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Peec AI AI Visibility Platform Fit Review for Citation Tracking

Peec AI is a good fit for companies that need citation-frequency tracking, source-level analysis, competitor citation benchmarking, and historical trends across major AI answer engines.

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

Answer Capsule

Peec AI is a good fit for companies that need citation-frequency tracking, source-level analysis, competitor citation benchmarking, and historical trends across major AI answer engines. Six of the seven platforms in this study named Peec AI during ranking discovery, and it finished second overall with an average listed rank of 3.5. Its strongest differentiator is the "used vs cited" source distinction, which separates sources an AI model consumed from URLs it explicitly referenced. The main limitation is pricing and coverage ambiguity: public sources conflict on U.S. dollar amounts, model counts, and which engines require paid add-ons, and no reviewed source independently audits Peec AI's citation-attribution accuracy.

Research Snapshot

FieldFinding
Platform mentions in ranking stage6 of 7 platforms (anthropic, deepseek, google, grok, openai, perplexity)
Share of included platform responses85.7%
Average listed rank3.5
Best listed rank2 (grok)
Relevant product/model/planPeec AI brand plans (Starter, Pro, Advanced) and agency plans; Enterprise for broad multi-engine tracking, API, and SSO
Overall use-case fitGood
Research date2026-09-19

Why Peec AI Qualified for This Study

Questions This Section Answers

  • Why did Peec AI qualify for this citation-tracking platform comparison?
  • How many AI platforms named Peec AI during the ranking stage?

Peec AI qualified because six of the seven platforms in this study named it during ranking discovery, clearing the two-mention minimum by a wide margin. The platforms that named it were anthropic, deepseek, google, grok, openai, and perplexity; kimi did not surface it. Its average listed rank was 3.5, with grok placing it highest at rank 2 and perplexity lowest at rank 5 [1].

Qualification reflects topical relevance, not verified product quality. Peec AI is positioned as a purpose-built AI-search visibility platform that tracks how brands appear and are cited in AI answers across ChatGPT, Google AI Overviews, Perplexity, Gemini, Claude, and similar engines [6]. Multiple platforms independently described citation and source tracking as core to the product, which is why it advanced to fit evaluation.

The qualification stage also surfaced identity caveats. Official-site retrieval failed for one or more Peec AI mentions, and identity used an exact-name fallback; the reported domain peec.ai was retained for downstream research but remains unverified at the ranking stage [6]. Buyers should confirm that Peec AI is the intended entity and that peec.ai is its current official domain.

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

Questions This Section Answers

  • Which Peec AI plan is most relevant for citation-frequency tracking and source-level analysis?
  • Is Peec AI's Enterprise tier required for broad multi-engine citation tracking?

The most relevant offering is the Peec AI platform itself, sold as brand plans (Starter, Pro, Advanced) and agency plans, with Enterprise most relevant when broad multi-engine citation tracking, API access, SSO, or custom tracking is required [8]. For a buyer whose priority is citation tracking, the brand Starter, Pro, or Advanced tiers cover daily tracking, while Enterprise unlocks all models and custom prompt setup.

Plan capacities as reported by company-controlled documentation: Starter covers 50 prompts, 3 chosen models, 1 project, and 1 country per project; Pro covers 150 prompts, 3 chosen models, 2 projects, and 3 countries per project; Advanced covers 350 prompts, 3 chosen models, 5 projects, multi-country tracking, and Looker Studio integration [11]. All three include daily tracking and unlimited users [9].

The citation-tracking feature set centers on a "used vs cited" distinction. Peec AI separates sources an AI model accessed during response generation from sources explicitly referenced in the response text [12]. It ranks most-cited sources by citation count and classifies sources into categories such as editorial, corporate, UGC, reference, and owned-site sources [14]. It also provides source-gap analysis where competitors are cited but the buyer is not [8].

Agency buyers have a separate plan line. The agency pricing page indicates allocations are slots rather than a monthly budget and references multi-brand tracking [10]. Independent sources describe agency tiers using credit allocations, with Growth at $495/month (25,000 credits) and Scale at $795/month (65,000 credits) [16].

What the AI Platforms Agreed About

Questions This Section Answers

  • What do multiple AI platforms agree Peec AI does well for citation tracking?
  • Does Peec AI track both used and cited sources across AI answer engines?

