7 Best LLMrefs Alternatives

LLMRefs is a useful tool for comparing large language models, but it has limits that make researchers and developers look elsewhere.

You might need deeper benchmark insights that go beyond static scores.
You might want transparent evaluation data or customizable test scenarios.
Or you might be looking for tools that track performance trends and model behavior over time.

If that’s the case, you’re in the right place. In this article, we’ll look at the best LLMRefs alternatives that give you more control, better visibility, and richer insights into how large language models actually perform.

TL;DR

Analyze: The best and most comprehensive alternative to LLMrefs for ai search visibility tracking

Most GEO tools tell you whether your brand appeared in a ChatGPT response. Then they stop. You get a visibility score, maybe a sentiment score, but no connection to what happened next. Did anyone click? Did they convert? Was it worth the effort? 

These tools treat a brand mention in Perplexity the same as a citation in Claude, ignoring that one might drive qualified traffic while the other sends nothing.

Analyze connects AI visibility to actual business outcomes. The platform tracks which answer engines send sessions to your site (Discover), which pages those visitors land on, what actions they take, and how much revenue they influence (Monitor). You see prompt-level performance across ChatGPT, Perplexity, Claude, Copilot, and Gemini, but unlike visibility-only tools, you also see conversion rates, assisted revenue, and ROI by referrer. 

Analyze helps you act on these insights to improve your AI traffic (Improve), all while keeping an eye on the entire market, tracking how your brand sentiment and positioning fluctuates over time (Govern).

Your team then stops guessing whether AI visibility matters and starts proving which engines deserve investment and which prompts drive pipeline.

Key Analyze features

• See actual AI referral traffic by engine and track trends that reveal where visibility grows and where it stalls.

• See the pages that receive that traffic with the originating model, the landing path, and the conversions those visits drive.

• Track prompt-level visibility and sentiment across major LLMs to understand how models talk about your brand and competitors.

• Audit model citations and sources to identify which domains shape answers and where your own coverage must improve.

• Surface opportunities and competitive gaps that prioritize actions by potential impact, not vanity metrics.

Here are in more details how Analyze works:

See actual traffic from AI engines, not just mentions

Analyze attributes every session from answer engines to its specific source—Perplexity, Claude, ChatGPT, Copilot, or Gemini. You see session volume by engine, trends over six months, and what percentage of your total traffic comes from AI referrers. When ChatGPT sends 248 sessions but Perplexity sends 142, you know exactly where to focus optimization work.

Know which pages convert AI traffic and optimize where revenue moves

Most tools stop at "your brand was mentioned." Analyze shows you the complete journey from AI answer to landing page to conversion, so you optimize pages that drive revenue instead of chasing visibility that goes nowhere.

The platform shows which landing pages receive AI referrals, which engine sent each session, and what conversion events those visits trigger. 

For instance, when your product comparison page gets 50 sessions from Perplexity and converts 12% to trials, while an old blog post gets 40 sessions from ChatGPT with zero conversions, you know exactly what to strengthen and what to deprioritize.

Track the exact prompts buyers use and see where you're winning or losing

Analyze monitors specific prompts across all major LLMs—"best Salesforce alternatives for medium businesses," "top customer service software for mid-sized companies in 2025," "marketing automation tools for e-commerce sites."

For each prompt, you see your brand's visibility percentage, position relative to competitors, and sentiment score.

You can also see which competitors appear alongside you, how your position changes daily, and whether sentiment is improving or declining.

Don’t know which prompts to track? No worries. Analyze has a prompt suggestion feature that suggests the actual bottom of the funnel prompts you should keep your eyes on.

Audit which sources models trust and build authority where it matters

Analyze reveals exactly which domains and URLs models cite when answering questions in your category. 

You can see, for instance, that Creatio gets mentioned because Salesforce.com's comparison pages rank consistently, or that IssueTrack appears because three specific review sites cite them repeatedly. 

Analyze shows usage count per source, which models reference each domain, and when those citations first appeared.

Citation visibility matters because it shows you where to invest. Instead of generic link building, you target the specific sources that shape AI answers in your category. You strengthen relationships with domains that models already trust, create content that fills gaps in their coverage, and track whether your citation frequency increases after each initiative.

