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AthenaHQ AI Visibility Platform Fit Review for Competitive Benchmarking

AthenaHQ is a good fit for AI Visibility Platforms for Competitive Benchmarking, with caveats.

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

Answer Capsule

AthenaHQ is a good fit for AI Visibility Platforms for Competitive Benchmarking, with caveats. Two of seven platforms named AthenaHQ during the ranking stage — Anthropic (rank 6) and DeepSeek (rank 2) — giving it an average listed rank of 4.0 and a 28.6% share of included platform responses. The strongest reason to consider it is its documented cross-platform competitive benchmarking: prompt-level tracking, share-of-voice and citation monitoring, and competitor comparison across major AI answer engines [1]. The main limitation is that public pricing, metric methodology, and historical-trend depth are inconsistent or undisclosed across sources, and independent reviews flag credit-based cost unpredictability and shallow sentiment analytics [4].

Research Snapshot

FieldValue
Platform mentions in ranking stage2 of 7
Share of included platform responses28.6%
Average listed rank4.0
Best listed rank2 (DeepSeek)
Relevant product/model/planAthenaHQ Platform; Geo/AEO monitoring with competitive benchmarking
Overall use-case fitGood (platform-reported; not independently verified)
Research date2026-09-19

Why AthenaHQ Qualified for This Study

Questions This Section Answers

  • Is AthenaHQ a good choice for AI Visibility Platforms for Competitive Benchmarking?
  • Why did only two of seven AI platforms name AthenaHQ in the ranking stage?

AthenaHQ qualified because two platforms independently surfaced it during ranking discovery for this exact use case, and both tied it to competitive benchmarking rather than generic AI monitoring. Anthropic listed it at rank 6 and DeepSeek at rank 2, producing an average listed rank of 4.0 and a 28.6% share of included platform responses. The remaining five platforms evaluated fit but did not name AthenaHQ in their ranking output, so its qualification rests on a minority of the panel.

The entity's own positioning supports the category match. AthenaHQ describes cross-platform monitoring, competitive intelligence, prompt and response analysis, source and competitor insights, and recommendation tracking [7]. Its published research recommends measuring share of voice in key prompts and monitoring daily citations — the same metrics this buyer needs [8]. Independent directories describe share-of-voice, citation-rate, and recommendation-coverage benchmarking with side-by-side competitor views [9].

Qualification is not the same as verification. DeepSeek's assessment was explicitly uncertain, citing an unresolved identity and failed official-site retrieval [10]. Buyers should treat the ranking placement as a signal to investigate, not as proof of capability.

The Product, Model, Plan, or Service Most Relevant to AI Visibility Platforms for Competitive Benchmarking

Questions This Section Answers

  • Which AthenaHQ plan should a buyer choose for competitive benchmarking across multiple AI platforms?
  • Does AthenaHQ's Starter plan include the competitive benchmarking features a buyer needs?

The relevant offering is the AthenaHQ Platform, marketed as GEO/AEO monitoring with competitive benchmarking. Platform responses named it as "AthenaHQ Platform" and "AthenaHQ platform (Geo/AEO monitoring)" [11]. The self-serve entry point is the Starter plan, listed at $295/month with 3,600 credits, while Enterprise is custom-quoted [14].

Plan-to-feature mapping is the central buying question. AthenaHQ states that all plans include ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot, while paid plans add Google AI Mode, Claude, Grok, DeepSeek, and Meta AI [15]. Google's response reports up to 11 models on paid plans [17]. Advanced items — the ACE citation engine, multi-region and multi-language tracking, and persona targeting — are reported as Enterprise-gated [18].

Independent sources disagree on what Starter actually contains. Some describe prompt/response analysis, sources, and competitor insights on lower tiers [20]; others say key features are locked behind enterprise tiers and self-serve plans are limited to a single country [19]. This conflict is unresolved in the supplied evidence and should be settled with a written plan comparison before purchase.

What the AI Platforms Agreed About

Questions This Section Answers

  • What competitive benchmarking capabilities do AI platforms agree AthenaHQ provides?
  • Which AI answer engines does AthenaHQ monitor according to multiple platform assessments?

The clearest cross-platform agreement is that AthenaHQ tracks brand visibility against competitors across multiple AI answer engines. Anthropic, Google, Grok, and OpenAI all describe competitive benchmarking, share-of-voice, or competitor comparison as core functions [21]. Independent directories echo this with share-of-voice and citation-rate comparisons [25].

