Best LLM Tracking Tools in 2026: 7 Tools Compared

If you want to know whether ChatGPT, Claude, or Gemini recommends your brand, you need an LLM tracking tool. Checking prompts by hand gives you one answer from one session. A tracker runs the same prompts on a schedule, across several models, and shows you the pattern.

The hard part is choosing one. Most tools look similar on a feature list, but they differ in which models they cover at each price, how they collect answers, how many prompts you get, and whether they help you act on the data.

Short answer: the best LLM tracking tools in 2026 depend on the job you need done:

• Visby fits teams that want tracking plus prioritized GEO tasks, with ChatGPT, Claude, Gemini, and Google AI Overviews to choose from.

• Profound fits enterprise teams that need up to nine answer engines, SSO, and API access.

• Peec AI fits agencies and multi-market teams that want interface-level data and unlimited seats.

• Scrunch fits teams that want Claude, Gemini, and Meta coverage plus AI agent traffic monitoring on a self-serve plan.

• Semrush AI Visibility Toolkit fits teams that already run their SEO in Semrush.

• Ahrefs Brand Radar fits researchers who want to look up any brand across a large pre-built prompt index.

• Otterly AI fits solo marketers who want the lowest entry price.

The rest of this guide explains how LLM tracking works, the criteria we used, and where each tool is strong or limited. Pricing reflects vendor information available in September 2026 and can change.

What Is LLM Tracking?

LLM tracking is the practice of repeatedly asking large language models a fixed set of prompts and recording whether your brand is mentioned, recommended, or cited in their answers. An LLM tracking tool automates that process across models such as ChatGPT, Claude, Gemini, and Perplexity, then turns thousands of individual answers into trend lines you can act on.

The output usually covers four things: brand mentions, citations (which URLs the model used as sources), position within the answer, and sentiment. Most tools also show the same data for competitors, which is how share of voice is calculated.

LLM tracking is the measurement layer of Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO). GEO and AEO describe the work of earning a place in AI answers. LLM tracking tells you whether that work is paying off. If you want the optimization side as well, our guide to tools for generative engine optimization covers platforms that go beyond monitoring.

How Do LLM Tracking Tools Work?

Most LLM tracking tools follow the same five-step loop:

1. Build a prompt set. You (or the tool) define the questions buyers ask, such as “best CRM for a 10-person sales team.”

2. Run the prompts on each model. The tool sends every prompt to each selected LLM on a schedule, daily or weekly depending on the plan.

3. Parse the answers. Each response is scanned for your brand, competitor brands, cited URLs, and the tone used to describe each brand.

4. Aggregate the results. Individual answers become metrics such as mention rate, share of voice, and citation frequency.

5. Report changes over time. Trend views show whether visibility is rising or falling after content changes, model updates, or competitor moves.

Why Does the Same Prompt Give Different Answers?

LLM answers are not fixed. Ask ChatGPT the same question twice and the brands it lists can change, because models generate text probabilistically and many answers also pull in live web results that shift from day to day.

Answers also differ by model, model version, user location, language, and whether web search was used. This is the main reason one-off manual checks are unreliable. A single screenshot shows one sample, not your real visibility.

Good LLM tracking tools deal with this in three ways: they run each prompt more than once or on a regular schedule, they report rates (for example, mentioned in 6 of 10 runs) rather than a single yes or no, and they keep history so you can separate real trends from noise.

API Data vs Interface Data: Why Collection Method Matters?

LLM tracking tools collect answers in one of two ways. Some query the model through its developer API. Others capture the answer from the consumer chat interface, the same screen a real user sees.

The two can differ. Consumer interfaces may add web search, shopping results, citations, or memory features that a plain API call does not include. Neither method is wrong, but you should know which one a vendor uses before comparing its numbers with another tool’s.

How Is LLM Tracking Different From SEO Rank Tracking?

The two disciplines still overlap. Pages that perform well in classic search are often the ones AI systems retrieve, so many teams run both. If your team wants that combined view, compare AI-powered SEO tools that bring traditional and AI search data together.

How We Evaluated These LLM Tracking Tools?

We compared each tool on six criteria that change the quality of LLM tracking data, not just the look of the dashboard:

1. Model coverage by plan. Which LLMs are included at the entry price, and which require an add-on or an enterprise contract. Claude is the most common gap.

2. Prompt allowance and refresh rate. How many prompts you can track and how often they run. More runs per prompt give a more reliable mention rate.