Prioritize opportunities and close competitive gaps

Analyze surfaces opportunities based on omissions, weak coverage, rising prompts, and unfavorable sentiment, then pairs each with recommended actions that reflect likely impact and required effort. 

For instance, you can run a weekly triage that selects a small set of moves—reinforce a page that nearly wins an important prompt, publish a focused explainer to address a negative narrative, or execute a targeted citation plan for a stubborn head term.

AthenaHQ: best LLMrefs alternative for deeper audits and actionable insights

Key AthenaHQ standout features

• Multi-engine visibility tracking across leading LLM surfaces

• Action Center with step-by-step optimization suggestions

• Share-of-voice and competitive citation mapping over time

• Prompt intelligence with topic gaps and query coverage

• Brand sentiment and mention context inside AI answers

AthenaHQ shines when you need more than a scoreboard and want a plan you can run each week. It studies how AI tools cite your site and then turns those findings into clear tasks, so a marketer can move from “we see the problem” to “we fixed the problem” without jumping across many tools. Teams that manage many products or regions get value because the dashboards group signals by topic, market, and brand, which helps leaders see progress without digging through raw logs.

The platform also stands out because it links measurement with change. You can see where you win or lose share inside AI answers and then push targeted fixes such as prompt framing, content rewrites, or richer entity markup. LLMrefs does tracking well for many users, yet AthenaHQ leans harder into prescriptive next steps and portfolio-level views, which helps large teams ship improvements on a steady cadence.

That power brings trade-offs that matter in day-to-day use. The product asks for setup choices, data scoping, and process changes, which creates a learning curve for smaller teams that just want quick checks. Some users will feel the platform sits closer to enterprise analytics than to a light monitor, which can slow first wins if the team lacks time or resourcing.

Cost also enters the picture once you scale projects and engines. Plans that support many prompts, markets, and models can climb, which pushes budget owners to guard usage and enforce workflow rules. If you mainly need simple tracking and a lighter bill, LLMrefs can fit that need better, while AthenaHQ fits best when the team needs deeper audits and guided action every month.

AthenaHQ vs LLMrefs: practical comparison

Best-fit use cases for AthenaHQ

• You manage many markets and need topic-level SOV targets that roll up for leadership.

• You want guided fixes that tie audits to weekly tasks and owners.

• You need sentiment checks to see how models frame your brand and products.

• You run a program that treats GEO as an ongoing practice, not a side report.

When LLMrefs may be the smarter pick

• You want fast visibility checks without process change.

• You need simple competitor cites and trend lines at a lower cost.

• You run a small program and prefer a light footprint and quick setup.

Bottom line: choose AthenaHQ when your team needs deeper audits that produce clear next steps and sustained gains, and choose LLMrefs when you want fast tracking with a lighter lift and a simpler bill.

Otterly AI: best LLMrefs alternative for automated brand mention tracking

Key Otterly AI standout features

• AI visibility and brand mention tracking across leading LLMs

• Sentiment and brand context analysis in AI-generated responses

• Competitive benchmarking and share-of-voice dashboards

• Alerts and automated weekly reporting on brand visibility changes

• Prompt generation and prompt-level tracking for branded queries

Otterly AI focuses on giving marketers fast, continuous awareness of how their brand shows up across AI platforms. It scans responses from models like ChatGPT, Perplexity, Gemini, and Copilot, then flags when your domain or brand name appears. This makes it ideal for teams that want to know where and how often their brand is mentioned, without building a heavy GEO or technical analytics process. The platform’s simple dashboards and automated alerts give you real-time awareness without extra setup work or steep learning requirements.

Where Otterly stands out is in its ability to translate AI visibility into brand perception insights. It doesn’t just show citations—it evaluates how your brand is framed, whether neutrally, positively, or negatively. This helps marketing, communications, and PR teams see beyond raw counts to understand tone and context. Compared with LLMrefs, which focuses primarily on citation monitoring and share of voice, Otterly adds a lightweight layer of brand sentiment and competitive framing. That blend of simplicity and contextual awareness makes it particularly effective for communications and brand reputation teams who need quick answers, not long audits.

