One marketing need. Multiple leading AI platforms. One transparent consensus. How it works
AI MarketingConsensus Index

AI Consensus Fit Review

AmICited AI Citation Intelligence Platform Fit Review for Source and Domain Tracking

AmICited is a good fit for companies that need prompt-level, domain-level, page-level, competitor, and time-series tracking of AI citations, based on the reviewed evidence.

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

Answer Capsule

AmICited is a good fit for companies that need prompt-level, domain-level, page-level, competitor, and time-series tracking of AI citations, based on the reviewed evidence. Three of seven platforms named AmICited during the ranking stage (anthropic, grok, kimi), a 42.9% share of included platform responses, at an average listed rank of 4.33 and a best listed rank of 4. The strongest reason to consider it is its dedicated Source & Citation Intelligence module, which vendor documentation describes as mapping cited domains down to exact pages and prompts across multiple AI engines. The main limitation is that nearly all reviewed evidence is company-owned, with no independent validation of citation accuracy, completeness, or methodology.

Research Snapshot

FieldValue
Platform mentions in ranking stage3 of 7 (anthropic, grok, kimi)
Share of included platform responses42.9%
Average listed rank4.33
Best listed rank4
Relevant product/model/planAmICited Source & Citation Intelligence; Standard platform
Overall use-case fitGood (per openai, anthropic, perplexity); Strong (per grok, google); Uncertain (per deepseek, kimi)
Research date2026-09-19

Why AmICited Qualified for This Study

Questions This Section Answers

  • Is AmICited a good choice for AI Citation Intelligence Platforms for Source and Domain Tracking?
  • How many AI platforms named AmICited during the ranking stage for source and domain tracking?

AmICited qualified because three of the seven included platforms named it during ranking discovery, and its published product documentation directly addresses the study's four criteria: which domains and pages AI systems rely on, which sources support competitors, how citation patterns differ across platforms and prompts, and how those patterns change over time [1].

The three platforms that named AmICited were anthropic (rank 4), grok (rank 4), and kimi (rank 5). Four platforms — openai, perplexity, google, and deepseek — evaluated AmICited's fit but did not name it in the ranking stage. Platform mentions count only platforms that named the entity during ranking discovery, not platforms that evaluated fit.

Fit ratings diverged across the seven platforms: grok and google rated AmICited "strong," openai, anthropic, and perplexity rated it "good," and deepseek and kimi rated it "uncertain." The uncertainty from deepseek and kimi stems largely from those platforms not locating verifiable public detail during their searches, not from evidence of product failure.

The Product, Model, Plan, or Service Most Relevant to AI Citation Intelligence Platforms for Source and Domain Tracking

Questions This Section Answers

  • Which AmICited plan should a buyer choose if they need page-level and domain-level citation tracking across multiple AI engines?
  • Does AmICited's Source & Citation Intelligence module track cited pages, or only domains?

The relevant product is AmICited Source & Citation Intelligence, described across platform responses as the module that maps cited domains, pages, and sources across AI engines [5]. The ranking stage also referenced a "Standard platform" label, which deepseek noted could not be matched to a published tier name on the official site [9].

Vendor documentation states that Source & Citation Intelligence aggregates cited domains and supports drill-down to the exact pages and prompts associated with a source [5]. Prompt-detail documentation describes ranked cited URLs by provider, with provider tabs for comparison [10]. A separate feature page describes domain and URL views, competitor source identification, structure comparison, and citation-rank change tracking [11].

Platform responses disagreed on page-level granularity. Google reported that the module maps every citation by domain and exact URL, with a heatmap of which engine trusts which source [12]. Perplexity reported the platform tracks which domains AI engines cite, which pages win, and which source each engine references [7]. Anthropic, by contrast, stated that AmICited provides domain-level tracking but does not offer explicit page-level URL tracking, citing an independent comparison that credits Profound with page-level citation tracking instead [14]. This is a direct conflict between platform responses and should be resolved with a trial before purchase.

What the AI Platforms Agreed About

Questions This Section Answers

  • What do AI platforms agree AmICited does well for source and domain tracking?
  • Is AmICited's multi-engine coverage consistent across platform evaluations?

Platforms broadly agreed on four points.

First, AmICited publicly presents itself as a citation-tracking service. The homepage describes tracking whether brands are mentioned and cited by AI systems [16], and the features page describes an AI citation tracking tool that shows which domains AI engines cite and which pages win [19].