The platforms broadly agreed that Peec AI's core strength is citation and source analysis tied to tracked prompts. This was the most consistent finding across the six platforms that named it.

On citation-frequency tracking, platforms agreed Peec AI tracks citation rate and source citation counts across major engines on a daily cadence [17]. On source-level analysis, they agreed the platform exposes URL- and domain-level citation detail and distinguishes used from cited sources [20]. On competitor benchmarking, they agreed Peec AI supports competitor visibility, share-of-voice, and source-gap comparisons on the same prompt set [24]. On historical trends, they agreed daily tracking supports trend views over time [26].

Platforms also agreed on the collaboration model: unlimited user seats across public plans, which reduces per-seat cost for agency and in-house teams [25]. Google's response described unlimited seats as a differentiator versus per-user competitors [27].

Agreement here reflects repeated platform reporting, not independent verification. No reviewed source independently audited Peec AI's citation precision, recall, or completeness [29].

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

  • Where do AI platforms disagree about Peec AI's citation-tracking capabilities?
  • Can Peec AI connect citations to the exact prompts and answers where they appeared?

Platforms disagreed most sharply on whether Peec AI connects citations to the full prompt and generated answer. Grok reported that Peec AI links citations to specific tracked prompts and AI answers [30]. Google reported that the platform maps brand mentions and citation activity to specific custom prompts and shows the recent chats and answers where the brand is referenced [32]. Anthropic reported the opposite: that Peec AI does not provide explicit UI features to view the full prompt and generated answer text alongside the citations they produced [34]. Perplexity rated prompt-to-answer-to-citation linkage as unclear from primary evidence [36]. This is a material conflict for a buyer whose core requirement is connecting citations with the prompts and answers in which they appear.

Platforms also disagreed on model coverage. The official pricing page states up to 13 LLM models for Enterprise, while some independent sources describe six named engines or additional API models [38]. Google reported that base tiers track 3 chosen models by default and that premium models such as Claude, DeepSeek, and Grok require paid add-ons or upgrades [40]. Grok reported that Claude and GPT-5 Search coverage may require Enterprise custom pricing [30].

Pricing conflicts were widespread. A company-controlled July 2026 documentation page reports $95/month Starter, $245/month Pro, and $495/month Advanced on monthly billing [42]. Independent sources report different figures, including $89/month Starter and $499/month Enterprise [30], $80/month Starter billed annually [43], and €70/month annually for Starter [44]. Anthropic reported Growth and Advanced both at $495/month [45]. The differences may reflect billing currency, annual versus monthly billing, regional pricing, or stale data.

One platform reported a more fundamental uncertainty. Kimi stated that no verifiable public information about Peec AI's products, features, or pricing was found through web search on 2026-09-19, that the official website could not be retrieved, and that Peec AI was not found in any independent comparison or directory during its search [46]. Kimi's response also listed competing platforms with documented feature sets and pricing [47]. This is a single-platform retrieval failure, not evidence that Peec AI lacks these capabilities, but it is a disclosed limitation.

Use-Case-Specific Features and Capabilities

Questions This Section Answers

  • Does Peec AI support source-gap analysis and competitor citation benchmarking?
  • Which AI engines does Peec AI track for citation monitoring?

Peec AI covers the six core capabilities in this use case, with the prompt-to-answer connection being the least consistently confirmed.

CapabilityAssessmentEvidence
Citation-frequency trackingAdvantageTracks citation rate and source citation counts across engines on daily cadence
Source-level analysisAdvantageURL/domain-level detail, source classification, used vs cited distinction
Platform comparisonsAdvantageCross-engine visibility, position, and sentiment comparisons
Competitor citation benchmarkingAdvantageCompetitor visibility, share of voice, and source gaps on the same prompts
Historical trendsAdvantageDaily tracking with trend views over time
Prompt-to-answer connectionMixedGrok and Google report prompt-linked answers; Anthropic reports no full prompt/answer view

Engine coverage as reported: ChatGPT, Google AI Overviews, Google AI Mode, Microsoft Copilot, Perplexity, and Gemini appear on public plan descriptions [50]. Google reported that Claude, DeepSeek, and Grok are available as upgrades [52]. Anthropic reported daily tracking across ChatGPT, Gemini, Claude, Perplexity, Google AI Mode, and Microsoft Copilot [53].

