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Ahrefs Brand Radar AI Visibility Platform Fit Review for Tracking Recommendation Share

Ahrefs Brand Radar is a mixed fit for tracking AI recommendation share.

Research: 2026-09-197 usable platform responsesRead the methodology ↗

Answer Capsule

Ahrefs Brand Radar is a mixed fit for tracking AI recommendation share. Three of seven platforms named it during the ranking stage (43% of included platform responses), at an average listed rank of 4.3 and a best rank of 4. Its strongest case is platform-by-platform AI visibility, AI Share of Voice, competitor benchmarking, and Custom Prompts for exact buyer questions inside an existing Ahrefs workflow. The main limitation is that public documentation emphasizes mentions, citations, and share of voice rather than structured recommendation position or ranked recommendation share, so buyers whose core KPI is recommendation rank must verify that output before purchase.

Research Snapshot

FieldFinding
Platform mentions in ranking stage3 of 7 platforms (deepseek, openai, perplexity)
Share of included platform responses42.9%
Average listed rank4.33
Best listed rank4
Relevant product/model/planAhrefs Brand Radar AI, including All Platforms access and Custom Prompts
Overall use-case fitMixed
Research date2026-09-19

Why Ahrefs Brand Radar Qualified for This Study

Questions This Section Answers

  • Is Ahrefs Brand Radar a good choice for AI Visibility Platforms for Tracking Recommendation Share?
  • Why did only three of seven AI platforms name Ahrefs Brand Radar for tracking recommendation share?

Ahrefs Brand Radar qualified because it is a documented AI visibility product that measures brand presence across major AI answer engines, not a generic SEO tool with no AI feature set. Ahrefs states that Brand Radar supports AI visibility measurement, Custom Prompts, major AI platforms, competitor benchmarking, and published Brand Radar pricing [1]. Ahrefs also documents Brand Radar coverage, AI Share of Voice benchmarking, Custom Prompt functionality, platform access, and pricing tiers [2].

It cleared the study's minimum-mention threshold of two platforms, appearing in three of seven platform responses at an average listed rank of 4.33. That is a mid-pack showing: it was named, but no platform placed it first or second. The ranking-stage identity was an exact-name fallback, and official-site retrieval reportedly failed during research, so the reported domain remains unverified per the audit context [3].

The product's relevance to this use case rests on three documented capabilities: platform-by-platform AI visibility, competitor comparison, and Custom Prompts for exact buyer questions. Whether those capabilities extend to recommendation-level position is the open question that shapes the rest of this review.

The Product, Model, Plan, or Service Most Relevant to AI Visibility Platforms for Tracking Recommendation Share

Questions This Section Answers

  • Which Ahrefs Brand Radar plan should a buyer choose if they need exact buyer-question tracking across multiple AI platforms?
  • Does Ahrefs Brand Radar All Platforms include Custom Prompts, and how many checks does it include?

The relevant configuration is Ahrefs Brand Radar AI, including All Platforms access and Custom Prompts. Ahrefs documents Brand Radar coverage, AI Share of Voice benchmarking, Custom Prompt functionality, platform access, and pricing tiers [4]. The All Platforms tier includes full access to 243M+ organic prompts for all platforms, plus custom prompts, with 2,500 checks per month for custom prompts [5].

Custom prompts are documented for ChatGPT, Gemini, Perplexity, and Copilot [6]. Ahrefs' product page also identifies Claude as available with All Platforms access, though platform availability and naming should be confirmed at purchase [7]. Independent coverage describes the tracked engine set as Google AI Overviews and Google AI Mode, alongside ChatGPT, Perplexity, Gemini, and Microsoft Copilot, as of July 2026 [8].

Ahrefs defines Mentions, Citations, Impressions, and AI Share of Voice as Brand Radar metrics and explains their measurement [9]. Ahrefs also provides an AI Responses API endpoint and documents API-unit implications for requests containing Ahrefs prompt data [10]. That API is the closest documented path to programmatic access, but the public materials do not confirm that it returns recommendation position.