Second, the platform covers multiple AI engines. Vendor materials list ChatGPT, Perplexity, Gemini, Claude, Copilot, Google AI Overviews, and Google AI Mode, with some additional engines described as weekly coverage [17]. Google's response listed eight core platforms including Grok and DeepSeek [22].

Third, competitor source analysis is a stated capability. Vendor pages describe showing which competitor websites AI platforms cite most, identifying source landscape gaps, and comparing citation frequency against competitors [23].

Fourth, historical or time-series tracking is described. Vendor documentation describes fixed-schedule prompt runs, stored answers with citations and positions, citation-over-time charts, and per-provider breakdowns [17]. Google reported full archival of AI responses with citation footnotes and diff comparisons [27].

Agreement among platforms does not establish product quality. Most of these findings trace back to AmICited-owned pages, and company-owned citations materially outnumber independent citations in this study.

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

  • Why did some AI platforms rate AmICited's fit as uncertain for source and domain tracking?
  • Does AmICited gate Claude, Grok, and DeepSeek behind a higher-priced plan?

Disagreements clustered around four issues.

Page-level granularity. Google and perplexity reported page-level URL tracking [28]. Anthropic reported domain-level tracking only, citing an independent source that credits Profound with page-level citation tracking [30]. Deepseek and kimi could not confirm granularity at all [32].

Engine gating. Google reported that Copilot requires Pro or higher, and Claude, Grok, and DeepSeek are gated behind the Premium €500/month plan [34]. Anthropic reported the opposite — that all AI engines are included on every plan with no per-engine upsells [35]. Perplexity noted that a SaaS-focused page says Premium adds additional engines [36]. This conflict is material for buyers who need Claude or Grok at entry pricing.

Trial terms. Perplexity found conflicting trial descriptions: one directory snippet mentions a free one-off check plus 14-day full access, while another page describes a 7-day free trial with card required [37]. The official pricing page states a 7-day free trial with 1 domain, 5 prompts, 3 articles, and card required (official:C2). Anthropic reported a 7-day free trial with no credit card required [38]. These accounts do not agree.

Pricing currency and level. OpenAI reported Starter at €50, Pro at €120, Premium at €500, and Enterprise from €1,500 [39]. Anthropic reported the same EUR tiers but also referenced a $300–500/month range in one comparison page [40]. Google reported the same EUR tiers with high confidence [42]. Deepseek and kimi located no verifiable pricing at all [32].

Independent validation. No platform located independent evidence validating citation accuracy, sampling representativeness, or comparative performance [44]. One independent directory and one independent comparison page were cited, but neither validates measurement quality [37].

Use-Case-Specific Features and Capabilities

Questions This Section Answers

  • Does AmICited support competitor source gap analysis for AI citations?
  • Can AmICited track how AI citation patterns change over time by source?

Source and domain identification. Vendor documentation describes aggregating cited domains with drill-down to exact pages and prompts [46], ranked cited URLs by provider [47], and domain or URL views with competitor source identification [48]. Domain & Source Intelligence is described as tracking which domains AI platforms cite, measuring citation frequency, and enabling competitive source benchmarking [49].

Cross-platform comparison. Source views can be compared by provider [47]. The platform publicly lists tracking for ChatGPT, Perplexity, Gemini, Claude, Copilot, Google AI Overviews, and Google AI Mode, with some additional engines described as weekly coverage [51].

Competitor source intelligence. Vendor pages describe showing which competitor websites AI platforms cite most, identifying source landscape gaps, and analyzing what makes sources citation-worthy [53]. A citation-ranking-gap feature describes identifying domains or pages cited instead of the buyer's pages [48].

Historical monitoring. Vendor documentation describes fixed-schedule prompt runs, stored answers with citations and positions, citation-over-time charts, daily history, and per-provider breakdowns [51]. Google reported full archival of AI responses with citation footnotes and diff comparisons [56]. Perplexity noted that public sources only partially evidence historical depth beyond weekly or new-per-week reporting [57].

Prompt and fan-out analysis. Vendor documentation describes prompt tracking and semantic-map or fan-out-query capabilities [47]. Anthropic reported the platform converts traditional keywords into conversational prompts [58].