Platforms also agree on prompt-level analysis. Anthropic reports prompt-level tracking that pinpoints the exact queries triggering brand mentions [27]; Grok reports prompt and response analysis with AI blindspot detection [28]; Perplexity reports prompt and response analysis in independent writeups [29].

A third area of agreement is multi-engine coverage. Base plans cover ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot, with paid plans adding Google AI Mode, Claude, Grok, DeepSeek, and Meta AI [30]. Independent directories list ChatGPT, Perplexity, Gemini, Google AI Mode, Google AI Overviews, Claude, Copilot, and Grok [32]. Exact counts vary by source — 8 to 11 models — and should be confirmed for the specific plan [28].

Agreement here reflects repeated vendor-framed and review-framed descriptions, not independent validation of metric accuracy.

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

  • How reliable is AthenaHQ's recommendation-share and citation-share measurement for competitive benchmarking?
  • Is AthenaHQ's pricing transparent enough for a buyer to forecast annual cost?

Fit ratings diverged sharply: Google rated AthenaHQ "strong," OpenAI, Anthropic, and Grok rated it "good," Perplexity rated it "mixed," and DeepSeek and Kimi rated it "uncertain" [33].

Pricing is the most contested area. The official plans page shows a free Essential tier with 300 credits and a Starter plan at $295/month with 3,600 credits (official:C2). Independent reviews report roughly $295/month Starter and custom Enterprise [40], while other sources cite $95/month annual, $270/month annual, $245/month annual, $545/month Growth, and $2,000+/month Enterprise [41]. One AthenaHQ-hosted answer states specific pricing figures are not publicly listed [43]. These figures cannot be reconciled from the supplied evidence.

Metric methodology is another gap. OpenAI notes that recommendation share, citation share, visibility score, and sentiment definitions are not sufficiently documented for rigorous cross-vendor comparison [34]. Perplexity reports that public evidence for recommendation share and citation share is incomplete or inconsistent [37]. DeepSeek found no independent evidence of benchmark-specific modules at all [38].

Sentiment analytics drew consistent criticism. Independent reviews describe sentiment and competitive analytics as too basic to be actionable [44]. Identity is also unresolved: normalization flagged conflicting official domains and failed official-site retrieval, leaving the reported domain unverified [38].

Use-Case-Specific Features and Capabilities

Questions This Section Answers

  • Does AthenaHQ support historical trend analysis and competitive reporting for benchmarking?
  • Can AthenaHQ attribute AI visibility changes to revenue through GA4 or Shopify?

AthenaHQ's feature set maps well to most of this buyer's criteria, with two weak spots: historical-trend depth and metric definitions.

Recommendation share and share of voice. The platform tracks brand mention rates, citation rates, share of voice, and recommendation frequency across major LLMs [48]. AthenaHQ's own research recommends measuring share of voice in key prompts [50]. The calculation methodology is not publicly documented [51].

Citation share and source analysis. Citation tracking, source analysis, and competitor citation-frequency comparison are documented [51]. The proprietary Athena Citation Engine (ACE) is marketed as differentiating but is not independently validated and is reported as Enterprise-gated [53].

Prompt-level performance. Prompt-and-response analysis, prompt-level ChatGPT tracking, and competitor benchmarks are described across sources [51].

Platform differences. Cross-platform monitoring covers ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot, and — on paid plans — Google AI Mode, Claude, Grok, DeepSeek, and Meta AI [56]. Side-by-side platform-difference reporting depth is not clearly specified [51].

Historical trends and reporting. The Olympus dashboard centralizes AI metrics, and reports are generated in under five minutes [58]. Historical retention, sampling frequency, and export granularity are unclear [51].

Actionability and attribution. Content-gap identification, optimization recommendations, and an Action Center are documented [61]. GA4, Search Console, and Shopify integrations support revenue attribution [63]. Independent reviewers note the execution layer is more advisory than automated [65].

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

  • How much does AthenaHQ cost per month, and are there setup or cancellation fees?
  • What happens to AthenaHQ costs if prompt volume or credit usage exceeds the plan?

AthenaHQ's public pricing is inconsistent, and buyers should treat every figure below as unverified until confirmed in a written quote.