What the AI Platforms Agreed About

Questions This Section Answers

  • What do AI platforms agree Ahrefs Brand Radar does well for tracking AI recommendation share?
  • Is Ahrefs Brand Radar strong for competitor comparison and historical trends in AI visibility?

The platforms broadly agreed that Ahrefs Brand Radar is a legitimate AI visibility and share-of-voice product with platform-by-platform reporting, competitor comparison, and historical trend tracking. Agreement was strong on capability, not on fit.

On platform-by-platform results, Brand Radar provides separate visibility metrics and Share of Voice data for each tracked AI engine, with the ability to compare across platforms in a unified dashboard [11]. Ahrefs states that Brand Radar supports benchmarking AI Share of Voice against competitors and identifying visibility gaps, cited pages, and cited domains [12].

On historical trends, Brand Radar includes week-over-week tracking of custom prompts and month-over-month monitoring of the default prompt database, plus competitor Share of Voice comparisons [13]. Ahrefs markets Brand Radar with trend and visibility tracking over time, which supports the historical-trend requirement at the category level [15].

On prompt scale, Ahrefs operates a database of 250M–356M search-backed prompts per month, described as a significant differentiator [16]. Ahrefs' own product page cites 250M+ search-backed prompts [16], while other Ahrefs pages cite different totals — a conflict addressed below.

On citation source tracking, Brand Radar shows which URL the AI model pulled the mention from, including own site, Reddit, review roundups, or competitor blogs [20]. One independent review calls the citation view the most underrated feature [20].

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

  • Does Ahrefs Brand Radar track recommendation position, or only brand mentions and AI Share of Voice?
  • Which AI platforms does Ahrefs Brand Radar not cover, and does that matter for recommendation-share tracking?

The central disagreement is whether Brand Radar delivers recommendation-level data at all. This is the buyer's core criterion, and the platforms did not resolve it.

Ahrefs defines AI Share of Voice as the percentage of AI responses in a topic set that mention or cite a brand versus competitors, and the public methodology does not clearly document a separate recommendation-share metric or recommendation-position field [22]. Independent review coverage states that Brand Radar counts a mention when the tracked brand appears at least once in an AI-generated response [23], and that mention count does not indicate whether the brand was the first recommendation, an alternative, a negative example, or a minor reference [24]. One review notes that Brand Radar emphasizes mentions, citations, and Share of Voice rather than explicit per-response recommendation ranking [25].

Platform coverage is a second area of uncertainty. Brand Radar tracks six AI engines: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Microsoft Copilot, and Gemini, with no coverage of Claude, Meta AI, Grok, or other emerging models [26]. Independent coverage states that missing major platforms like Claude, Meta AI, and Grok undermines a holistic AI visibility strategy [28]. One platform reported that Brand Radar covers Grok [29], which conflicts with the six-engine list; buyers should confirm the current engine list directly.

Accuracy is a third uncertainty. Independent reviews document accuracy gaps, particularly in ChatGPT and Perplexity tracking, and inconsistencies large enough to caution against building strategies on Brand Radar data alone [30]. One review states that inconsistencies in ChatGPT and Perplexity tracking are too large to ignore for strategy-level decisions [30]. No supplied source quantifies error rates.

Pricing conflicts are a fourth. Ahrefs' product page presents All Platforms at $699 per month and the Help Center presents the same price, while single-index and Custom Prompt packaging may vary by account or purchase route [32]. Ahrefs' FAQ says Brand Radar is sold standalone from $50/month with custom prompt functionality and no Ahrefs subscription required, conflicting with other Ahrefs pages [34]. Independent coverage reports a required Ahrefs base subscription and add-on pricing, highlighting the public pricing inconsistency [35].

Prompt-index size is a fifth conflict. Ahrefs product pages reference more than 455 million prompts, the Help Center references more than 405 million, and the pricing page references more than 475 million organic prompts, so the prompt-index size should be treated as approximate [32].