Content execution. Vendor documentation describes content-generation workflows based on losing prompts, provider answers, citations, sources, and fan-out queries [59]. Anthropic reported that AmICited is fundamentally a monitoring tool with no built-in content gap analysis, writing agents, or optimization features [60], which conflicts with the vendor's own content-generation page. Google reported the AI article-writing agent is bundled across all tiers rather than optional [61].

Integration depth. Anthropic reported basic integrations including API access, email reporting, and standard CRM connections, with a limited ecosystem compared to enterprise platforms [62]. The official pricing page lists Google Search Console, Bing Webmasters, and GA4 as data sources included on every plan (official:C2).

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

  • How much does AmICited cost per month, and do unused credits roll over?
  • Are there setup, overage, or cancellation fees with AmICited?

Published monthly tiers are Starter at €50, Pro at €120, Premium at €500, and Enterprise from €1,500 [64]. Prices exclude VAT [68]. The official pricing page states every plan includes the full engine list and unlimited seats (official:C1).

Credits drive usage. One AI response — a prompt run on one engine — costs 0.015 credits; agent runs and article generation cost more [69]. Unused credits do not roll over, and top-ups are available any time [70]. A prompt tracked daily on eight engines equals eight AI responses per day, or 0.12 credits (official:C2).

Plan allowances as reported by openai: Starter includes 50 credits, 1 domain, and 10 competitors; Pro includes 120 credits, 5 domains, and 30 competitors; Premium includes 500 credits, 50 domains, and unlimited competitors; Enterprise includes custom credit volume with unlimited domains and prompts [64]. Google reported similar tiers with additional detail, including AI article counts per tier [66].

Contract and cancellation terms are only partly documented. The official terms state payments are non-refundable unless required by law, no refunds or credits are issued for partial periods, upgrades are prorated, downgrades apply at the next billing cycle, and prices are subject to change with notice (official:C3). The terms also state that registering grants AmICited the right to use the customer's logo and name for marketing unless the customer opts out in writing (official:C3). A comparison page says annual billing provides a 20% discount [71], and the official pricing page shows a yearly option with one month free (official:C2). Perplexity noted that annual contract requirements are not clearly verified in public snippets [68].

No separate implementation, overage, API, data-export, or premium-support fees were verified in the reviewed sources [64]. Anthropic reported that additional team seats may incur per-workspace seat fees not specified in public pricing [72]. Deepseek and kimi located no verifiable pricing at all [73].

Best Suited For

Questions This Section Answers

  • Who gets the most value from AmICited for source and domain tracking?
  • Is AmICited a good fit for mid-market SaaS and ecommerce teams monitoring AI citations?

AmICited is best suited for mid-market SaaS and ecommerce companies monitoring citations across multiple AI answer platforms [75]. Marketing, SEO, and content teams that need to identify competitor sources and pages are a stated fit [78]. Teams wanting daily or recurring prompt monitoring rather than one-time AI visibility checks are also a stated fit [80].

Buyers who value transparent, published entry pricing and fast implementation are a reasonable fit. Vendor materials describe 1–2 weeks to implementation [82], and the official pricing page publishes all four tiers (official:C2). Buyers who treat citation intelligence as a complement to human review rather than an audit-grade system of record are also a stated fit [83].

Probably Not Best Suited For

Questions This Section Answers

  • Who should not choose AmICited for AI Citation Intelligence Platforms for Source and Domain Tracking?
  • Is AmICited suitable for enterprises requiring SSO, RBAC, or security certifications?

Buyers requiring independently audited measurement methodology or guaranteed citation completeness are not a good fit, because no independent validation was located [84]. Large enterprises needing clearly documented SSO, RBAC, compliance, data-retention, or procurement terms are also not a good fit, because those details are not clearly documented in the reviewed public sources [84].

Organizations needing broad industry-specific prompt libraries beyond the publicly identified ecommerce and SaaS focus should confirm coverage before buying [89]. Teams needing deep technical SEO or GEO integration with existing marketing stacks may find the integration ecosystem limited [87]. Buyers who need end-to-end content creation workflows alongside monitoring should note the conflict between vendor content-generation pages and anthropic's report that AmICited lacks built-in content gap analysis or writing agents [92].

Buyers who require unlimited access to Claude, Grok, or DeepSeek at entry pricing should verify engine gating, because google reported those engines are locked behind the Premium plan while anthropic reported all engines are included on every plan [94].

When Another Option May Be Better

Questions This Section Answers

  • What is a better alternative to AmICited for a buyer who needs page-level citation tracking with independent validation?
  • When should a buyer choose Profound, Scrunch, or a broader SEO suite instead of AmICited?

