checked 10 October 2026.
| Vendor | Answer engines (documented) | Collection method (as documented) | Markets | Integrations | Recent dated updates |
|---|---|---|---|---|---|
| Profound | Enterprise: up to 9: ChatGPT, Perplexity, AI Mode, Gemini, Copilot, DeepSeek, Claude, AI Overviews, Exa. Trial: 3. A second Profound page lists Grok instead of DeepSeek and Exa | Consumer browser capture, "rather than from model APIs" | Trial: 1 region, 1 language. Enterprise: custom | API, CSV/JSON export, SSO (Enterprise); web log integrations; MCP and SCIM | 9 Oct 2026: Moz SEO data. 4 Sep 2026: Exa engine |
| Peec AI | Self-serve: choose 3 of ChatGPT, AI Mode, AI Overviews, Copilot, Gemini, Naver AI. Perplexity as add-on. Enterprise: up to 13, incl. Claude, Grok, DeepSeek | Browser automation, logged out. Some enterprise models labelled "API" | Any supported country or language, no extra cost | API, MCP, Looker Studio (Advanced+), CSV, SSO (Enterprise), Shopify, web logs | 3 Aug 2026: Brand Perception generally available; Ads page (ChatGPT) |
| Otterly.AI | Base: ChatGPT, AI Overviews, Perplexity, Copilot. Add-ons: AI Mode, Gemini, Claude | Public web interfaces; Claude via API | 50+ countries on all plans | API and MCP (Standard+), Looker Studio (Standard+), SSO (Enterprise) | 5 Oct 2026: sentiment and position on main chart. 16 Sep 2026: ChatGPT Ads reporting paused |
| Scrunch | Core: ChatGPT, Perplexity, AI Overviews, Copilot. Enterprise: 9, incl. Claude, Gemini, AI Mode, Grok | Mix of browser automation and official APIs, chosen per platform. Split not documented | Core: 1 country, 1 language. Enterprise: unlimited | Core: Google SSO. Enterprise: API, Looker Studio, MCP, SAML/OIDC SSO | 3 Jun 2026: acquired by Sitecore, continues standalone. Product changelog not documented |
| AthenaHQ | Starter: 11, incl. ChatGPT, Perplexity, AI Overviews, AI Mode, Gemini, Claude, Copilot, Grok. Free tier: 5 | Not documented | Starter: single region and language. Enterprise: multiple | Google Analytics, Search Console, Shopify, Webflow, CSV. Enterprise: BI tools, SSO | Not documented |
| Rankscale | ChatGPT, Perplexity, AI Mode, AI Overviews, Gemini, DeepSeek, Mistral, Claude, Copilot, Grok, plus Meta Muse Spark | Not documented, except AI Overviews from "live rendered snapshots" | All regions | MCP, Data Studio, CSV/Sheets, REST API (Pro+), white-label links | 9 Oct 2026: ChatGPT tracking back in Czechia. 2 Oct 2026: new GPT models |
| Semrush | Prompt Tracking: AI Mode, AI Overviews, Gemini, ChatGPT Search. Brand Performance report adds Perplexity | "Captured from real requests and not via any APIs of LLMs" | Prompt database: 32 countries | MCP, API (Advanced), CSV export, SSO (Enterprise) | 14 May 2026: prompt database to 32 countries |
| Ahrefs Brand Radar | AI Overviews, AI Mode, ChatGPT, Perplexity, Gemini, Copilot. Grok paused. Claude for custom prompts only | Public web interfaces, default model, no user context. Claude via API | Location set per prompt; strongest in English | API and MCP on paid plans, Report Builder | 13 Jul 2026: Claude for custom prompts. 24 Apr 2026: Grok added |
| Genezio | Enterprise: ChatGPT, Perplexity, AI Mode, Gemini, Copilot, Meta AI, Grok, DeepSeek, Claude, AI Overviews. Agency: up to 5 per brand | Simulated multi-turn persona conversations from in-country IPs. Consumer app vs API not documented | Custom languages and locations | API, MCP, SSO/SAML, SCIM; Claude connector, web logs, Google Analytics, Search Console | Sep 2026: Google Analytics and Search Console. Jun 2026: SoV metric, in-country proxies |
Table 2: Metrics, volume limits and pricing
Prices as published on 10 October 2026, in US dollars unless stated.