Additional capabilities reported by platforms include a robots.txt crawlability checker, Model Context Protocol (MCP) and REST API access for querying data inside tools such as Claude and Cursor [54], sentiment tracking from the Pro tier upward [55], and multi-language and multi-country tracking on all paid plans [55].

Known technical limits: Peec AI states that tracked AI models only see HTML content and cannot read behind paywalls or load JavaScript-dependent content, which can create measurement blind spots [56]. Anthropic reported that Peec AI uses UI scraping to simulate real user interactions rather than API-based estimates [57], though another source described methodology documentation as basic [57].

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

  • How much does Peec AI cost per month for citation tracking, and are there model add-on fees?
  • What are Peec AI's cancellation, renewal, and annual-billing terms?

Peec AI's pricing is not consistently verifiable across sources, and buyers should confirm current U.S. checkout amounts before budgeting. The official public pricing page confirms Starter, Pro, Advanced, and custom Enterprise brand plans but did not expose numeric prices in the retrieved content [58]. A company-controlled July 2026 documentation page reports the following monthly U.S. figures [59]:

PlanReported monthly pricePromptsModelsProjects
Starter$95503 chosen1
Pro$2451503 chosen2
Advanced$4953503 chosen5
EnterpriseCustomCustomizableAll modelsUnlimited

Independent sources report conflicting figures. Grok reported Starter at approximately $89/month and Enterprise at $499/month [60]. Google reported $80/month Starter, $205/month Pro, and $420/month Advanced when billed annually, with $499/month Advanced on monthly billing [61]. Google also reported €70/month annually for Starter in a European context [62]. Anthropic reported Growth and Advanced both at $495/month and Scale at $795/month [63]. The differences may reflect billing currency, annual versus monthly billing, regional pricing, or stale data.

Additional fees reported: model add-ons at $35/month Starter, $85/month Pro, and $165/month Advanced [59]; per-model add-ons of approximately €20–30/month per engine beyond the three included baseline models [64]; and extra model add-ons of roughly $35–$165/month per engine depending on plan [60]. Enterprise integrations, custom setup, API access, SSO, and dedicated support are subject to custom commercial terms [59].

Contract terms as reported: monthly and annual billing are available, with annual billing saving approximately 15% [59]. Upgrades are prorated by day; downgrades take effect at the end of the billing cycle; project data remains intact on upgrade or downgrade [64]. A seven-day free trial with no credit card required was reported by one independent source [65]. Refund, cancellation, renewal, notice, data-retention, and unused-prompt terms were not verified in the reviewed sources [59].

Best Suited For

Questions This Section Answers

  • Who gets the most value from Peec AI for citation tracking?
  • Is Peec AI a good fit for agencies tracking citations across multiple client brands?

Peec AI is best suited to marketing and SEO teams that need citation-frequency tracking, source-gap analysis, and competitor benchmarking across major AI answer engines, and that can operate within prompt, project, model, and country limits [66].

Specific fits reported across platforms:

  • Marketing and SEO teams tracking citation frequency and source gaps across ChatGPT, Google AI Overviews, Google AI Mode, Microsoft Copilot, Perplexity, and Gemini [66].
  • Agencies tracking multiple client brands with daily citation monitoring and multi-client workspaces [67].
  • Teams prioritizing the "used vs cited" source distinction to understand content consumption versus explicit citation [71].
  • Collaborative teams that benefit from unlimited user seats across all pricing tiers [73].
  • Technical marketers who want to feed visibility metrics into workflow agents via MCP or REST API [74].
  • Organizations needing multi-language and multi-country citation tracking [67].

Probably Not Best Suited For

Questions This Section Answers

  • Who should not choose Peec AI for citation tracking?
  • Is Peec AI unsuitable for buyers who need citation-to-revenue attribution?

Peec AI is probably not the best fit for buyers whose requirements fall outside monitoring and diagnostics.

Reported exclusions:

  • Buyers requiring all available engines on an entry-level plan, since Starter through Advanced include three models by default and additional models incur add-on fees [75].
  • Buyers requiring independently validated citation precision or recall measurements; no reviewed source independently audits these [77].
  • Organizations needing AI traffic estimation or citation-to-lead attribution; Peec AI does not provide these [78].
  • Teams lacking internal execution capacity and expecting the platform to create, optimize, or publish content; the Actions module provides recommendations only [79].
  • Enterprise buyers requiring SOC 2 or HIPAA compliance for citation data; one platform reported Peec AI does not offer these certifications [79].
  • Buyers needing coverage of seven or more AI models on base plans without per-model fees [79].
  • Buyers who need the full prompt and generated answer displayed alongside citations, given the conflicting platform reports on this capability [81].