Anthropic listed several conditions for choosing alternatives: page-level URL citation tracking and content-asset-level gap analysis (Profound or Scrunch); deep integration with MarTech stacks, GA4, or BI tools (Profound); prompt-demand forecasting and panel-derived query volume data (Profound); end-to-end content execution (Profound, Scrunch, or Dageno); technical SEO/GEO capabilities such as schema audits and crawlability assessment (Scrunch); workflow automation on visibility changes (Profound); and multi-market support across 38+ countries (Profound) [96].

OpenAI listed similar conditions: enterprise procurement requiring documented governance, security certifications, SSO, RBAC, formal support commitments, or independently reviewable methodology; broader SEO or digital-intelligence suites; and specialist research workflows needing statistically controlled experiments or independent auditing [99].

Kimi named specific alternatives with published pricing and coverage: Cite AI tracks 6 engines from $19/month; Cited covers up to 7 platforms with Source Intelligence showing competitor-cited pages; Vercite covers 5 engines; Truffle covers 6 engines on all plans with daily cycles; Indexly offers daily citation share trends; DemandSphere tracks URLs at domain and page level [100].

Grok noted that buyers prioritizing USD pricing or seat-based plans over credits, or requiring independent benchmarks or case studies from neutral sources, may prefer another option [106]. Google noted that buyers wanting all 8+ AI models on an entry-level tier may prefer Trakkr or Rankscale, and buyers wanting a traditional SEO suite with white-label AI tracking may prefer SE Ranking [107].

Questions to Verify Before Buying

Questions This Section Answers

  • What should a buyer confirm with AmICited before signing a contract?
  • How does AmICited distinguish a linked citation from an unlinked brand mention?

The reviewed platform responses produced a consistent list of verification items. Buyers should confirm what exactly consumes one credit and how many prompts, providers, answers, or competitors each tier permits [109]. Buyers should confirm whether ChatGPT, Perplexity, Gemini, Claude, Copilot, Google AI Overviews, and Google AI Mode are all available at the same refresh frequency on the selected plan, because platform responses conflict on engine gating [111].

Buyers should confirm whether reporting identifies cited domains and individual pages, or only aggregate brand mentions, because platform responses conflict on page-level granularity [113]. Buyers should confirm the prompt and query panel methodology, sample size, and refresh cadence [115]. Buyers should confirm how far back historical citation data goes and whether trend or change tracking is retained [116].

Buyers should confirm whether they can select United States locations, languages, logged-out or clean-session conditions, and device or browser parameters [109]. Buyers should confirm whether complete answer text, cited URLs, citation positions, timestamps, provider metadata, and historical exports are retained and exportable [109]. Buyers should confirm API, MCP, CSV-export, retention, rate-limit, and integration restrictions [109].

Buyers should confirm whether SSO, RBAC, audit logs, data-processing terms, deletion controls, and security certifications are available for the selected plan [120]. Buyers should confirm annual-discount, renewal, cancellation, refund, unused-credit, overage, and enterprise-commitment terms, because public pages conflict on trial length and annual discounting [122]. Buyers should confirm how AmICited distinguishes a linked citation from an unlinked brand mention and how citations are deduplicated [120]. Buyers should confirm what accuracy or completeness benchmarks AmICited provides for source and domain detection [120].

Final AI Consensus Verdict

AmICited is a good fit for AI Citation Intelligence Platforms for Source and Domain Tracking when the buyer values actionable, multi-provider, prompt-level and time-series citation intelligence at a relatively accessible published price [124]. Three of seven platforms named it during ranking, and five of seven rated fit as good or strong.

The strongest reason to consider it is the dedicated Source & Citation Intelligence module, which vendor documentation describes as mapping cited domains down to exact pages and prompts across multiple AI engines [127]. The main limitation is that nearly all reviewed evidence is company-owned, with no independent validation of citation accuracy, completeness, or methodology [127].

Treat the platform as a sampled monitoring system, not an independently verified census of AI citations. Enterprise buyers should condition purchase on verification of measurement methodology, coverage, security, export, retention, and contract terms. Buyers who need page-level granularity, deep integrations, or independently audited methodology should compare against the alternatives named in this review before committing.