ItemReported figureSource
Essential (free)300 credits, $25 free credit(official:C2)
Starter$295/month, 3,600 credits
Starter (annual, conflicting)$95/month, $245/month, or $270/month annual
Growth$545/month, 10,000 credits
EnterpriseCustom; reported $2,000+/month
Extra credits$100 per 1,250 credits
API accessPaid add-on on Starter(official:C2)

The credit model is the main cost risk. One credit equals one AI model response, so monitoring many prompts across many engines consumes credits quickly [67]. Independent reviews describe credit-based pricing as scaling unpredictably and depleting fast [68]. One review reports the free tier as a one-time 300-credit grant [70]; the official page does not clearly state whether it recurs (official:C2).

Contract terms are largely undisclosed. One source reports a first-month 67% discount on annual plans [71]; another reports a 17% annual discount [72]. Cancellation, renewal, refund, data-retention, and service-level terms are not clearly disclosed in reviewed public sources [73]. Multiple sources report no free trial, requiring commitment without testing [75].

Best Suited For

Questions This Section Answers

  • Who gets the most value from AthenaHQ for competitive benchmarking?
  • Is AthenaHQ worth it for mid-market teams with a dedicated marketing budget?

AthenaHQ best suits mid-market and enterprise brands with dedicated marketing budgets that need real-time competitive share-of-voice monitoring, prompt-level diagnostics, and citation tracking across major AI platforms [77]. Teams that want measurement connected to action — content-gap identification, prioritized optimization tasks, and revenue attribution through GA4 or Shopify — are the strongest match [80].

Agencies managing multiple client brands are also a reported fit, particularly given unlimited users with role-based access control, which one source describes as more cost-effective than per-user pricing [82]. Multi-brand dashboards are documented [83].

E-commerce brands using Shopify are a specific fit because of the native Shopify integration for AI-search revenue attribution [84]. Organizations that can absorb credit-based cost variability and validate terms before signing are better positioned than those needing fixed, predictable spend.

Probably Not Best Suited For

Questions This Section Answers

  • Who should not choose AthenaHQ for AI Visibility Platforms for Competitive Benchmarking?
  • Is AthenaHQ overkill for early-stage startups or lean teams?

Small teams and solopreneurs are a poor fit. Independent reviews describe the platform as likely overkill for early-stage startups and note that the $295/month entry price and credit-based consumption suit organizations with dedicated budgets, not lean teams [86]. One review describes a high price floor [88].

Buyers needing deep sentiment or brand-perception analytics should look elsewhere. Independent reviews consistently rate AthenaHQ's sentiment and competitive analytics as too basic to be actionable [89].

Teams requiring hands-on content execution at scale will find the platform measurement-heavy. Reviewers describe it as focused on measurement and advisory recommendations rather than content creation, with an execution layer that is "more advisory than genuinely automated" [92].

Buyers who require transparent published pricing, verified vendor identity, or independently validated metric accuracy should not commit without resolving those gaps first [94].

When Another Option May Be Better

Questions This Section Answers

  • What is a better alternative to AthenaHQ for a buyer who needs predictable flat-rate pricing?
  • Which alternative suits a buyer who needs deeper sentiment analytics than AthenaHQ provides?

Several alternatives are named in the supplied research for specific buyer situations.

For lower entry price and predictable pricing, Rankability is reported at $99/month, well below AthenaHQ's reported floor, and Scalenut offers a lower starting price with content optimization included [97]. Otterly.ai ($29/month) and Peec AI (€89/month) are named for basic monitoring [99]. WorkDuo and Dageno AI are suggested for flat-rate pricing without credit forecasting [100].

For deeper sentiment and brand-perception analytics, Profound is described as offering deeper historical data and visual presentation of competitive perception shifts, plus SOC 2 Type II compliance and API maturity [101]. Profound is also described as a stronger enterprise option for reliable forecasting and sentiment depth [101].

For full GEO execution workflows, LovedByAI or Dageno AI bundle monitoring with content creation at more transparent pricing [103]. For multi-region benchmarking on self-serve plans, Rankability and others are reported to support global competitive monitoring at lower cost [103].

For buyers who need verified, documented competitive benchmarking immediately, Seerly, Viali, SE Visible, optiseo, and Mentionlytics are named as lower-risk alternatives with published pricing and documented prompt-level tracking [104].