Use-Case-Specific Features and Capabilities

Questions This Section Answers

  • Does Ahrefs Brand Radar Custom Prompts let a buyer track exact high-intent buyer questions across AI platforms?
  • Can a buyer export Ahrefs Brand Radar AI answer data to identify recommendation order and competing brands?

Custom Prompts is the feature most directly aligned with this use case. Custom Prompts allows buyers to track exact questions, including prompts that may not exist in Ahrefs' pre-collected prompt database, with scheduled refreshes and platform selection [37]. You supply the prompts you care about, and Brand Radar re-runs them so you can see week-over-week movement [39].

Platform coverage is documented as AI Overviews, Google AI Mode, ChatGPT, Perplexity, Gemini, and Copilot in Ahrefs' AI indexes, with Claude identified on the product page as available with All Platforms access [37]. Custom prompts are documented for ChatGPT, Gemini, Perplexity, and Copilot [40].

Competitor comparison is documented: Ahrefs states that Brand Radar supports benchmarking AI Share of Voice against competitors and identifying visibility gaps, cited pages, and cited domains [38]. Ahrefs visibility in Brand Radar measures how often a brand appears or is cited in AI-generated answers, the estimated demand, and its share of exposure versus competitors [41].

Citation source tracking shows which URL the model pulled the mention from [42]. Ahrefs documents API access for AI responses and states that AI visibility metrics can be pulled through its API, although API-unit consumption applies to requests that include Ahrefs prompt data [44].

The capability gap is recommendation position. The public methodology does not clearly document a separate recommendation-share metric or recommendation-position field [44], and mention count does not indicate whether the brand was the first recommendation, an alternative, a negative example, or a minor reference [46]. Underlying AI answers must be manually reviewed to determine recommendation position [46].

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

  • How much does Ahrefs Brand Radar cost per month, and is a base Ahrefs subscription required?
  • What happens when a buyer exceeds the 2,500 Custom Prompt checks included with Ahrefs Brand Radar All Platforms?

Public pricing is documented but internally inconsistent, and buyers should treat any single figure as unconfirmed until Ahrefs quotes their exact configuration.

Documented costs include a single Brand Radar platform/index at $199 per month, Brand Radar All Platforms at $699 per month documented as including 2,500 Custom Prompt checks per month, and Custom Prompts packages at $50 per month for 2,500 checks with $0.020 per-check overage, $100 per month for 7,000 checks with $0.015 per-check overage, and $250 per month for 25,000 checks with $0.010 per-check overage [47]. Ahrefs publicly lists Brand Radar AI pricing and Custom Prompt package pricing, including included checks and overage rates [47].

Independent reviews calculate all-in costs higher. Full AI coverage realistically costs around $828 per month: $699 for all indexes plus $129 for the cheapest qualifying base plan [48]. Brand Radar requires an active Ahrefs base subscription, which starts at $129/month for the Lite plan [49]. Ahrefs Brand Radar pricing is described as an add-on to a base Ahrefs plan starting at $129/mo, costing an additional $199/mo per AI index or $699/mo for the 6-platform bundle [50]. One review states that full Brand Radar coverage at $828/mo is more than Peec AI's top agency tier ($795/mo) and sits well above the reported ~$499/mo entry for Profound [51]. Another states that the industry average cost for a dedicated AI visibility tracking tool is $337/month, and that Ahrefs Brand Radar at full deployment costs roughly 2.5 times the industry average [52].

Additional fees documented by independent sources include per-seat add-ons for users beyond plan limits ($40–$100/mo each), a Report Builder upgrade at $99/mo for 50 reports, Project Boost Pro at $20/mo per project, Project Boost Max at $200/mo per project, custom prompt packages beyond bundle allowance ($50–$250/mo), and overage charges for prompt checks exceeding the monthly limit at $0.020 per check [49]. YouTube and TikTok tracking are currently free during beta but will cost $199/mo after beta ends [53].