How This Review Was Produced

This review was produced from platform fit-research responses collected on 2026-09-19 from seven AI platforms: openai (gpt-5.6-luna), anthropic (claude-haiku-4-5-20251001), google (gemini-3.5-flash), grok (x-ai/grok-4.3), perplexity (perplexity/sonar), kimi (moonshotai/kimi-k2.6), and deepseek (deepseek-v4-flash). Six platforms ran with search enabled; deepseek ran with search disabled. Each platform evaluated AmICited against the same use case and returned fit ratings, strengths, limitations, pricing observations, and verification questions.

The ranking stage counted only platforms that named AmICited during discovery. Fit ratings and use-case findings were collected separately and are reported as platform-reported evidence, not independently verified facts. All citations in this review are platform-reported evidence. The supplied URLs were collected from platform responses and were not independently validated by the writer stage. Company-owned citations materially outnumber independent citations in this study, and company claims are not described as independently verified.

Methodology Limitations

Several limitations apply. First, evidence reviewed is mainly vendor-owned; independent validation of measurement quality was not found [132]. Second, platform-reported research dates differ from the authoritative run date: deepseek reported 2026-06-02 while the run research date is 2026-09-19. Platform-reported dates are provenance metadata and do not independently prove freshness.

Third, sampling frequency and engine coverage differ by provider, with some surfaces described as weekly rather than daily [135]. Fourth, public documentation does not clearly establish citation completeness, statistical confidence, reproducibility, or API-rate limits [132]. Fifth, publicly reviewed materials do not clearly document enterprise governance, security certifications, SSO, RBAC, retention, or detailed data-processing terms [132].

Sixth, the product appears especially oriented toward ecommerce and SaaS, so broader industry coverage should be confirmed [138]. Seventh, platform responses conflict on trial length, engine gating, page-level granularity, and pricing currency; this review describes the conflicts rather than resolving them. Eighth, deepseek ran without search enabled, so its findings rest on limited retrieved material and should be weighted accordingly. Ninth, no platform reported personal testing, customer experience, or independent verification of AmICited's performance.