When Another Option May Be Better

Questions This Section Answers

  • When is a Peec AI alternative better for citation tracking?
  • What should a buyer choose instead of Peec AI if they need citation-to-revenue attribution?

Another option may be better in several reported scenarios.

If the buyer needs many AI engines without paying model add-ons or moving to Enterprise, a broader-coverage platform may fit better [83]. If the buyer has a small prompt set, one or two users, and limited need for source-gap or competitor analysis, a lower-cost tracker may suffice [83]. If auditability, reproducibility, or formal data-governance requirements outweigh ease of use, an enterprise platform with independently documented measurement methodology may be preferable [83]. If the buyer needs recommendations and implementation beyond citation monitoring, a workflow or content-execution platform may be better [83].

If ROI attribution is the primary success metric, Peec AI does not estimate traffic or attribute citations to leads, while some competitors provide attribution [85]. If regulated-industry compliance is required, one platform reported that a competitor offers compliance certifications Peec AI does not [86]. If the buyer is budget-conscious and tracking fewer than 25 prompts, lower entry-price options were reported [86].

Kimi's response listed several competing platforms with documented feature sets and pricing, including Foglift, GetCited, Viali, Visiby, CitationRadar, Citare, Citany, and CiteMetrix [87]. These are platform-reported alternatives, not independently validated recommendations.

Questions to Verify Before Buying

Questions This Section Answers

  • What should a buyer confirm with Peec AI before signing a contract?
  • How can a buyer verify Peec AI's citation methodology and model coverage?

Before purchase, buyers should confirm the following with Peec AI directly, since public sources conflict or leave these unresolved:

  • What exact U.S. dollar price, billing currency, annual commitment, renewal, cancellation, and refund terms apply at checkout on September 19, 2026 [95]?
  • Which six or up to 13 models are currently available to the account, and which require Enterprise or add-on fees [97]?
  • Are Google AI Overviews, Google AI Mode, ChatGPT, Gemini, Perplexity, and Copilot all available for the buyer's target U.S. locations and languages [99]?
  • How are citation frequency, source retrieval, duplicate URLs, snippets, redirects, citations without visible URLs, and hallucinated or inaccessible sources counted [95]?
  • Can the buyer export raw prompt, answer, source URL, citation, timestamp, model, country, and competitor records through API or CSV [95]?
  • What are the retention period, historical backfill rules, rate limits, API limits, and data-deletion procedures [95]?
  • Are source-gap and competitor benchmarks calculated from the same prompt set, geography, model, and tracking cadence [95]?
  • Does Peec AI display the full prompt text and generated AI answer in the dashboard alongside the citations that appeared in that response [101]?
  • Does Peec AI use APIs, web scraping, or a hybrid approach to capture citations, and is methodology documentation available [101]?
  • What service-level commitments, support response times, onboarding fees, and implementation assistance apply to Enterprise [95]?
  • Does Peec AI provide an independent validation report or customer-accessible methodology for citation-attribution accuracy [103]?

Final AI Consensus Verdict

Peec AI is a good fit for AI Visibility Platforms for Citation Tracking. Six of seven platforms named it during ranking discovery, and it finished second overall with an average listed rank of 3.5. Platforms broadly agreed it delivers citation-frequency tracking, source-level analysis with a used-versus-cited distinction, competitor citation benchmarking, and historical trends across major AI answer engines.

The principal purchasing risks are unresolved rather than disqualifying. Public sources conflict on U.S. pricing, model counts, and which engines require add-ons. Platforms disagreed on whether Peec AI connects citations to the full prompt and generated answer, which is a core requirement in this use case. No reviewed source independently audits Peec AI's citation-attribution accuracy, and one platform reported a complete retrieval failure for the entity. Buyers should treat Peec AI as a strong candidate requiring a hands-on trial and direct vendor confirmation of pricing, engine coverage, citation methodology, and contract terms before purchase.

