Best 5 LLM Mentions APIs 2026
A client asks why their brand never shows up when someone asks an AI assistant about “top tools in [category].” You open ChatGPT, type the prompt, and there it is: three competitors named, yours nowhere. Now you need proof, at scale, across models and countries, refreshed daily. Screenshotting chat windows doesn’t scale past one prompt. Building your own scraper means fighting rate limits, rotating proxies, and rebuilding parsers every time a model updates its output format. What you actually need is structured data: answers, citations, and a mentions history you can pipe into your own dashboard or client report. The real test is coverage across models, control over geo and prompt sets, and clean output your pipeline can parse without babysitting it.
How I Narrowed This List
I started from the integration side, not the marketing page. For each provider I checked whether the output was structured JSON with citations attached, or just rendered text that needs its own parsing layer. That single distinction eliminated a few candidates before pricing even entered the conversation.
I went through customer feedback on Trustpilot and G2 to see how teams actually rate these providers first-hand, looking for patterns in what broke versus what held up under daily polling volume. I also weighed how each vendor handles geo and model targeting: some let you lock a city and a specific model version, others only offer country-level and a generic “AI” bucket. Pricing transparency mattered too. If I couldn’t find a usage-based rate card without booking a call, that counted against a provider, since teams running daily volume need to model cost before committing engineering time.
Team seniority on the collection side came up repeatedly, since someone has to maintain scrapers against model updates, and that reliability shows up as uptime, not a feature list.
What Actually Separates These Providers
Coverage breadth across models
Some APIs only cover ChatGPT. Others stretch to Claude, Gemini, Perplexity, and Google AI Overviews in one call, which matters if your prompt set needs cross-model comparison rather than single-model tracking.
Structured output vs rendered text
A response with a citations array and a mentions-history field is worth more to a downstream pipeline than a wall of HTML that needs scraping twice.
Geo and prompt control
City-level targeting, not just country, changes what a local business actually sees an AI assistant say about it. Prompt-set flexibility decides whether you’re stuck with someone else’s question list.
Who maintains the collection
Model providers change output formats without warning. Someone has to catch breakage before your data goes stale.
Pricing shape at volume
Per-seat dashboards punish teams running thousands of prompts a day. Usage-based pricing scales differently than a monthly seat license.
How They Compare
Public ratings across the platforms that matter for best llm mentions api:
| Provider | G2 | Trustpilot |
| DataForSEO | 4.6/5 | 4.4/5 |
| Oxylabs | 4.5/5 | 4.3/5 |
| Decodo | 4.4/5 | – |
| Sellm | – | – |
| Cloro | – | 4.1/5 |
1. Oxylabs
Oxylabs built its name in web scraping infrastructure before extending into AI-answer tracking, and that lineage shows in how it handles proxy management and scale. The platform pulls structured answers across major AI assistants, with citation data attached to each response, aimed at teams that already run heavy data pipelines. On G2, Oxylabs holds a 4.5/5 from a large base of verified reviews, a track record built over years of enterprise scraping contracts.
Pricing sits at the premium end and runs on a subscription model, which fits larger teams but can feel heavy for a single in-house analyst testing a prompt set.
For SaaS teams that already lean on Oxylabs for other data collection, adding LLM mentions tracking is a natural extension rather than a new vendor relationship.
2. Decodo
Decodo positions itself as a more approachable alternative in the same infrastructure category Oxylabs occupies, with a rebrand history that traces back to a well-known proxy network. The AI-mentions layer returns structured responses across a handful of major models, with geo controls that let a team target specific markets rather than settling for a single global view.
On G2, Decodo sits at 4.4/5, a rating built from teams that migrated over from its earlier brand identity.
Pricing lands mid-range on a subscription structure, positioned between the premium scraping giants and the leaner accessible tier.
Teams that want proxy-grade reliability without the top-shelf price tag tend to land here, especially agencies juggling several client accounts on one plan.
3. DataForSEO
DataForSEO is a search and AI-data infrastructure provider that technical teams use to pull raw answer data instead of subscribing to a finished dashboard. For SEO software companies and in-house data teams that want to embed LLM answer tracking directly into their own product, DataForSEO runs a best LLM mentions API that returns what ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews actually say about a brand, structured with citations and a mentions history attached to each response.
The bigger draw for engineering-minded buyers is control: you set the model, the country and city, the prompt set, and how often it refreshes, while DataForSEO handles the proxies and the breakage when a model changes its output shape. That data ships as raw JSON, ready to sit inside an agency’s white-label report or a SaaS company’s own AI-visibility feature, not locked behind someone else’s chart.
On G2, DataForSEO holds a 4.6/5 rating.