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

Sources

Company-Owned Sources

Independent Sources

  • AmICited: pricing, engines and status: https://agentvisibilitytools.com/tool/amicited/
  • Additional AI research evidence139 records
    1. AI research evidence record openai:c1
    2. AI research evidence record openai:c2
    3. AI research evidence record openai:c3
    4. AI research evidence record openai:c6
    5. AI research evidence record openai:c1
    6. AI research evidence record anthropic:18-1
    7. AI research evidence record perplexity:c1
    8. AI research evidence record grok:web:5
    9. AI research evidence record deepseek:c1
    10. AI research evidence record openai:c2
    11. AI research evidence record openai:c3
    12. AI research evidence record google:1.2.1
    13. AI research evidence record perplexity:c3
    14. AI research evidence record anthropic:21-3
    15. AI research evidence record anthropic:21-4
    16. AI research evidence record deepseek:c1
    17. AI research evidence record openai:c5
    18. AI research evidence record grok:web:0
    19. AI research evidence record perplexity:c3
    20. AI research evidence record grok:web:5
    21. AI research evidence record anthropic:3-2
    22. AI research evidence record google:1.1.6
    23. AI research evidence record anthropic:18-15
    24. AI research evidence record anthropic:18-13
    25. AI research evidence record openai:c4
    26. AI research evidence record openai:c6
    27. AI research evidence record google:1.2.1
    28. AI research evidence record google:1.2.1
    29. AI research evidence record perplexity:c1
    30. AI research evidence record anthropic:21-3
    31. AI research evidence record anthropic:21-4
    32. AI research evidence record deepseek:c1
    33. AI research evidence record kimi:cited-source-intel
    34. AI research evidence record google:2.1.7
    35. AI research evidence record anthropic:37-8
    36. AI research evidence record perplexity:c4
    37. AI research evidence record perplexity:c7
    38. AI research evidence record anthropic:6-2
    39. AI research evidence record openai:c5
    40. AI research evidence record anthropic:5-1
    41. AI research evidence record anthropic:33-1
    42. AI research evidence record google:2.2.1
    43. AI research evidence record kimi:cited-main
    44. AI research evidence record openai:c1
    45. AI research evidence record grok:web:0
    46. AI research evidence record openai:c1
    47. AI research evidence record openai:c2
    48. AI research evidence record openai:c3
    49. AI research evidence record anthropic:18-1
    50. AI research evidence record anthropic:18-4
    51. AI research evidence record openai:c5
    52. AI research evidence record anthropic:3-2
    53. AI research evidence record anthropic:18-13
    54. AI research evidence record anthropic:18-15
    55. AI research evidence record openai:c6
    56. AI research evidence record google:1.2.1
    57. AI research evidence record perplexity:c11
    58. AI research evidence record anthropic:3-6
    59. AI research evidence record openai:c7
    60. AI research evidence record anthropic:33-8
    61. AI research evidence record google:1.2.8
    62. AI research evidence record anthropic:5-3
    63. AI research evidence record anthropic:5-5
    64. AI research evidence record openai:c5
    65. AI research evidence record anthropic:33-1
    66. AI research evidence record google:2.2.1
    67. AI research evidence record grok:web:11
    68. AI research evidence record perplexity:c2
    69. AI research evidence record anthropic:37-12
    70. AI research evidence record anthropic:6-13
    71. AI research evidence record perplexity:c8
    72. AI research evidence record anthropic:5-3
    73. AI research evidence record deepseek:c1
    74. AI research evidence record kimi:cited-main
    75. AI research evidence record openai:c4
    76. AI research evidence record anthropic:3-7
    77. AI research evidence record perplexity:c4
    78. AI research evidence record openai:c3
    79. AI research evidence record anthropic:18-15
    80. AI research evidence record openai:c5
    81. AI research evidence record anthropic:3-6
    82. AI research evidence record anthropic:5-1
    83. AI research evidence record deepseek:c1
    84. AI research evidence record openai:c1
    85. AI research evidence record deepseek:c1
    86. AI research evidence record grok:web:0
    87. AI research evidence record anthropic:5-5
    88. AI research evidence record perplexity:c2
    89. AI research evidence record openai:c4
    90. AI research evidence record anthropic:3-7
    91. AI research evidence record anthropic:5-6
    92. AI research evidence record openai:c7
    93. AI research evidence record anthropic:33-8
    94. AI research evidence record google:2.1.7
    95. AI research evidence record anthropic:37-8
    96. AI research evidence record anthropic:5-6
    97. AI research evidence record anthropic:21-3
    98. AI research evidence record anthropic:21-4
    99. AI research evidence record openai:c1
    100. AI research evidence record kimi:cite-ai-pricing
    101. AI research evidence record kimi:cited-source-intel
    102. AI research evidence record kimi:vercite-tracking
    103. AI research evidence record kimi:truffle-tracking
    104. AI research evidence record kimi:indexly-citation
    105. AI research evidence record kimi:demandsphere-analytics
    106. AI research evidence record grok:web:11
    107. AI research evidence record google:2.1.6
    108. AI research evidence record google:2.2.8
    109. AI research evidence record openai:c5
    110. AI research evidence record anthropic:37-12
    111. AI research evidence record google:2.1.7
    112. AI research evidence record anthropic:37-8
    113. AI research evidence record google:1.2.1
    114. AI research evidence record anthropic:21-3
    115. AI research evidence record deepseek:c1
    116. AI research evidence record perplexity:c11
    117. AI research evidence record openai:c6
    118. AI research evidence record perplexity:c2
    119. AI research evidence record anthropic:5-3
    120. AI research evidence record openai:c1
    121. AI research evidence record anthropic:5-5
    122. AI research evidence record perplexity:c7
    123. AI research evidence record perplexity:c8
    124. AI research evidence record openai:c5
    125. AI research evidence record anthropic:33-1
    126. AI research evidence record google:2.2.1
    127. AI research evidence record openai:c1
    128. AI research evidence record openai:c2
    129. AI research evidence record perplexity:c1
    130. AI research evidence record deepseek:c1
    131. AI research evidence record grok:web:0
    132. AI research evidence record openai:c1
    133. AI research evidence record deepseek:c1
    134. AI research evidence record grok:web:0
    135. AI research evidence record openai:c5
    136. AI research evidence record perplexity:c2
    137. AI research evidence record anthropic:5-5
    138. AI research evidence record openai:c4
    139. AI research evidence record anthropic:3-7

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
38
Ranking mentions
3 of 7
Platform share
43%
Final consensus rank
#4

Research trail and source mix

Configured platforms

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

Source mix

5 independent · 33 company-owned

Evidence support

37 direct · 1 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 281b8c9edb8c2cd8344f35080b3eba00e31e0cdd1c9795febf0666f0fc10fcd4