| Vendor | Headline metrics | Volume unit and entry-plan limit | Pricing model (entry price as published) |
|---|---|---|---|
| Profound | Visibility Score, SoV, Average Position, citation share, sentiment, accuracy | Prompts, run daily. Trial: 50 prompts for 7 days. Enterprise: custom | Free 7-day trial; Enterprise custom. Self-serve price not documented |
| Peec AI | Visibility, SoV, Position, sentiment, win rate | "AI answers" = prompts × models × daily runs. Starter: 50 prompts, 3 models, 4,500 answers a month | Monthly tiers by prompts and models; Enterprise custom. Plan prices not verified |
| Otterly.AI | Brand Coverage, SoV, Avg. Brand Position, Brand Visibility Index | Prompts, run daily. Lite: 15 prompts, 4 engines | Lite $29/mo; Standard $189; Premium $489. Engine add-ons extra |
| Scrunch | Presence, position, sentiment, SoV. Formulas not documented | Prompts and responses. Core: 125 prompts, 5,000 responses a month | Core $250/mo with 7-day trial; Enterprise custom |
| AthenaHQ | AI share of voice in mention, citation and position variants. Full formula not documented | Credits: 1 credit = 1 AI response. Starter: 3,600 a month | Free tier; Starter $295/mo; Enterprise custom. API is a paid add-on on Starter |
| Rankscale | Visibility Score, Detection Rate, Avg. Position, Top-3, SoV, sentiment | Credits, about 0.25 per engine query; varies by engine. Pro: 1,200 credits, "up to 4,800 answers" on the cheapest engines | Essentials from $20/mo, yearly only; Pro $99/mo; higher tiers to $780 |
| Semrush | AI Visibility, SoV, sentiment. Formulas not documented | Prompts, run daily. Toolkit: 25 prompts, 1 project | Toolkit $99/mo, no trial. Semrush One from $199/mo |
| Ahrefs Brand Radar | Mentions, citations, AI SoV, Estimated Impressions. SoV formula not documented | Checks: 1 prompt × 1 platform × 1 location × 1 update. Ahrefs Lite plan: 5 tracked prompts, 150 checks. A Claude check costs 8 | Ahrefs Lite plan $129/mo includes the 5 tracked prompts. Full Brand Radar AI index from $199/mo, standalone available. Custom prompt packages from $50/mo |
| Genezio | Brand Visibility, Brand Recommendation, SoV | Conversations. About 3,000 a month for about 30 scenarios. No published plan limit | Custom enterprise pricing, based on monthly conversation volume. Self-serve price not documented |
Two gaps stand out. Three vendors do not say whether they read the consumer app or call an API: AthenaHQ, Rankscale (outside AI Overviews) and Genezio. And no vendor has an independent check of its data. Of all the columns, collection method is the one that matters most and is least understood.
Does it matter whether a tool reads the consumer app or calls an API?
There are two ways to ask an AI model a question at scale.
Consumer answer-engine monitoring types the prompt into the public website or app, as a person would. The answer can include web search results, interface-specific behaviour and effects of the user's location.
API-based model testing sends the prompt straight to the model through the developer API. That is cheaper and easier to automate. But the answer may not include the search step or other behaviour of the consumer product.
The vendors split across both approaches, and several mix them per engine:
- Profound says it captures answers from the consumer browsing experience, not model APIs. Semrush says its prompt data comes from real requests, not LLM APIs.
- Peec AI uses logged-out browser automation, but labels some enterprise models "API."
- Otterly.AI and Ahrefs use public web interfaces for most engines, and an API for Claude.
- Scrunch uses both methods, chosen per platform, without a public breakdown.
- AthenaHQ and Rankscale do not document a method. Rankscale's one exception is AI Overviews.
- Genezio documents simulated multi-turn conversations from in-country IP addresses, but not whether they go through the consumer app or an API.
These are all vendor statements. Profound says its browser capture means "the data reflects what real users actually see." That is a vendor claim, not a verified result.
Does the difference show up in the data? Only two published tests compare the two surfaces directly, and neither is independent.
The first is a study by Genezio, one of the vendors in this comparison, from February 2026. It compared GPT-5.2 through the API with ChatGPT's web app across 3,645 conversations about UK banking. The web app was not necessarily running the same model. The study looked at fan-out queries, the background web searches the model runs to build an answer. Not one of the 3,856 unique fan-out queries appeared on both surfaces. Only 12.3% of source URLs were used by both.
The second is a self-published test by Windsor.ai, a data-integration company that measured its own brand in August and September 2026. It compared the ChatGPT API, running gpt-5-mini with web search on, against the logged-in app, whose model was not recorded. One other tool, Pipedream, appeared in 59% of API answers. It appeared in none of the app answers. The app sample was small, with 90 answers in the final round.
Both tests cover one market, one engine and a short period. Both come from parties with an interest in the result. Because the two sides may also have used different models, not all of the gap can be put down to the surface. Still, the tests suggest that how answers are collected can change them a great deal. They do not prove how large the gap is for your brand, engine or market.
What this means: ask each vendor, in writing, which surface it uses for each engine on your plan. A single answer for the whole tool is not enough when the method can change per engine.
Why isn't "visibility 40%" in one tool the same as 40% in another?