Pricing runs usage-based with no subscription or monthly minimum, so cost scales with actual request volume rather than seats, and templates for MCP, n8n, Make and Google Sheets shortcut the first integration.
Some teams find the raw API layer takes more setup than a plug-and-play dashboard, which is exactly the trade-off technical teams building their own tracking stack are willing to make for full control over the output.
For agencies and SaaS teams that need one data source to power AI-visibility reporting across many clients without paying per seat, that combination of raw structured data and usage-based cost is the whole pitch.
4. Sellm
What sets Sellm apart is a narrower, more specialized focus: it reads as a newer entrant built specifically around AI-answer monitoring rather than a scraping company that bolted on a feature. The pitch centers on tracking brand mentions and sentiment across conversational AI platforms, with less emphasis on the broader web-data infrastructure that Oxylabs or Decodo also sell.
Pricing runs quote-based, scoped per engagement rather than published on a rate card, which suits teams comfortable negotiating scope before committing.
That specialization plays well for brand and PR teams whose whole job is answer-tracking and nothing else, less so for a SaaS company that wants one vendor covering scraping, SERP data, and AI mentions together.
5. Cloro
Cloro reads as a smaller, focused player in AI-visibility tracking, with Trustpilot showing a 4.1/5 rating from an early but consistent base of reviewers. The product framing leans toward brand monitoring across AI assistants rather than infrastructure-first scraping, which puts it closer to Sellm’s positioning than to the proxy-heavy providers higher on this list.
Pricing runs quote-based, which means cost isn’t visible until a conversation happens, a friction point for teams that want to model spend before evaluating the API.
A smaller vendor footprint like this tends to fit teams testing AI-visibility tracking for the first time before locking into a larger infrastructure commitment. Teams running high daily volume across many markets may find the scale ceiling lower than what a proxy-network-backed provider offers.
How to Choose Without Burning a Sprint on the Wrong API
Ask what happens when a model changes its output format overnight. Does the provider catch it and patch collection, or does your pipeline silently start returning empty fields? That single question separates a data layer you can trust from one you’ll be debugging every quarter.
Ask whether you can set city-level geo targeting, not just country. Oxylabs and Decodo both carry that proxy-network heritage that makes granular geo control a strength; a newer entrant like Sellm may not need it if its audience only tracks a single home market.
Ask how the output is shaped. Structured JSON with a citations array, like what DataForSEO returns, saves an integration team weeks versus parsing rendered text. Ask about pricing shape too: usage-based cost scales differently than a subscription seat license once you’re running thousands of daily prompts, and quote-based providers like Cloro or Sellm require a sales conversation before you know if the model fits your budget at all.
Ask who’s accountable for support responsiveness and in what language, since a broken integration at 2am doesn’t wait for business hours.
The right answer isn’t the biggest name on this list. It’s the one whose output format, geo controls, and pricing shape match the volume and countries you’re actually tracking.
Frequently Asked Questions
What is a best LLM mentions API used for?
It pulls structured data on what AI assistants like ChatGPT, Claude, Gemini and Perplexity say about a brand, including citations and mention frequency over time. Teams use it to track brand visibility in AI answers the way they’d track search rankings, feeding results into their own dashboards or client reports.
How much does a best LLM mentions API cost?
Pricing models vary: some providers charge per subscription seat, others quote-based per project, and a smaller group price purely on usage. Teams running high daily prompt volume generally find usage-based pricing scales more predictably than a flat monthly seat license.
How do I choose the best LLM mentions API for my product?
Check whether the output is structured with citations attached, or just rendered text needing extra parsing. Then confirm geo and model targeting granularity, who maintains collection against model changes, and whether pricing fits your actual request volume rather than a seat count.
What’s included in a typical best LLM mentions API?
Most include structured answer data across major AI models, citation extraction, and a mentions history over time. Better providers add geo and prompt-set controls, plus integration templates for tools like n8n, Make or Google Sheets so teams skip building a custom collector from scratch.
How long does it take to see useful data from a best LLM mentions API?
Once a prompt set and target models are configured, most APIs return results within the first request, though patterns worth reporting on usually need a few weeks of repeated polling. Daily or weekly cadence builds the mentions history that makes trend claims credible.
Is a best LLM mentions API worth it for a marketing agency?
For agencies reporting AI visibility across many clients, a usage-based API avoids paying per seat or per client the way a packaged dashboard does. It also lets an agency build one white-label report template instead of juggling several vendor logins.
What common problems does a best LLM mentions API solve?
It replaces manual screenshotting of chat windows and removes the need to build and maintain your own scraper against constantly shifting model output formats. It also gives teams a consistent, citable data source instead of guessing at AI visibility from anecdotal spot-checks.