Its simplicity, however, comes with limits. Otterly’s data depth is narrower than enterprise-level GEO platforms. The tool does a good job identifying when your brand is mentioned, but it doesn’t go far into the “why” or “how to fix it.” Marketers seeking full optimization guidance—such as specific schema changes, on-page adjustments, or AI model engagement strategies—will find that Otterly stops short of that level of analysis. It’s built for monitoring and awareness, not end-to-end optimization workflows.

Dataset size also matters. Because Otterly tracks across many AI surfaces, the data on small or niche topics can be patchy or slower to update. Teams operating in specialized industries or emerging topics may find fewer data points compared to LLMrefs, which maintains broader baseline coverage across major AI queries. The dashboard itself, while clean, can still feel dense to newcomers who expect a linear “on/off” experience.

Lastly, Otterly doesn’t connect brand mentions to conversion data or customer outcomes. It’s a visibility tool, not a performance attribution system. If you need to tie AI visibility to leads or pipeline, you’ll likely need integrations or external analytics. These trade-offs don’t diminish its core value but define its focus: it’s built for visibility and reputation, not deep technical optimization.

Otterly AI vs LLMrefs: practical comparison

Best-fit use cases for Otterly AI

• You manage PR or brand reputation and need early detection of AI mentions.

• You want a lightweight monitoring layer without complex data setups.

• You track multiple brands or domains and need weekly visibility summaries.

• You want sentiment context to guide brand messaging in AI search results.

When LLMrefs may be the smarter pick

• You need deeper GEO data for SEO or AI optimization.

• You work in a niche field where AI mentions are less frequent.

• You want more control over keyword datasets and export workflows.

• You plan to connect visibility metrics with marketing performance reports.

Bottom line: Otterly AI fits best when your goal is awareness, not analytics. It’s the right choice for teams who value quick, automated alerts and sentiment insights over technical audits. If your focus is visibility management and brand monitoring across AI engines, Otterly delivers strong coverage with minimal setup. For teams needing deeper data and optimization guidance, LLMrefs remains the stronger analytical companion.

Peec AI: best LLMrefs alternative for balance of usability and depth

Key Peec AI standout features

• Multi-model visibility tracking across Claude, ChatGPT, Perplexity, Gemini, and AI Overviews

• Prompt-level insights showing which queries trigger your brand mentions

• Citation and source mapping to identify where AI models pull content

• Competitor benchmarking through share-of-voice and visibility trends

• Daily data refreshes with exportable, presentation-ready dashboards

Peec AI sits in a rare middle ground between depth and simplicity. It gives teams more insight than a lightweight mention tracker but without the friction or learning curve of enterprise GEO suites. The platform helps you see not only if your brand appears in AI-generated responses, but why it appears—what prompts triggered the mention, and what source domains influenced that visibility. This makes it easier to reverse-engineer how LLMs interpret your content and why certain competitors get cited more often.

Its appeal lies in how it balances power with clarity. Peec’s interface is clean, its dashboards are structured for comprehension, and its metrics are simple enough for quick interpretation without losing depth. Small teams and agencies can grasp performance patterns fast—no data engineering or onboarding sessions required. Reviewers consistently praise its intuitive UI and transparent scoring system, which make AI visibility data easy to digest and act on. Compared with LLMrefs, which can feel data-dense or overengineered for smaller use cases, Peec focuses on usability and immediate clarity.

Pricing is also one of Peec AI’s strongest draws. Its Starter plan includes up to 25 tracked prompts and roughly 2,000 AI answer analyses each month, scaling up through Pro and Enterprise tiers. This approach gives users flexibility to expand coverage only when needed. For small to mid-sized agencies managing multiple clients or content portfolios, that scalability keeps cost-to-insight ratios favorable. It’s a system designed to grow with your scope rather than overwhelm you from the start.

However, Peec AI’s simplicity creates boundaries. While its data visualizations are rich, the platform stops short of prescribing what to do next. It tells you where you’re visible, not how to change that visibility. For teams seeking explicit optimization recommendations—such as prompt rewrites, schema fixes, or content adjustments—Peec provides the “what” but not always the “how.” That limitation makes it best suited for teams confident in interpreting data rather than relying on software to generate next steps.