Even when two tools read the same surface, they turn answers into numbers differently. "Visibility" generally means the share of something in which your brand appears. The "something" is where vendors diverge.
| Vendor | Metric name | Counts | Out of | Adjustment |
|---|---|---|---|---|
| Peec AI | Visibility Score | Responses mentioning the brand | All responses | None |
| Profound | Visibility Score | Responses including the brand | Responses that mention at least one brand | None |
| Otterly.AI | Brand Coverage | Prompts that mention the brand | All prompts in the period | None |
| Rankscale | Visibility Score | Appearances | All runs | Penalty for lower positions |
| Genezio | Brand Visibility | Conversations where the brand appears | Eligible conversations only | Shown with an accuracy range |
| Semrush | AI Visibility | Not documented | Not documented | Combines topic coverage and mention consistency |
| Ahrefs, Scrunch, AthenaHQ | — | Formula not documented | — | — |
Each difference moves the number in a predictable way.
Profound's denominator leaves out answers that name no brand at all. Applied to the same answers, it gives an equal or higher score than Peec's all-responses formula.
Otterly.AI counts prompts rather than individual responses.
Rankscale multiplies the appearance rate by a penalty for lower positions. In its own example, a brand appears in 80% of runs at an average position of 2. Its Visibility Score is about 72.7%, not 80%. Rankscale's Detection Rate is the closer match to other tools' visibility.
Genezio excludes conversations whose prompt already contains the brand name, such as a direct comparison. It also runs until a set accuracy threshold is reached, rather than using a fixed sample size.
Share of voice comes closest to a shared definition. Peec AI, Otterly.AI and Genezio all document it as your brand mentions divided by all brand mentions. Profound's wording mixes responses and mentions. Even the shared formula depends on which competitors are counted. Peec's Position metric, for example, counts every brand detected, not just the competitors you track.
What this means: compare trends within one tool over time. Do not compare levels across tools. A move from 30% to 40% in one dashboard is meaningful. A 40% in one tool versus 30% in another is not evidence that either brand is doing better.
How do you compare price when every vendor sells a different unit?
Metric definitions explain the numbers. What you pay depends on volume, and volume is sold in five different units: prompts, credits, checks, responses and conversations.
The common unit is answers per month:
answers per month = prompts × engines × runs per month × markets
Here is how published plans convert, as listed on 10 October 2026:
- Peec AI Starter does the conversion itself. It runs 50 prompts daily across 3 models. That gives 4,500 answers a month.
- Scrunch Core covers 125 prompts on 4 engines, with responses capped at 5,000 a month. By our calculation, that allows about 10 runs per prompt and engine each month. That matches Scrunch's default cadence of one run every 72 hours. New prompts run daily for their first 14 days, though, and at that pace a full set of 125 prompts would exceed the cap.
- AthenaHQ Starter includes 3,600 credits a month, and one credit buys one AI response.
- Rankscale Pro includes 1,200 credits, which the vendor says covers "up to 4,800 answers." That figure assumes the cheapest engines at a quarter of a credit each. Newer GPT models cost 1 credit per run, or 3 with web search, so the real answer count depends on the engines you pick.
- Otterly.AI Lite tracks 15 prompts daily on 4 engines. By our calculation, that is about 1,800 answers in a 30-day month.
- Ahrefs charges one check per prompt, platform, location and update. A Claude check costs 8, because Claude runs through an API with web search.
- Genezio quotes about 3,000 conversations a month for 30 scenarios. Each conversation can contain several turns, so it is not the same unit as a single answer.
Answer counts are only half the picture. Check what the entry plan leaves out:
- Otterly.AI sells Gemini, AI Mode and Claude as add-ons.
- Peec AI's self-serve tiers cover 3 chosen models, with Claude only on Enterprise.
- Scrunch Core covers one country and one language.
- AthenaHQ Starter charges extra for API access, and Semrush puts its API on the Advanced plan.
- Ahrefs refreshes chatbot answers in its own prompt index monthly. Custom prompts can run more often, but each run uses checks.
Do not convert these into a price per prompt across vendors. A prompt in one tool may cover more engines, markets or runs than a prompt in another. Convert to answers per month for your own engines and markets, then compare.
How do you shortlist and test a GEO tool?
The three lenses above turn into a short buying checklist:
- List the engines your buyers actually use. Confirm each one is on the plan you would buy, not an add-on or enterprise-only.
- Get the collection method per engine in writing. Ask whether each engine is read from the consumer app or an API, and whether the session is logged in.
- Get the metric formulas. Ask what counts in the numerator and denominator of visibility and share of voice.
- Convert to answers per month. Use your own engines, markets and refresh frequency.
- Check that you can export raw answers. An API or CSV export lets you audit the numbers rather than trust the dashboard.
- Spot-check during the trial. Run a handful of your own prompts by hand in the consumer apps, and compare them with what the tool reports.
No tool in this comparison is the right default. A brand that needs Claude coverage, ten markets and raw exports will land on a different shortlist than an agency tracking ChatGPT for many small clients. The tables are a starting point for that conversation. What settles it is the vendor's written answers to these six questions, and your own test.