Contract terms are not clearly established. The public sources checked do not clearly establish cancellation, refund, annual-commitment, or month-to-month terms for Brand Radar specifically, and Ahrefs Enterprise pricing is shown as requiring an annual commitment, though that does not establish that the same requirement applies to Brand Radar purchases [54]. Independent sources describe annual billing available at a discount versus monthly, month-to-month billing available, and no published early termination penalties found in search results [49]. Ahrefs blog material says Brand Radar can only be purchased monthly, except enterprise users can buy annually [55].

Best Suited For

Questions This Section Answers

  • Who gets the most value from Ahrefs Brand Radar for AI visibility and share-of-voice tracking?
  • Is Ahrefs Brand Radar worth it for a company already paying for an Ahrefs SEO subscription?

Ahrefs Brand Radar is best suited to companies already inside the Ahrefs ecosystem that want AI visibility layered onto an existing SEO workflow, and to teams whose primary metric is mention share rather than recommendation rank.

Documented best-fit profiles include companies already using Ahrefs that want AI visibility added to an existing SEO workflow, teams measuring brand mentions, citations, estimated impressions, and competitor AI Share of Voice across major AI search platforms, buyers needing exact buyer-question monitoring through Custom Prompts, and agencies or enterprises that also value Ahrefs SEO, web visibility, YouTube, Reddit, and related discovery data [56]. Independent sources add existing Ahrefs customers already paying for an SEO suite who want to layer AI visibility monitoring into established workflows, enterprise brands with large budgets needing directional AI visibility research at scale, companies prioritizing citation source tracking, and brands focused on Google AI Overviews and search-aligned prompts [58].

The common thread is that the value is highest when the buyer already pays for Ahrefs and treats Brand Radar as an incremental add-on rather than a standalone recommendation-share platform.

Probably Not Best Suited For

Questions This Section Answers

  • Who should not choose Ahrefs Brand Radar for tracking AI recommendation share?
  • Is Ahrefs Brand Radar a poor fit for agencies that need white-label client reporting?

Buyers whose primary KPI is recommendation position or ranked recommendation share should not treat Ahrefs Brand Radar as a proven solution without verification. The public documentation emphasizes mentions, citations, impressions, and AI Share of Voice rather than recommendation position or ranked recommendation share [60], and mention count does not indicate whether the brand was the first recommendation, an alternative, a negative example, or a minor reference [61].

Documented poor-fit profiles include buyers whose primary KPI is recommendation position or ranked recommendation share, teams requiring clearly documented answer-level scoring of first-choice, shortlisted, or recommended brands, and buyers seeking only AI visibility tracking without paying for an Ahrefs ecosystem or without confirming required platform and prompt coverage [62]. Independent sources add companies seeking dedicated recommendation-share tracking without SEO suite overhead, buyers requiring recommendation position or ranking data, teams needing Claude, Meta AI, Grok, or other emerging LLM coverage, budget-conscious buyers facing all-in costs roughly 2.5 times the industry average for AI-only tools, and agencies needing white-label, client-branded AI visibility reporting [64].

Agencies are a specific mismatch. Brand Radar is built as an exploratory tool inside the Ahrefs ecosystem, not as a multi-client AI visibility tracker with white-label client reporting [66].

When Another Option May Be Better

Questions This Section Answers

  • What is a better alternative to Ahrefs Brand Radar for a buyer who needs documented recommendation-position ranking?
  • When should a buyer choose a dedicated AI visibility tool instead of the Ahrefs Brand Radar add-on?

Another option may be better in four documented situations: when recommendation position is the core metric, when broader engine coverage is required, when budget is the primary constraint, or when agency-grade reporting is needed.