3. Collection method. Whether answers come from the consumer interface, the API, or a pre-built prompt index.

4. Depth of analysis. Whether the tool reports citations and source URLs, sentiment, and position, or only raw mentions.

5. Competitor benchmarking. Whether share of voice is shown against named competitors on the same prompts.

6. Action layer and integrations. Whether findings become tasks or recommendations, and whether data connects to GA4, Search Console, Looker Studio, an API, or MCP.

We did not score tools on a single “best overall” scale. A tool that suits an enterprise with 500 prompts in nine markets is rarely the right tool for a startup with 20 prompts. Teams whose main goal is optimizing content for answer engines, rather than measuring it, can also compare dedicated answer engine optimization tools.

Best LLM Tracking Tools Compared at a Glance

This table answers one question: what do you get at each tool’s lowest paid (or free) entry point? Prices are monthly in USD as listed in September 2026 and can change.

Two patterns stand out. First, prompt allowances at the entry tier range from 15 to 350, so compare cost per tracked prompt, not headline price. Second, Claude is still the model most often left out of entry plans, which matters if your buyers use it.

The 7 Best LLM Tracking Tools in 2026

1. Visby

Visby is an AI visibility platform that tracks brand mentions across LLMs and then turns the gaps it finds into GEO tasks. It is built for teams that want to know what to change after they see the data, not only where they stand.

On the tracking side, Visby shows which prompts trigger your brand, how mention frequency changes over time, and how competitors perform on the same prompts. Prompts are grouped by funnel stage, so you can see whether you lose visibility at the research stage or closer to a purchase decision. Competitor analysis highlights prompts where competitors appear and you do not, and measures their share of voice across platforms and funnel stages.

The part that separates Visby from monitoring-only trackers is the task layer. Visby scans your site, generates technical and content GEO tasks based on your visibility gaps, and prioritizes them by likely impact. It also connects to GA4, Google Search Console, and Bing, so you can see which pages receive AI-referred traffic and what that traffic is worth. Review tracking across G2, Trustpilot, and Reddit covers the third-party sources LLMs often draw on. For teams that prefer to query their data from an AI assistant, Visby also offers an MCP server.

Best for: SMBs, SaaS teams, and agencies that want LLM tracking tied to a clear list of next actions.

Limitations: each plan includes 3 engines, selected from ChatGPT, Claude, Gemini, and Google AI Overviews. Analyzed answers are capped per month (45 on Starter), so teams that need high-frequency sampling on many prompts should size the plan to match.

Pricing: Starter is $79/month (1 domain, 15 tracked prompts, 45 analyzed AI answers per month, 1 seat). Growth is $199/month (3 domains, 90 prompts, 270 answers, 3 seats). Enterprise is quoted and adds 250+ prompts, 10+ seats, and an expert GEO audit. See the Visby pricing page.

2. Profound

Profound is an enterprise AI visibility platform with the widest engine list in this guide. Its Enterprise plan can track up to nine answer engines: ChatGPT, Perplexity, Google AI Mode, Gemini, Microsoft Copilot, DeepSeek, Claude, Google AI Overviews, and Exa Search.

Beyond prompt tracking, Profound offers Prompt Volumes (estimates of what users ask AI engines), ChatGPT Shopping visibility, and Agent Analytics, which connects to CDNs and hosting platforms such as Cloudflare, Vercel, and AWS to measure AI crawler and referral traffic. Enterprise includes all-time history, CSV and JSON exports, an API, and SSO with SOC 2 compliance. The company has also moved heavily into content automation through its AI Marketer and Agents features.

Profound’s packaging has changed several times in 2026. Many roundups still quote $99 Starter and $399 Growth plans, but the Profound pricing page checked in September 2026 lists only a free trial and a custom Enterprise plan.

Best for: large brands that need many engines, multiple markets, security reviews, and data warehouse integration.

Limitations: the free trial runs 50 prompts daily for 7 days on ChatGPT, Gemini, and AI Overviews, uses a recommended prompt set you cannot edit, and includes no history or exports. Claude and custom prompts require Enterprise.

Pricing: free 7-day trial, then custom Enterprise pricing.

3. Peec AI

Peec AI is a monitoring-first platform popular with agencies. Its main technical choice is collection method: Peec states that it captures most engines through the consumer interface rather than the API, so answers reflect what a real user sees, including web search results and citations.

