The Best LLM Visibility Tools in 2026 (Tested & Compared)

You ask ChatGPT the exact question your best customers ask, and your brand never comes up — a competitor does instead. You want a way to measure that, track it over time, and fix it if you can. The catch: "LLM visibility tool" is a label stuck on three different products that measure three different things.

What is an LLM visibility tool?

An LLM visibility tool measures how often — and how favorably — large language models like ChatGPT, Claude, and Gemini mention or recommend your brand when users ask buying questions. That's the whole category in one sentence, and it's worth sitting with, because most tools wearing this label actually measure something else.

A large language model (LLM) is the AI system behind ChatGPT, Claude, Gemini, and similar products — trained on huge volumes of text to generate an answer rather than return a ranked list of links. Ask one "what's the best CRM for a five-person team" and it doesn't crawl the web live and rank pages. It draws on training data plus, increasingly, live retrieval — and it either says your name or it doesn't.

This sits inside the broader idea of AI visibility: how discoverable and citable your brand is across AI-driven surfaces generally, from search generative answers to chat assistants. "LLM visibility" is the narrower slice — specifically the chat models, specifically the mention.

Here's the split most buyer's guides skip. Some tools measure real model output: actual prompts, actual answers, actual mentions, run on a schedule. Others measure your crawlability: whether GPTBot and friends can technically reach your pages, then call that "visibility" because it's cheaper to build. Only the first one tells you whether the model is naming you.

The distinction matters in practice. A page can be perfectly crawlable, fast, and well-structured, and still never get mentioned when someone asks an LLM for a recommendation — because the model is weighing training data, reviews, comparison content, and citation patterns, not just whether your sitemap is clean. Crawlability is necessary. It isn't the same as being recommended.

The best LLM visibility tools in 2026

The tools worth evaluating in 2026 split into dedicated AI-visibility platforms built for marketing and brand teams — Profound, AthenaHQ, Peec AI, Otterly, and AEOeye come up most often — plus DIY approaches that hit model APIs directly. Here's how the dedicated platforms compare on paper.

Pricing above reflects general positioning, not locked numbers — this category moves fast, and more than one vendor has shifted from flat pricing to usage-based tiers in the past year. Check each vendor's own site before you buy anything.

The DIY route — calling the ChatGPT, Claude, or Gemini APIs yourself and logging the responses — works too, and it's cheaper in dollars if you already have engineering time to spare. What it costs is consistency: someone has to maintain the prompt list, re-run it on schedule, and build the sentiment scoring by hand. Most teams start there, feel the maintenance burden after a month, and move to a dedicated tool.

For a longer walkthrough — feature notes and where each tool sits on the crawlability-vs-mentions divide — see our fuller AI visibility tools roundup. This piece stays narrow: does the tool tell you what the model actually said about you, or just whether it could?

How LLM visibility is actually measured

Most tools run a fixed set of buyer prompts across models on a schedule, then score mention rate, sentiment, and share of voice. The real differences between vendors come down to prompt design, run frequency, and how transparently they show you the raw model output behind the score.

Three numbers do most of the work:

• Mention rate — the percentage of relevant prompts where the model names your brand at all, whether or not it recommends you.
• Share of model — your mentions as a percentage of all brand mentions across that answer set, the LLM equivalent of share of voice.
• Sentiment — whether the model talks about you positively, neutrally, or negatively on the occasions it does mention you.

None of this happens by magic. A vendor has to choose which prompts to run — generic category questions versus your actual buyer language — how often to re-run them as models update and answers drift, and how many engines to cover. A tool that only checks ChatGPT is measuring a slice of the picture; Perplexity, Gemini, and Google's AI Overviews increasingly answer the same buying questions with different sources cited.

Run frequency matters more than people expect. A single snapshot tells you where you stand today; a weekly or monthly cadence tells you whether a content push, a PR mention, or a competitor's campaign actually moved the number. Check once and you're just guessing at cause and effect.

It's also worth treating "AI answers are eating clicks" claims with some caution — the scale varies a lot by query type and industry, and vendors have an incentive to make the threat sound bigger than it is. We go deeper on methodology, including how to read those claims skeptically, in how AI visibility is actually measured.

What to look for when choosing one

Before you commit to a subscription, check five things: engine coverage, real prompts instead of generic templates, whether you can see raw model output or just a black-box score, pricing that matches your team's size, and a free tier to test before you pay.

1. Engine coverage. ChatGPT alone isn't the market. Check whether the tool also covers Perplexity, Gemini, Google AI Overviews, and Claude — buyers use all of them, and each model tends to cite different sources.
2. Real prompts, not just keywords. A tool running your actual buyer questions ("best [category] for [use case]") tells you more than one that just flags whether your brand name shows up somewhere in a generic category answer.
3. Verifiable output, not a black box. Ask to see the transcript behind the score. If a vendor only hands you a number with no underlying answer text, you can't audit it, and you can't act on it.
4. Price that matches how often you'll actually check. Weekly enterprise monitoring is a different budget than a monthly gut-check. Match the plan to how the results get used on your team, not the other way around.
5. A free tier or sample report. You should be able to see what the output looks like before committing to a subscription. A vendor unwilling to show a sample is telling you something.

The fastest way to see where you stand

If you haven't measured this at all yet, skip the subscription decision for now. Run one free audit across the major engines, see what actually comes back, and decide from there whether ongoing monitoring is worth paying for.

That's what AEOeye's free audit does: enter your brand or URL, and it checks how ChatGPT, Perplexity, Gemini, Google AI, and Claude answer buying questions in your category — in about a minute, with results visible before any subscription decision. You can also look at an example report first, so you know exactly what you're getting before you run your own.

Full monitoring — scheduled re-runs, competitor tracking, trend lines over months — is a reasonable thing to pay for once you know the free snapshot is worth acting on. But you shouldn't have to guess whether that's true before you've seen a single real answer from a real model.

Run your free AEOeye audit and see your visibility across ChatGPT, Perplexity, Gemini & Google AI — no credit card required, results in about a minute.