How This Review Was Produced

This review evaluates Peec AI only for the AI Visibility Platforms for Citation Tracking use case. It draws on fit-research responses from seven platforms (anthropic, deepseek, google, grok, kimi, openai, perplexity) collected for a study dated 2026-09-19. Each platform independently assessed Peec AI against the use-case criteria: citation-frequency tracking, source-level analysis, platform comparisons, competitor citation benchmarking, historical trends, and the ability to connect citations with the prompts and answers in which they appear.

Platform mentions in the ranking stage count only platforms that named Peec AI during ranking discovery. All included platforms evaluated fit, but not all named the entity. Citations are platform-reported evidence, not independently verified facts. Company-owned sources are labeled as such; independent sources are labeled separately in the Sources section.

Methodology Limitations

Several limitations apply to this review.

Platform-reported research dates differ from the authoritative run date. Deepseek's response is dated 2026-02-14, while the study date is 2026-09-19 [104]. Platform-reported dates are provenance metadata and do not independently prove freshness.

Official-site retrieval failed for one or more Peec AI mentions, and identity used an exact-name fallback; the reported domain peec.ai remains unverified at the ranking stage [104]. The official homepage fetch also failed because the HTML exceeded the size limit, so no official-page excerpt was available for verification.

Pricing conflicts were not resolved by guessing. Company-controlled documentation, independent reviews, and platform responses report different figures in different currencies and billing views [105]. Buyers should confirm current U.S. checkout pricing directly.

No reviewed source independently audits Peec AI citation precision, recall, or completeness [109]. Platform agreement on capabilities reflects repeated reporting, not verified product quality. The supplied URLs were collected from platform responses and were not independently validated by the writer stage. Kimi's response reported a complete retrieval failure for Peec AI, which is disclosed as a single-platform limitation rather than evidence of absence [110].