Scalability is another trade-off worth considering. As you track more prompts, models, or regions, your data volume may outgrow your plan, requiring an upgrade. This can push costs up quickly for expanding teams. Peec’s integrations, such as API exports and enterprise modules, are also less developed than larger GEO platforms. In niche industries, its dataset may lag behind or feel incomplete due to lower citation frequency. These aren’t dealbreakers but important context for buyers expecting fully mature coverage.

Peec AI vs LLMrefs: practical comparison

Best-fit use cases for Peec AI

• You manage multiple brands and want quick, visualized AI visibility data.

• You need prompt-level context without enterprise pricing.

• You want clean dashboards that clients or stakeholders can read instantly.

• You’re building GEO capability but don’t need prescriptive optimization yet.

When LLMrefs may be the smarter pick

• You want built-in recommendations or deeper optimization analytics.

• You need API-level integrations and workflow automation.

• You monitor highly specialized or low-volume industries.

• You prefer broader data coverage with detailed export options.

Bottom line: Peec AI delivers balanced intelligence—enough data to drive strategy, but simple enough to use daily. It’s ideal for agencies and lean marketing teams that want GEO visibility without technical friction or enterprise overhead. If you’re ready for a tool that gives clarity without complexity, Peec AI fits the gap between quick tracking and deep analysis.

Rankability AI Analyzer: best LLMrefs alternative for all-in-one SEO + GEO performance

Key Rankability AI Analyzer standout features

• Brand and prompt visibility tracking across ChatGPT, Gemini, Claude, and Perplexity

• Benchmarking and trend tracking for competitive AI share of voice

• Built-in audits and recommendations to improve AI citations

• Integration with Rankability’s SEO, keyword, and content optimization suite

• Unified dashboards that merge SEO and AI visibility metrics

Rankability’s AI Analyzer is designed for teams who want to move from monitoring to fixing without leaving their SEO ecosystem. It connects AI visibility data—how often your brand appears in AI responses—to the same tools you already use for keyword research, briefs, and content optimization. Instead of running visibility checks in one platform and implementing fixes in another, you work inside a single workflow. This makes Rankability particularly appealing for agencies or marketing teams that need both visibility tracking and actionable SEO workflows in one place.

The biggest value lies in how Rankability blends GEO intelligence into established SEO habits. It brings AI visibility signals into keyword dashboards, performance reports, and optimization checklists. That means the same data guiding your Google strategy can now inform your presence in ChatGPT, Perplexity, and Gemini. The result is less context switching and faster implementation—teams can audit, adjust, and measure in one cycle. For agencies managing multiple clients, this unified structure simplifies operations, reporting, and onboarding while maintaining consistency across projects.

Rankability’s pricing also supports that positioning. It’s not marketed as a separate GEO product but as part of the broader Rankability suite, making it an accessible add-on rather than a major new expense. This makes it ideal for teams that already rely on Rankability for SEO and want to extend their visibility coverage into AI surfaces without adding another vendor or dashboard.

However, integration has its trade-offs. Because the AI Analyzer is tied to Rankability’s ecosystem, teams must use the platform’s SEO modules to unlock full functionality. For users looking for a standalone AI visibility tool, this bundled approach may feel restrictive. It’s an ecosystem play, not a plug-and-play solution. That dependency also means your visibility data and workflows are linked—if you move away from Rankability later, you’ll lose the historical continuity tied to its integrated reports.

Another limitation is maturity. The AI Analyzer module remains partly in development, and several advanced features—like deeper model behavior mapping and refresh rate controls—are still evolving. This makes it less predictable for teams that need immediate, enterprise-level GEO precision. Rankability is catching up quickly, but its early-stage roadmap means some performance gaps and stability questions will remain until the feature matures.

Lastly, balancing SEO and GEO can stretch focus. While Rankability’s integration is convenient, it can’t yet match the analytical depth of dedicated GEO tools like AthenaHQ or Probe Analytics when it comes to model-specific behavior or multi-language coverage. As data scales across more clients and prompts, cost and performance may become concerns, since usage is tied to overall platform activity rather than a standalone quota.

Rankability AI Analyzer vs LLMrefs: practical comparison

Best-fit use cases for Rankability AI Analyzer

• You already use Rankability for SEO and want GEO inside the same platform.

• You manage multiple brands or clients and need unified SEO + AI visibility reporting.

• You prefer working within one workflow to track, optimize, and measure performance.