On recommendation position, dedicated platforms are described as offering recommendation-level granularity such as first mention and position in answer that Brand Radar does not provide [67]. Independent coverage names Profound, Semrush, and Dageno AI in that context [67]. One platform notes that pure-play tools explicitly track numeric position within answers, citing BeVisible measuring first position and numeric position, Viali tracking rank in the answer, and Centium reporting rank against competitors, with no verified Ahrefs documentation confirming equivalent recommendation-position tracking [68].

On engine coverage, buyers needing Claude, Meta AI, or Grok coverage are directed to dedicated platforms offering broader LLM coverage [71]. One platform notes that Meev re-runs fixed prompt sets across ChatGPT, Claude, Gemini, Perplexity, Grok, Google AI Overviews, AI Mode, and DeepSeek, recording mention and position per answer [72].

On budget, dedicated AI visibility tracking is described at $99–$500/mo all-in without requiring an SEO suite subscription, with Peec AI, Trakkr, Cairrot, and Otterly.AI named [73]. Documented entry points include Centium at $99/mo, BeVisible at $99/mo ($79/mo annual), SE Visible Basic at $99, and a Mentionlytics AI Visibility add-on starting at $49/month [70].

On agency reporting, platforms like Profound, Semrush One, or Peec are described as offering white-label dashboards and agency-ready client reporting, which Brand Radar lacks [76].

Questions to Verify Before Buying

Questions This Section Answers

  • What should a buyer confirm with Ahrefs Brand Radar before signing a contract for recommendation-share tracking?
  • Does Ahrefs Brand Radar provide structured recommendation position, or only mentions, citations, and AI Share of Voice?

The single most important verification is whether the product provides structured recommendation position, ranked recommendation share, or only mentions, citations, and AI Share of Voice [77]. Buyers should also confirm whether they can export every answer and identify the exact recommendation order, context, and competing brands [78].

Platform and package scope needs confirmation: which platforms, models, locations, languages, and refresh cadences are included in the selected All Platforms or single-index package, and whether ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, Google AI Mode, and Claude are all included under the quoted package [79]. Buyers should also confirm the historical retention period and trend granularity that apply to Custom Prompts [80].

Billing mechanics need confirmation: how prompt checks are calculated when multiple platforms, locations, models, or refresh schedules are selected, what happens after the included check allowance is exceeded, whether overage billing can be capped, whether an Ahrefs base subscription is required, and what the total monthly cost including the base plan will be [81]. Buyers should also confirm whether API access, exports, scheduled reports, multi-user access, and competitor tracking are included, and what the cancellation, refund, renewal, and annual-commitment terms are for the exact purchase route [78].

Independent sources recommend running a 2–4 week pilot tracking a competitor across Ahrefs Brand Radar and a dedicated specialist platform to compare mention counts and citation sources before relying on the data for strategy-level decisions [83].

Final AI Consensus Verdict

Ahrefs Brand Radar is a mixed fit for AI Visibility Platforms for Tracking Recommendation Share. Three of seven platforms named it during the ranking stage at an average listed rank of 4.33, and six of seven platform fit ratings were mixed, with one rating it good (perplexity). No platform rated it a strong or best-in-class fit for this specific use case.

The consensus case for it is consistent: platform-by-platform AI visibility, AI Share of Voice, competitor benchmarking, citation source tracking, historical trends, and Custom Prompts for exact buyer questions, all inside an existing Ahrefs workflow [85]. The consensus case against it is equally consistent: public documentation emphasizes mentions, citations, and share of voice rather than structured recommendation position or ranked recommendation share [87], coverage omits Claude, Meta AI, and Grok [92], all-in costs are calculated at $828/mo or more by independent reviewers [95], and public pricing and packaging conflict across Ahrefs' own pages [97].

The practical verdict: treat Ahrefs Brand Radar as a credible AI visibility and share-of-voice product, and only as a recommendation-share solution if Ahrefs confirms structured recommendation extraction and ranking for the buyer's target platforms and prompts. Buyers already paying for Ahrefs may find it a defensible incremental add-on. Buyers whose core KPI is recommendation position should verify that output directly or evaluate dedicated alternatives before committing.