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

Sources

Company-Owned Sources

  • Citany — AI Brand Visibility Monitor Across 8 AI Engines: https://citany.com/
  • FAQ - CiteMetrix: https://citemetrix.com/faq/
  • Understanding sources - Peec.ai Docs: https://docs.peec.ai/understanding-sources
  • AI Citation Tracking Across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews: https://foglift.io/monitor
  • Peec AI - AI Search Analytics for Marketing Teams: https://peec.ai/
  • Peec AI - AI Search Analytics for Marketing Teams: https://peec.ai/ai-instructions
  • AI Search Analytics for Marketing Teams - Peec AI: https://peec.ai/entity-map
  • Pricing for Brands: https://peec.ai/pricing
  • Peec AI Agency Pricing: Plans Built for Multi-Brand Tracking: https://peec.ai/pricing-agencies
  • Pricing for Peec AI - AI Search Analytics for Marketing teams and SEO agencies: https://peec.ai/pricing?orderBy=name0
  • Peec AI - AI Visibility and Share of Voice Tracking: https://peec.ai/product/ai-visibility
  • Visibility Tracker — See Every AI Answer: https://viali.ai/product/visibility-tracking/
  • AI Visibility Platform for ChatGPT, Perplexity & AI Overviews: https://visiby.net/ai-visibility-platform
  • Brand Radar — AI search visibility monitoring across 5 platforms: https://www.citare.ai/brand-radar
  • AI Citation Tracking for ChatGPT, Perplexity & Gemini: https://www.citationradar.ai/
  • Additional AI research evidence110 records
    1. AI research evidence record openai:c1
    2. AI research evidence record anthropic:1-22
    3. AI research evidence record google:1.1.1
    4. AI research evidence record grok:web:1
    5. AI research evidence record perplexity:c1
    6. AI research evidence record deepseek:c1
    7. AI research evidence record deepseek:c2
    8. AI research evidence record openai:c1
    9. AI research evidence record openai:c2
    10. AI research evidence record perplexity:c6
    11. AI research evidence record openai:c6
    12. AI research evidence record anthropic:13-2
    13. AI research evidence record anthropic:11-11
    14. AI research evidence record anthropic:10-2
    15. AI research evidence record openai:c3
    16. AI research evidence record anthropic:19-1
    17. AI research evidence record anthropic:15-10
    18. AI research evidence record anthropic:5-5
    19. AI research evidence record grok:web:1
    20. AI research evidence record anthropic:13-2
    21. AI research evidence record anthropic:11-11
    22. AI research evidence record grok:web:2
    23. AI research evidence record google:1.1.1
    24. AI research evidence record openai:c1
    25. AI research evidence record anthropic:1-22
    26. AI research evidence record openai:c2
    27. AI research evidence record google:1.1.7
    28. AI research evidence record google:1.1.8
    29. AI research evidence record openai:c5
    30. AI research evidence record grok:web:1
    31. AI research evidence record grok:web:3
    32. AI research evidence record google:1.1.1
    33. AI research evidence record google:1.2.3
    34. AI research evidence record anthropic:16-13
    35. AI research evidence record anthropic:27-6
    36. AI research evidence record perplexity:c2
    37. AI research evidence record perplexity:c3
    38. AI research evidence record openai:c2
    39. AI research evidence record openai:c4
    40. AI research evidence record google:2.1.7
    41. AI research evidence record google:1.1.8
    42. AI research evidence record openai:c6
    43. AI research evidence record google:2.2.2
    44. AI research evidence record google:2.2.7
    45. AI research evidence record anthropic:19-1
    46. AI research evidence record kimi:search_failure_1
    47. AI research evidence record kimi:foglift_1
    48. AI research evidence record kimi:getcited_1
    49. AI research evidence record kimi:viali_1
    50. AI research evidence record perplexity:c4
    51. AI research evidence record openai:c2
    52. AI research evidence record google:2.1.7
    53. AI research evidence record anthropic:5-5
    54. AI research evidence record google:1.2.5
    55. AI research evidence record anthropic:1-22
    56. AI research evidence record openai:c1
    57. AI research evidence record anthropic:16-13
    58. AI research evidence record openai:c2
    59. AI research evidence record openai:c6
    60. AI research evidence record grok:web:1
    61. AI research evidence record google:2.2.2
    62. AI research evidence record google:2.2.7
    63. AI research evidence record anthropic:19-1
    64. AI research evidence record anthropic:1-22
    65. AI research evidence record anthropic:25-2
    66. AI research evidence record openai:c1
    67. AI research evidence record anthropic:1-22
    68. AI research evidence record google:1.1.9
    69. AI research evidence record perplexity:c4
    70. AI research evidence record perplexity:c6
    71. AI research evidence record anthropic:7-10
    72. AI research evidence record anthropic:13-2
    73. AI research evidence record google:1.1.7
    74. AI research evidence record google:1.2.5
    75. AI research evidence record anthropic:19-1
    76. AI research evidence record google:2.1.7
    77. AI research evidence record openai:c5
    78. AI research evidence record anthropic:18-11
    79. AI research evidence record anthropic:1-22
    80. AI research evidence record google:1.1.9
    81. AI research evidence record anthropic:16-13
    82. AI research evidence record grok:web:1
    83. AI research evidence record openai:c1
    84. AI research evidence record google:1.1.9
    85. AI research evidence record anthropic:18-11
    86. AI research evidence record anthropic:1-22
    87. AI research evidence record kimi:foglift_1
    88. AI research evidence record kimi:getcited_1
    89. AI research evidence record kimi:viali_1
    90. AI research evidence record kimi:visiby_1
    91. AI research evidence record kimi:citationradar_1
    92. AI research evidence record kimi:citare_1
    93. AI research evidence record kimi:citany_1
    94. AI research evidence record kimi:citemetrix_1
    95. AI research evidence record openai:c6
    96. AI research evidence record perplexity:c7
    97. AI research evidence record openai:c2
    98. AI research evidence record openai:c4
    99. AI research evidence record perplexity:c4
    100. AI research evidence record google:1.2.5
    101. AI research evidence record anthropic:16-13
    102. AI research evidence record grok:web:1
    103. AI research evidence record openai:c5
    104. AI research evidence record deepseek:c1
    105. AI research evidence record openai:c6
    106. AI research evidence record grok:web:1
    107. AI research evidence record google:2.2.2
    108. AI research evidence record anthropic:19-1
    109. AI research evidence record openai:c5
    110. AI research evidence record kimi:search_failure_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
44
Ranking mentions
6 of 7
Platform share
86%
Final consensus rank
#2

Research trail and source mix

Configured platforms

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

Source mix

25 independent · 19 company-owned

Evidence support

39 direct · 5 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 50df6de10d07597c4e4470846fdff7e152dfbfb37a15355e86af295cd1363c91