• You want light GEO functionality built into your broader optimization ecosystem.

When LLMrefs may be the smarter pick

• You need a standalone GEO tool without bundling requirements.

• You want established tracking coverage and historical data depth.

• You rely heavily on exportable data and cross-platform integration.

• You’re not using Rankability for SEO and don’t want to migrate workflows.

Bottom line: Rankability AI Analyzer is ideal for marketers who value cohesion over specialization. It’s built for teams that want to manage SEO and AI visibility as one continuous process—auditing, optimizing, and measuring in the same environment. For teams already embedded in Rankability’s suite, it transforms GEO from an external insight into a native, fix-ready feature.

Scrunch AI: best LLMrefs alternative for proactive optimization workflows

Key Scrunch AI standout features

• Visibility and brand-mention tracking across AI platforms like ChatGPT, Perplexity, and Gemini

• AI readability audits that test how well your content can be understood and interpreted by models

• Hallucination and misinformation detection to catch inaccurate AI outputs referencing your brand

• Content gap and structural recommendations to improve AI interpretability

• Persona-based visibility views and competitive benchmarking

• Agency-grade tools for managing multiple clients or brands

• Multi-day refresh cycles for up-to-date insights

Scrunch AI represents the new wave of GEO tools that don’t stop at visibility tracking—they go further into optimization. It’s designed for teams that not only want to know how often they’re cited by AI engines, but also why their content is or isn’t being surfaced. The platform’s audits look under the hood of your pages, analyzing how models read your structure, entities, and markup. Then it closes the loop by recommending changes to improve comprehension, positioning, and factual reliability.

That dual focus—visibility and optimization—makes Scrunch AI one of the more ambitious tools in the space. Rather than reporting mentions, it acts like a “coach” for AI-readable content. If your brand is cited inaccurately or not at all, Scrunch identifies what’s missing and how to fix it. This makes it particularly valuable for brands that depend on topical authority or complex technical accuracy, where even small hallucinations can affect perception.

Scrunch also pays close attention to brand clarity and reputation control inside AI results. Many GEO tools limit themselves to citation frequency, but Scrunch looks at tone and accuracy: whether the model is presenting your brand positively, neutrally, or with factual drift. Combined with persona-based segmentation, teams can see how different audience types encounter their brand inside AI conversations. This depth makes it useful not just for SEO or content marketers but also for communications and PR teams managing AI-era reputation.

For agencies, Scrunch offers strong workflow infrastructure. Its multi-brand dashboards, prompt upload systems, and partner program are built for scale. Reporting cycles are frequent, and Scrunch claims that most visibility data refreshes every few days, helping agencies maintain timely updates for clients. This makes it a natural fit for consulting or enterprise service environments where accountability and precision are critical.

Still, Scrunch AI is not a lightweight product. Its depth and modular complexity can feel heavy for smaller teams or single-brand operations. While enterprise users benefit from its integrated ecosystem, startups or small teams might find its learning curve and setup demands high. Several users describe it as “a platform you grow into”—powerful, but initially resource-intensive.

Pricing reflects that enterprise tilt. Entry plans start around $300/month, positioning Scrunch above simpler monitoring tools. For teams exploring GEO for the first time, that can be a barrier. But for those already investing in structured content and AI visibility as part of their digital strategy, the return on precision and control may justify the cost.

Because the product recently launched out of beta, some modules—such as hallucination detection and entity gap scoring—are still evolving. While performance continues to improve, early adopters should expect occasional inconsistencies or limited depth in niche domains. Scrunch is rapidly iterating, but its cutting-edge features are still stabilizing.

Scrunch AI vs LLMrefs: practical comparison

Best-fit use cases for Scrunch AI

• You want to understand not just if, but how AI models interpret your brand.

• You manage high-stakes or technical content where misinformation risks matter.

• You run multi-brand or agency workflows and need structured, repeatable reporting.

• You want actionable recommendations that improve AI-readability and visibility.

When LLMrefs may be the smarter pick

• You only need tracking, not in-depth content audits.

• You prefer fast setup and minimal data handling.

• You’re exploring GEO on a limited budget or with a small team.

• You don’t need sentiment or misinformation insights yet.