How This Review Was Produced

This review was produced from seven platform research responses collected for the study "Best AI Visibility Platforms for Tracking Recommendation Share," with an authoritative run research date of 2026-09-19. Each platform independently assessed Ahrefs Brand Radar against the use case criteria: recommendation-level data rather than simple brand mentions, platform-by-platform results, recommendation position, historical trends, and competitor comparisons.

Platform mentions in the ranking stage count only platforms that named Ahrefs Brand Radar during ranking discovery. All seven included platforms evaluated fit, but only three named the entity during ranking. Fit ratings were mixed for anthropic, deepseek, google, grok, kimi, and openai, and good for perplexity.

Citations in this review are platform-reported evidence, not independently verified facts. Company-owned sources are Ahrefs' own pages; independent sources are third-party reviews and journalism. Where a platform supplied no citation for a factual claim, the claim is labeled platform-reported or unverified.

Methodology Limitations

Several limitations apply. Platform-reported research dates differ from the authoritative run date: deepseek reported 2026-06-12, while anthropic, google, grok, kimi, openai, and perplexity reported 2026-09-19. Platform-reported dates are provenance metadata and do not independently prove freshness.

The supplied URLs were collected from platform responses and were not independently validated by the writer stage. Official-site retrieval failed for one or more mentions during research, and no failed fetch was used as a verified domain key. The identity used an exact-name fallback, and the matching reported domain was retained for downstream research but remains unverified.

Public Ahrefs pages conflict on prompt-index size, pricing, and whether a base subscription is required. These conflicts are reported rather than resolved. The definition of a Custom Prompt "check" (prompt × platform × location) is not entirely explicit in available sources. No supplied source quantifies Brand Radar's error rates on ChatGPT or Perplexity. Post-beta pricing for YouTube, TikTok, and Reddit tracking is described by independent sources but not confirmed from official Ahrefs communication.

Agreement among AI platforms does not prove product quality. This review reflects platform-reported evidence and does not include personal testing, customer experience, or independent verification of Ahrefs Brand Radar's performance.