Peec reports visibility, share of voice, sentiment, and position for each model. It separates sources (every URL a model accessed) from citations (URLs shown in the visible answer), which is a useful distinction when diagnosing why a page is read but not credited. A Query Fanouts view shows the background searches ChatGPT, Perplexity, and Copilot run while composing an answer. Data can be exported to CSV and Looker Studio, or queried through Peec’s MCP server.

Best for: agencies and multi-market brands that want clean reporting, unlimited seats, and interface-level data.

Limitations: each self-serve plan tracks 3 models chosen from six defaults (ChatGPT, AI Overviews, AI Mode, Perplexity, Gemini, Copilot). Claude Sonnet and Claude Haiku are paid upgrade models. Peec deliberately does not generate content, so execution happens elsewhere.

Pricing: Starter $95/month (50 prompts, 1 project), Pro $245/month (150 prompts), Advanced $495/month (350 prompts), per Peec’s published pricing as of July 2026. Annual billing saves about 15%. Agency plans start at $245/month.

4. Scrunch

Scrunch has the broadest model list among the self-serve plans in this guide: ChatGPT, Claude, Gemini, Perplexity, both Google AI surfaces, and Meta. That makes it a practical option for teams that need broad model coverage without an enterprise contract.

Every plan includes a Prompt Manager, citation tracking, page audits, personas for simulating different buyer types, and agent traffic monitoring to attribute AI-driven visits. Scrunch also sells AXP (Agent Experience Platform), which serves an AI-friendly version of your site to AI agents.

Best for: mid-market and enterprise teams that want wide LLM coverage and AI crawler insight in one product.

Limitations: the entry price is higher than most tools here. The Enterprise Data API and SAML or OIDC SSO are Enterprise-only. Third-party reviewers reported that Scrunch showed different plan lineups during 2026, so confirm the current plan before buying.

Pricing: Starter $250/month billed annually or $300 month-to-month (350 custom prompts, 3 users). Growth $417/month billed annually or $500 month-to-month. 7-day free trial with no card, per the Scrunch pricing page.

If Claude visibility is your main concern, our comparison of tools for tracking visibility in Claude looks at Claude coverage in more depth.

5. Semrush AI Visibility Toolkit

The Semrush AI Visibility Toolkit adds LLM tracking to the Semrush ecosystem. It covers brand visibility benchmarking, sentiment compared with competitors, prompt research, daily prompt tracking, and an AI Search site audit that flags technical issues blocking AI crawlers.

Its value is convenience. If your team already reports rankings, backlinks, and traffic in Semrush, AI visibility sits next to that data instead of in a separate tool.

Best for: teams already committed to Semrush that want a first LLM tracking layer.

Limitations: the base toolkit includes only 25 tracked prompts and one domain. Extra prompts cost $60/month per 50, and extra domains cost $99 each. There is no free trial of the toolkit. Claude is not listed among the toolkit’s tracked platforms.

Pricing: $99/month per domain, according to Semrush’s knowledge base. Semrush One bundles SEO and AI visibility with 50, 100, or 200 tracked prompts depending on the plan.

6. Ahrefs Brand Radar

Ahrefs Brand Radar takes a different approach from the other tools. Instead of starting with your prompt set, it gives you an index of hundreds of millions of AI prompts, so you can look up how any brand, including a competitor, appears without setting anything up first.

For your own priority questions, you add custom prompts, which Ahrefs meters in “checks” (one prompt on one platform in one location). This two-layer model is useful for market research, competitor audits, and finding topics you did not know to track.

Best for: SEO and research teams already using Ahrefs who want broad market data alongside a smaller custom prompt set.

Limitations: the index reflects Ahrefs’ prompt selection, not necessarily your buyers’ exact questions. Custom tracking is metered, so costs grow with platforms, locations, and refresh frequency. Ahrefs has presented Brand Radar pricing differently across its pages in 2026, so confirm platform coverage, including Claude, before committing.

Pricing: reported from $199/month, with custom prompt packages starting at $50/month for 2,500 checks. Verify on Ahrefs’ pricing page.

7. Otterly AI

Otterly AI is the lowest-cost way to start LLM tracking. The Lite plan tracks 15 prompts daily across ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot, with unlimited team members and a GEO URL audit that checks crawlability and content structure.

Otterly is quick to set up and easy to read, which makes it a good fit for a first test before you know how many prompts you really need.

Best for: solo consultants, founders, and small teams starting out.

Limitations: 15 prompts is enough for a pilot, not a full program. Gemini, Google AI Mode, and Claude are paid add-ons on every tier, and API and MCP access start on Standard.