Bottom line: Scrunch AI is best for organizations that treat AI visibility as part of a broader content optimization strategy. It goes beyond seeing where you appear—it helps you shape how AI understands your brand. For small teams, that might be more horsepower than needed, but for enterprises and agencies, it’s a proactive system built to future-proof visibility and reputation across the AI landscape.

SE Ranking: best LLMrefs alternative for budget-friendly GEO visibility

Key SE Ranking standout features

• AI Overviews Tracker monitors how your brand appears in Google’s AI-generated results

• Keyword-level tracking shows which queries trigger AI Overviews and which domains are cited

• Competitor benchmarking reveals who else appears in AI snippets for shared topics

• Daily tracking cadence ensures current data and visibility trend accuracy

• Integration with SE Ranking’s Rank Tracker and SEO suite for unified insights

SE Ranking’s AI Visibility Tracker provides a practical, accessible way to explore generative search without leaving your SEO environment. It tracks when your brand is cited or mentioned inside Google’s AI Overviews and related AI-driven SERP features, tying that data back to keyword performance and traditional rankings. For smaller teams or those testing GEO concepts, this balance of simplicity, affordability, and continuity makes SE Ranking an approachable entry point.

The integration into SE Ranking’s larger suite matters more than it first appears. Instead of operating as a standalone GEO product, AI visibility lives inside a familiar dashboard already used for keyword monitoring, site audits, and competitor tracking. This continuity means teams don’t need to learn new workflows or manage extra logins just to track AI visibility. You can see which keywords now generate AI results, check whether your domain appears, and compare your AI presence against competitors—all within the same analytics view. That lowers both cost and cognitive load.

Because SE Ranking updates daily, data freshness is another practical win. Many GEO-dedicated tools refresh weekly or less frequently due to heavier data collection costs. For marketing teams that rely on agile reporting cycles or weekly updates, SE Ranking’s pace feels more natural. It also fits smaller programs that prioritize consistent visibility checks over deep audits.

At its best, SE Ranking offers excellent value-to-coverage ratio. For the price of a standard SEO tool, users gain partial GEO functionality—something that makes it one of the most cost-effective ways to enter AI visibility tracking. Its integrated competitor benchmarking allows you to see which brands dominate AI Overviews for specific topics, enabling faster content adjustments without complex GEO workflows.

Still, the simplicity that makes SE Ranking attractive also defines its limits. Its visibility tracking focuses mainly on Google AI Overviews and “AI Mode” (Google’s assistant interface). It does not yet offer comprehensive coverage across ChatGPT, Perplexity, or Gemini in the way more advanced GEO platforms do. For most users, that means SE Ranking delivers insight into Google’s AI layer—but not into how other LLMs cite their content.

Another limitation is depth of analysis. SE Ranking reports whether your domain appears but stops short of interpreting why. It lacks the prescriptive optimization features (e.g., prompt analysis, schema fixes, entity enhancement) found in higher-tier GEO platforms. For many small teams, this simplicity is fine; for advanced users, it can feel incomplete.

As SE Ranking expands its AI feature set—currently listed in its “What’s New” product updates—expect improvements in coverage and usability. But for now, enterprise features like multi-brand collaboration, advanced APIs, and deep attribution remain limited. SE Ranking’s AI visibility remains best suited for individuals or teams that value integration and affordability over exhaustive data coverage.

SE Ranking vs LLMrefs: practical comparison

Best-fit use cases for SE Ranking

• You want to test GEO visibility without committing to a specialized tool.

• You already use SE Ranking for SEO and want a built-in AI layer.

• You prefer daily updates and competitor snapshots rather than deep audits.

• You run a lean marketing team and need a clear, low-friction setup.

When LLMrefs may be the smarter pick

• You need multi-engine coverage across Gemini, ChatGPT, and Perplexity.

• You require deeper optimization guidance and prompt-level recommendations.

• You manage enterprise or multi-region campaigns with complex workflows.

Bottom line: SE Ranking is the budget-friendly on-ramp to AI visibility tracking. It blends SEO familiarity with GEO essentials, giving marketers a fast, affordable way to monitor their AI presence. While it can’t yet match the analytical depth or multi-engine reach of LLMrefs, its ease of use and daily data cadence make it a strong choice for small teams beginning their GEO journey.