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Sources

Company-Owned Sources

  • Ahrefs Pricing: How to Choose the Right Ahrefs Plan: https://ahrefs.com/blog/ahrefs-pricing/
  • Ahrefs Brand Radar Methodology: How we collect and model AI visibility data: https://ahrefs.com/blog/brand-radar-methodology/
  • Ahrefs Brand Radar: See ANY brand’s AI visibility: https://ahrefs.com/brand-radar
  • Ahrefs FAQ | Frequently asked questions: https://ahrefs.com/faq
  • Additional AI research evidence98 records
    1. AI research evidence record openai:c1
    2. AI research evidence record openai:c2
    3. AI research evidence record deepseek:c1
    4. AI research evidence record openai:c2
    5. AI research evidence record anthropic:26-6
    6. AI research evidence record anthropic:26-7
    7. AI research evidence record openai:c1
    8. AI research evidence record anthropic:23-4
    9. AI research evidence record openai:c3
    10. AI research evidence record openai:c4
    11. AI research evidence record anthropic:29-4
    12. AI research evidence record openai:c2
    13. AI research evidence record anthropic:5-9
    14. AI research evidence record anthropic:21-5
    15. AI research evidence record deepseek:c1
    16. AI research evidence record anthropic:26-2
    17. AI research evidence record anthropic:27-6
    18. AI research evidence record anthropic:25-3
    19. AI research evidence record anthropic:15-6
    20. AI research evidence record anthropic:5-10
    21. AI research evidence record anthropic:5-11
    22. AI research evidence record openai:c3
    23. AI research evidence record anthropic:6-3
    24. AI research evidence record anthropic:6-6
    25. AI research evidence record grok:1
    26. AI research evidence record anthropic:5-1
    27. AI research evidence record anthropic:23-4
    28. AI research evidence record anthropic:2-15
    29. AI research evidence record grok:0
    30. AI research evidence record anthropic:4-11
    31. AI research evidence record anthropic:13-14
    32. AI research evidence record openai:c1
    33. AI research evidence record openai:c2
    34. AI research evidence record perplexity:c7
    35. AI research evidence record perplexity:c8
    36. AI research evidence record openai:c5
    37. AI research evidence record openai:c1
    38. AI research evidence record openai:c2
    39. AI research evidence record anthropic:5-9
    40. AI research evidence record anthropic:26-7
    41. AI research evidence record anthropic:21-5
    42. AI research evidence record anthropic:5-10
    43. AI research evidence record anthropic:5-11
    44. AI research evidence record openai:c3
    45. AI research evidence record openai:c4
    46. AI research evidence record anthropic:6-6
    47. AI research evidence record openai:c5
    48. AI research evidence record anthropic:12-3
    49. AI research evidence record anthropic:13-2
    50. AI research evidence record anthropic:3-5
    51. AI research evidence record anthropic:12-7
    52. AI research evidence record anthropic:18-12
    53. AI research evidence record anthropic:2-9
    54. AI research evidence record openai:c1
    55. AI research evidence record perplexity:c6
    56. AI research evidence record openai:c1
    57. AI research evidence record openai:c2
    58. AI research evidence record anthropic:29-4
    59. AI research evidence record anthropic:5-10
    60. AI research evidence record openai:c3
    61. AI research evidence record anthropic:6-6
    62. AI research evidence record openai:c1
    63. AI research evidence record openai:c2
    64. AI research evidence record anthropic:13-14
    65. AI research evidence record anthropic:18-12
    66. AI research evidence record anthropic:1-12
    67. AI research evidence record anthropic:6-6
    68. AI research evidence record kimi:bevisible-1
    69. AI research evidence record kimi:viali-1
    70. AI research evidence record kimi:centium-1
    71. AI research evidence record anthropic:2-15
    72. AI research evidence record kimi:meev-1
    73. AI research evidence record anthropic:18-12
    74. AI research evidence record kimi:sevisible-1
    75. AI research evidence record kimi:mentionlytics-1
    76. AI research evidence record anthropic:1-12
    77. AI research evidence record openai:c3
    78. AI research evidence record openai:c4
    79. AI research evidence record openai:c1
    80. AI research evidence record openai:c2
    81. AI research evidence record openai:c5
    82. AI research evidence record perplexity:c6
    83. AI research evidence record anthropic:4-11
    84. AI research evidence record anthropic:13-14
    85. AI research evidence record openai:c1
    86. AI research evidence record openai:c2
    87. AI research evidence record openai:c3
    88. AI research evidence record anthropic:29-4
    89. AI research evidence record anthropic:5-9
    90. AI research evidence record anthropic:6-6
    91. AI research evidence record grok:1
    92. AI research evidence record anthropic:5-1
    93. AI research evidence record anthropic:23-4
    94. AI research evidence record anthropic:2-15
    95. AI research evidence record anthropic:12-3
    96. AI research evidence record anthropic:13-2
    97. AI research evidence record perplexity:c7
    98. AI research evidence record perplexity:c8

Independent Sources

Verify this research

Review the study details behind this page or download the public machine-readable verification record.

Study date
September 19, 2026
Platforms analyzed
7
Source records
37
Ranking mentions
3 of 7
Platform share
43%
Final consensus rank
#6

Research trail and source mix

Configured platforms

openai, anthropic, deepseek, grok, perplexity, kimi, google

Source mix

27 independent · 10 company-owned

Evidence support

34 direct · 3 partial

Important limitation

Use the run research_date as the study date. Platform-reported dates are provenance metadata and do not independently prove freshness.

Source snapshot SHA-256 15e6e409e5d8f06d45822de51a100b3c5c7fdd2676fa9b4367b17d91299491c7