Questions to Verify Before Buying

Questions This Section Answers

  • What should a buyer confirm with AthenaHQ before signing a contract?
  • How should a buyer validate AthenaHQ's credit consumption and metric definitions before purchase?

The supplied research surfaces a consistent verification list. Buyers should confirm the exact formula distinguishing recommendation share, mention share, visibility share, citation share, and share of voice [107]. They should confirm how prompts are sampled, localized, refreshed, and normalized across ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot, Claude, and other platforms [107].

Cost verification matters most. Buyers should ask what the true all-in cost is for monitoring N brands with M prompts per brand over 12 months, including base plan plus estimated credits [109]. They should confirm whether the free credit allocation is recurring or one-time [110], how credits are consumed by monitoring, reruns, backfills, exports, and AI-agent queries [107], and whether overages, additional locations, models, integrations, implementation, and support are billed separately [107].

Feature verification should cover which platforms, models, locations, languages, brands, competitors, users, and prompt volumes are included in the quoted plan [107]; whether ACE and multi-region tracking require Enterprise [111]; and whether historical retention, export formats, API access, and scheduled competitive reports are included [107].

Finally, buyers should confirm the legal entity, official domain, data-processing terms, security controls, and service-level commitments that will appear in the contract, given the unresolved identity flags in the research [113].

Final AI Consensus Verdict

AthenaHQ is a good fit for AI Visibility Platforms for Competitive Benchmarking, with material caveats. Two of seven platforms named it during ranking discovery, and the platforms that evaluated it agreed on its core strengths: cross-platform competitive benchmarking, prompt-level analysis, citation tracking, and multi-engine coverage. Google rated it "strong"; OpenAI, Anthropic, and Grok rated it "good."

The caveats are significant. Perplexity rated fit "mixed," and DeepSeek and Kimi rated it "uncertain," citing unresolved identity, missing independent evidence of benchmark-specific modules, and inconsistent public pricing. Independent reviews flag credit-based cost unpredictability, shallow sentiment analytics, Enterprise-gated advanced features, and no free trial. Public pricing ranges from $95 to $545 per month depending on source, and one AthenaHQ-hosted answer says figures are not publicly listed.

For a funded mid-market or enterprise team that can validate methodology, credit economics, plan coverage, and contract terms in writing before committing, AthenaHQ is a reasonable fit. For lean teams, buyers needing deep sentiment analytics, or procurement teams requiring verified identity and transparent pricing, alternatives carry lower risk. This review reflects platform-reported evidence, not independent verification of product performance.

How This Review Was Produced

This review was produced from a structured multi-platform research run dated 2026-09-19. Seven AI platforms — OpenAI, Anthropic, Google, Grok, Perplexity, DeepSeek, and Kimi — were asked which AI visibility platforms they would recommend for competitive benchmarking. Each platform returned fit assessments, use-case findings, pricing and terms, limitations, and verification questions. AthenaHQ was named during ranking discovery by two platforms (Anthropic and DeepSeek) and evaluated for fit by all seven.

The report uses only the supplied platform responses and their cited sources. Company-owned sources (AthenaHQ's own site, plans page, and research) are distinguished from independent sources (reviews, directories, and third-party analyses). No personal testing, customer interviews, or independent verification was performed. Where platforms disagreed, the disagreement is preserved rather than resolved.

Methodology Limitations

Several limitations apply. Platform-reported research dates differ from the authoritative run date: DeepSeek's assessment is dated 2026-06-15, while the other six platforms are dated 2026-09-19. Platform-reported dates are provenance metadata and do not independently prove freshness.

Platform mentions count only platforms that named AthenaHQ during ranking discovery; all seven platforms evaluated fit, but five did not name it in their ranking output. The supplied URLs were collected from platform responses and were not independently validated by the writer stage. Citations are platform-reported evidence, not independently verified facts.

Identity is unresolved. Normalization flagged conflicting official domains and failed official-site retrieval, and the reported domain remains unverified [115]. Pricing conflicts across official and independent sources could not be reconciled. Metric methodology for recommendation share, citation share, and historical trends is not publicly documented. DeepSeek's assessment ran without search enabled, so its findings are model-reported rather than retrieval-backed. No platform agreement in this review proves product quality.

Explore more ai visibility llm monitoring guidance in the category directory.