Pricing: Lite $29/month, Standard $189/month (100 prompts), Premium $489/month (400 prompts), as reported in September 2026 reviews. Confirm on Otterly’s pricing page.

Free Baselines: Bing Webmaster Tools

Bing Webmaster Tools includes an AI Performance report that shows how often your site is cited as a source in Copilot and Bing’s AI answers, your citation share, and the grounding queries behind each citation. It does not cover ChatGPT, Claude, or Gemini, and it reports citations rather than impressions. It is still a free, first-party data source worth checking alongside any paid tracker. We tested the report with real data and explain how to read it.

Which LLM Tracking Metrics Should You Watch?

The most useful LLM tracking metrics are mention rate, share of voice, and citation rate. Sentiment, position, and AI referral traffic add context. Vendors name these differently, so check each tool’s definitions before comparing numbers across platforms.

Mention rate and citation rate can move independently. A brand that is often named but rarely cited is recognized but not trusted as a source. A brand that is often cited but rarely named has useful content but weak brand association. Each pattern points to a different fix.

How Do You Choose the Right LLM Tracking Tool?

Start with three questions: which models do your buyers use, how many prompts do you need to cover your category, and who will act on the data. The answers usually narrow the list to two tools.

Three practical tips before you buy. Build your prompt list first, then check which plan actually covers it. Run a trial on the prompts you care about, not the vendor’s sample set. And compare two tools on the same 10 prompts for a week, because collection methods differ and so will the numbers.

For a wider view of platforms that combine monitoring with optimization features, see our comparison of AI search visibility tools.

How to Set Up LLM Tracking: A Worked Example?

The example below is hypothetical. It uses a fictional project management brand, “Tasklane,” to show how LLM tracking data turns into decisions. The numbers are illustrative, not customer results.

Step 1: Build a prompt set by funnel stage. Tasklane writes 30 prompts: 10 awareness prompts (“how do small agencies manage client projects”), 10 consideration prompts (“best project management tool for a 15-person agency”), and 10 decision prompts (“Tasklane vs Asana for agencies”).

Step 2: Choose models based on buyers. Sales calls suggest buyers use ChatGPT and Claude most, with some Google AI Overviews exposure. Those three models go into the tracker.

Step 3: Collect a baseline for two to four weeks. One week of data is too noisy, because answers vary between runs. A few weeks of runs give a stable mention rate per stage.

Step 4: Read the pattern, not single answers.

In this example, Tasklane wins when buyers already know its name but is almost invisible at the research stage. The citation report shows that competitors are cited from comparison articles and review sites where Tasklane has no presence.

Step 5: Turn gaps into actions. Tasklane publishes an explainer on agency workflows, requests inclusion in two comparison articles that models already cite, and asks existing customers for reviews on the platforms that appear in citations. It then keeps the same prompts running to measure the effect.

The key habit is keeping the prompt set stable. If you change prompts every month, you cannot tell whether visibility moved because of your work or because you measured something different. For a longer walkthrough of this process, see our guide on how to track brand visibility in AI search.

Frequently Asked Questions About LLM Tracking Tools

Is there a free LLM tracking tool?

There is no free tool that tracks ChatGPT, Claude, and Gemini continuously. Profound and Scrunch both offer 7-day free trials, and Bing Webmaster Tools provides free citation data for Copilot and Bing AI answers. Manual checks are free but capture only one sample per prompt.

How many prompts should an LLM tracker cover?

Most brands can start with 20 to 50 prompts spread across awareness, consideration, and decision stages. That is our recommendation, not a fixed rule. Add prompts when you enter a new market or product line, and keep the core set stable so trends stay comparable.

How often should LLM tracking data refresh?

Daily refresh gives the most reliable mention rates, because more runs smooth out answer-to-answer variation. Weekly refresh can work for smaller prompt sets or tight budgets. Whatever the frequency, judge performance on multi-week trends rather than a single day.

Can an LLM tracking tool explain why a model recommends a competitor?

No tool can see inside a model’s reasoning. What trackers can show is correlated evidence: which URLs and domains the model cited, how competitors are described, and which prompt types they win. That evidence is usually enough to choose where to publish, update content, or earn coverage.

Do I need to track every LLM?

No. Track the models your buyers actually use. For many B2B brands that means ChatGPT and Claude first, with Gemini or Google AI Overviews added when search-driven discovery matters. Adding models you do not need raises cost without improving decisions.