Quick Verdict
You're tracking how often ChatGPT or Perplexity mentions your brand, but you can't decide between LLMrefs and Radix. Both launched in 2025 to solve the same problem — measuring your visibility in AI search results — but they take fundamentally different approaches.
LLMrefs focuses on broad keyword tracking across multiple AI platforms using automated prompts from a 4.5M ChatGPT dataset. Radix centers on brand recommendation counting with deeper analytics integration. The choice comes down to whether you want comprehensive coverage or focused brand tracking.
Score comparison
Score Comparison
| Dimension | LLMrefs | Radix |
|---|---|---|
| Feature Depth | 20.0 | 29.0 |
| Ease of Use | 35.0 | 50.0 |
| Data Quality | 32.0 | 42.0 |
| Value for Money | 75.0 | 55.0 |
| Integration | 0.0 | 0.0 |
| Market Traction | 9.0 | 6.0 |
Feature comparison
| Feature | LLMrefs | Radix |
|---|---|---|
| Multi-Platform Citation Tracking | ✓ | — |
| 4.5M+ ChatGPT Dataset | ✓ | — |
| AI Crawlability Checker | ✓ | — |
| LLMs.txt Generator | ✓ | — |
| Reddit Threads Finder | ✓ | — |
| A/B Content Tester | ✓ | — |
| AI Tools Directory | ✓ | — |
| Prompt Testing | — | ✓ |
| Citation Tracking | — | ✓ |
| Competitor Benchmarking | — | ✓ |
| AI Visitor Analytics | — | ✓ |
Pricing comparison
| Plan | LLMrefs | Radix |
|---|---|---|
| Free | $0 | Contact for pricing |
| Pro | Contact for pricing | — |
Feature Comparison
LLMrefs covers more ground with real UI crawling across ChatGPT, Google AI Overviews, Perplexity, Gemini, and Claude. Their keyword-based approach automatically generates relevant prompts instead of making you write them manually. You also get tools like an llms.txt generator and AI crawl checker that most competitors charge extra for. Radix takes a narrower but deeper approach. They focus specifically on counting brand recommendations rather than general keyword visibility. Their citation tracking shows exactly where your mentions appear, and the competitor benchmarking lets you compare your AI visibility against specific rivals. The standout feature is AI visitor analytics that connects to your existing Google Analytics or similar tools. Both use statistical significance methods with repeated prompt testing, but LLMrefs runs more platforms while Radix digs deeper into the data you actually care about as a brand manager.
Pricing Comparison
LLMrefs wins the pricing battle easily — they offer a free tier that actually includes useful features. Most AI visibility tools gate everything behind paid plans, but you can start tracking keywords immediately without a credit card. Radix hasn't published pricing yet, which usually means they're targeting enterprise budgets rather than individual marketers or small agencies. Based on their analytics integration focus, expect them to price closer to traditional enterprise SEO tools than the accessible rates LLMrefs offers.
Best For
Choose LLMrefs when you're starting AI visibility tracking or need broad coverage across platforms. The free tier makes it perfect for agencies testing AI tracking with clients before committing budget. The automated prompt generation from their massive dataset saves hours of manual work, and covering five major AI platforms gives you the complete picture of your AI presence. Choose Radix when you're specifically focused on brand mentions and have existing analytics infrastructure to connect with. If you're already spending serious money on competitor intelligence and want AI visibility integrated into those workflows, Radix's deeper brand tracking approach makes more sense than LLMrefs' broader keyword focus.
The Verdict
LLMrefs is the better starting point for most people. The free tier, multiple platform coverage, and automated prompt generation solve the biggest barriers to AI visibility tracking. Radix might offer more sophisticated brand analytics, but without published pricing and with fewer platforms covered, it's harder to justify unless you have specific enterprise requirements. Start with LLMrefs' free tier and upgrade later if you need deeper brand-focused analytics.