From the index

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Sources

Company-Owned Sources

  • Best alternatives for benchmarking brand vs competitors across LLMs: https://answers.athenahq.ai/athenahq-alternatives-that-benchmark-my-brand-vs-competitors-across-llms
  • What are AthenaHQ's pricing, features, and brand sentiment capabilities?: https://answers.athenahq.ai/athenahq-pricing-features-brand-sentiment
  • AthenaHQ | Agents to Win on AI Search: https://athenahq.ai/
  • How to Improve GEO Rankings with AI Visibility Monitoring Tools: https://athenahq.ai/blog/improve-geo-rankings-ai-visibility-monitoring-tools
  • AthenaHQ vs Semrush for AI Search Visibility: https://athenahq.ai/comparison/semrush
  • AthenaHQ Is Best for Competitive Benchmarking in CPG: https://athenahq.ai/industry/cpg/competitive-benchmarking
  • Plans & Pricing | Action on AI Search - AthenaHQ: https://athenahq.ai/plans
  • Pricing | AthenaHQ - Action on AI Search: https://athenahq.ai/pricing
  • Plans & Pricing | Action on AI Search - AthenaHQ: https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEVnY_TrVD2pxXQ4v3K6LF3mJcZGL7mHSe4HVIsY1uNqdid_0MzCoETipbGNgGDRs_-9uZjJlnB0P9LvmgClGjLiqKtJkkiZXu2zAc1i33S
  • One Dashboard, Every Brand - AthenaHQ: https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFeys2UrncGfW8ytRQ47MkvCJTgCt9I4XPzB3GHP38HRF7XbJE2w3bYtucsO7O3_Y6AzlV9wj7_SNiMw0PpKX8GWDgEwA6fPAPb_eweUIQODxXjcwvSEhVS3bnoWQ==
  • AthenaHQ vs Surfer SEO: Which Platform Is Better for AI Search Optimization in 2026?: https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGgeDjhufbK6oaPYLKArLJq7EyqSxr7Qf_tFS7azLmA4gu7SLy677BQ-Y57D518RnNkbRIeuthW7exoK44tTRGuLyiSYn1Rv_G909uHTPBmZAWzVMyySVbDoXOCUTCgsQ==
  • AthenaHQ | Agents to Win on AI Search: https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQH5-C_QU8Lg9xAzmIFS-kDfpAcQWP-x6eaAbKfQT8f9xcdJ4WHvKW31m7mXEGVHTxFDQrb9ostF9DDpfxUJMf8ZSvTL0vkSF3OD
  • Pricing | AthenaHQ: https://www.athenahq.ai/pricing
  • State of AI Search 2025 - AthenaHQ: https://www.athenahq.ai/reports/Athena%20State%20of%20AI%20Search%20Report%202025.pdf
  • Additional AI research evidence116 records
    1. AI research evidence record openai:c1
    2. AI research evidence record anthropic:4-1
    3. AI research evidence record grok:web:0
    4. AI research evidence record openai:c4
    5. AI research evidence record anthropic:1-13
    6. AI research evidence record perplexity:c6
    7. AI research evidence record openai:c1
    8. AI research evidence record openai:c5
    9. AI research evidence record anthropic:5-1
    10. AI research evidence record deepseek:c1
    11. AI research evidence record openai:c1
    12. AI research evidence record anthropic:4-1
    13. AI research evidence record grok:web:0
    14. AI research evidence record grok:web:11
    15. AI research evidence record anthropic:21-6
    16. AI research evidence record anthropic:21-7
    17. AI research evidence record google:cit_athenahq_plans
    18. AI research evidence record google:cit_dageno_review
    19. AI research evidence record anthropic:7-4
    20. AI research evidence record perplexity:c5
    21. AI research evidence record anthropic:4-1
    22. AI research evidence record google:cit_athenahq_home
    23. AI research evidence record grok:web:3
    24. AI research evidence record openai:c1
    25. AI research evidence record anthropic:5-1
    26. AI research evidence record anthropic:23-7
    27. AI research evidence record anthropic:2-7
    28. AI research evidence record grok:web:0
    29. AI research evidence record perplexity:c5
    30. AI research evidence record anthropic:21-6
    31. AI research evidence record anthropic:21-7
    32. AI research evidence record anthropic:23-11
    33. AI research evidence record google:cit_athenahq_home
    34. AI research evidence record openai:c1
    35. AI research evidence record anthropic:4-1
    36. AI research evidence record grok:web:0
    37. AI research evidence record perplexity:c6
    38. AI research evidence record deepseek:c1
    39. AI research evidence record kimi:search_void_1
    40. AI research evidence record openai:c4
    41. AI research evidence record anthropic:13-1
    42. AI research evidence record google:cit_athenahq_plans
    43. AI research evidence record openai:c6
    44. AI research evidence record anthropic:1-1
    45. AI research evidence record anthropic:1-13
    46. AI research evidence record anthropic:6-3
    47. AI research evidence record kimi:identity_unverified_1
    48. AI research evidence record grok:web:0
    49. AI research evidence record anthropic:4-3
    50. AI research evidence record openai:c5
    51. AI research evidence record openai:c1
    52. AI research evidence record anthropic:3-2
    53. AI research evidence record anthropic:22-6
    54. AI research evidence record google:cit_dageno_review
    55. AI research evidence record anthropic:2-7
    56. AI research evidence record anthropic:21-6
    57. AI research evidence record anthropic:21-7
    58. AI research evidence record anthropic:2-8
    59. AI research evidence record anthropic:5-5
    60. AI research evidence record perplexity:c6
    61. AI research evidence record anthropic:3-5
    62. AI research evidence record anthropic:16-2
    63. AI research evidence record anthropic:24-2
    64. AI research evidence record anthropic:15-18
    65. AI research evidence record anthropic:19-13
    66. AI research evidence record anthropic:10-11
    67. AI research evidence record google:cit_athenahq_plans
    68. AI research evidence record anthropic:7-4
    69. AI research evidence record google:cit_dageno_review
    70. AI research evidence record openai:c7
    71. AI research evidence record anthropic:12-1
    72. AI research evidence record grok:web:11
    73. AI research evidence record openai:c1
    74. AI research evidence record perplexity:c6
    75. AI research evidence record anthropic:2-5
    76. AI research evidence record anthropic:2-4
    77. AI research evidence record anthropic:25-3
    78. AI research evidence record anthropic:25-7
    79. AI research evidence record openai:c1
    80. AI research evidence record anthropic:16-2
    81. AI research evidence record anthropic:24-2
    82. AI research evidence record anthropic:18-4
    83. AI research evidence record google:cit_athenahq_multibrand
    84. AI research evidence record anthropic:15-18
    85. AI research evidence record google:cit_athenahq_surfer
    86. AI research evidence record anthropic:17-3
    87. AI research evidence record anthropic:17-4
    88. AI research evidence record openai:c7
    89. AI research evidence record anthropic:1-1
    90. AI research evidence record anthropic:1-13
    91. AI research evidence record anthropic:6-3
    92. AI research evidence record anthropic:19-13
    93. AI research evidence record anthropic:10-11
    94. AI research evidence record perplexity:c6
    95. AI research evidence record deepseek:c1
    96. AI research evidence record kimi:search_void_1
    97. AI research evidence record anthropic:11-1
    98. AI research evidence record anthropic:2-4
    99. AI research evidence record grok:web:0
    100. AI research evidence record google:cit_dageno_review
    101. AI research evidence record anthropic:1-1
    102. AI research evidence record anthropic:1-13
    103. AI research evidence record anthropic:7-4
    104. AI research evidence record kimi:competitor_ref_1
    105. AI research evidence record kimi:competitor_ref_2
    106. AI research evidence record kimi:search_void_1
    107. AI research evidence record openai:c1
    108. AI research evidence record perplexity:c6
    109. AI research evidence record anthropic:2-5
    110. AI research evidence record openai:c7
    111. AI research evidence record anthropic:7-4
    112. AI research evidence record google:cit_dageno_review
    113. AI research evidence record deepseek:c1
    114. AI research evidence record kimi:identity_unverified_1
    115. AI research evidence record deepseek:c1
    116. AI research evidence record kimi:identity_unverified_1

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
42
Ranking mentions
2 of 7
Platform share
29%
Final consensus rank
#7

Research trail and source mix

Configured platforms

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

Source mix

28 independent · 14 company-owned

Evidence support

14 direct · 7 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 a3ed16877567a4e8acd588a1980dca9220b4541f4087a0cd7c6d3527986ce4e2