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6 AI models · 5 answers per model · ranked by score
ordered by brand score: 100 pts for rank 1 → 20 for rank 5 · no mention = 0
Each bar covers the middle half of one model’s answers (Q1–Q3); the line inside it is that model’s median rank for the times it recommended the brand. The badge under a brand says how far apart the models are on it, and “first choice” scores rank 1 far above the rest — a brand can place well overall and still rarely lead an answer.
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Median rank each model gave each brand.
Scroll the table sideways to see every model →
| claude-opus-5 | gemini-3.7-flash | gpt-5.6-luna | gpt-5.6-sol | gpt-5.6-terra | kimi-k3 | |
|---|---|---|---|---|---|---|
| Accenture | 2.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| McKinsey & Company | 1.0 | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 |
| Boston Consulting Group | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 3.0 |
| Deloitte | 3.0 | 5.0 | 3.0 | 2.0 | 3.0 | 4.0 |
| IBM Consulting | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 |
| Bain & Company | — | 4.0 | 5.0 | — | 5.0 | 4.0 |
A dash means that model never named the brand in any of its runs. Deeper blue is better: rank 1 is the brand a model would recommend first.
0 of 6 brands split the panel. Each mark is one model's score for the brand, on the same 0–100 scale as the ranking.
No brands where the models disagree
from here down, the models agreegemini-3.7-flash 28 → gpt-5.6-sol 72
gpt-5.6-sol 52 → gemini-3.7-flash 100
gemini-3.7-flash 60 → gpt-5.6-terra 100
gpt-5.6-terra 40 → gemini-3.7-flash 80
claude-opus-5 0 → gemini-3.7-flash 28 · 2 of 6 never named it
gemini-3.7-flash 4 → gpt-5.6-sol 28
A hollow mark is a model that never named the brand in any of its runs, which scores 0. Agreement is not endorsement — a brand every model ignores equally agrees just as tightly as one they all rank first.
Across → how frequently the panel names the brand at all.
Up ↑ how often the answers that name it put it first.
The horizontal line sits at 20% — the rate a named brand would lead at if the models were picking one of its 5 slots at random. Above it they are choosing it first on purpose. Both figures average across models, so a thinly sampled model counts the same as a heavily sampled one.
Accenture leads with a score of 91 of 100, ranked by 6 of 6 models.
Across the six models, the top of the field is settled and the disagreements concentrate in the middle. Accenture and McKinsey trade the top two spots, while Boston Consulting Group, Deloitte, IBM Consulting, and Bain sort into a more contested lower tier where individual models diverge noticeably.
The clearest consensus is on the extremes. Accenture holds a category-wide median of 1 and IBM Consulting a rock-solid median of 5 (Q1 5, Q3 5) with every model landing on exactly 5. IBM's uniformity extends to reasoning: all six credit engineering depth, watsonx, and regulated-environment fit, and five of the six cite vendor bias toward IBM's own stack as the reason it sits mid-tier. There is little to separate the models on either brand.
McKinsey is also broadly agreed on in substance — C-suite strategy credibility and QuantumBlack depth recur everywhere — even though its placement varies by a rank or two.
The sharpest divergences are best read model by model:
BCG shows the widest disagreement of any brand: gemini places it 2nd, kimi 3rd, and the remaining four settle at 4. Deloitte spans an even wider model range (2 through 5) despite a tidy 3.5 category median — a case where the aggregate obscures real disagreement.
The source data is suggestive rather than conclusive, and these links are hypotheses.
The most legible pattern is on Accenture: models ranking it 1st lean on first-party material (Accenture service pages, Technology Vision, the $3B investment newsroom item), whereas gemini-3.7-flash — the lowest ranker — is described as drawing almost entirely on external outlets (Gartner, IDC MarketScape, Bloomberg, FT, WSJ) with no first-party Accenture citations. It is plausible that a reliance on outside analyst and press framing produced its more measured, integrator-oriented view, but the data shows correlation, not cause.
A similar hypothesis fits Deloitte: gemini is again noted as leaning more on Gartner, IDC MarketScape, Bloomberg, FT, and Fortune, coinciding with its lowest placement — consistent with a pattern of external sourcing tracking cooler positioning, though the sample is too small to confirm.
For McKinsey and BCG, the recalled sources are heavily first-party (State of AI, QuantumBlack; BCG X, AI Radar) across models regardless of rank, so the sourcing does not obviously explain why claude and gemini rate them higher than the gpt models. Here the placement differences appear to come from how each model weighs strategy versus at-scale delivery rather than from distinct source pools.
Bain and IBM rest on fewer ranked answers (11 and 19) than the leaders (30), so their model-level medians are less stable. All sources are recalled by the models from memory rather than verified citations, which limits how far any source-to-view link can be pushed.
91 sources across 428 references, grouped by site from 256 recalled names. The top 5 carry 44% of them.
mckinsey.com
Sources are what each model recalled as having shaped its view — not verified citations. A model without web access reconstructs a reference from memory, so a link may not lead where the model thought it did.
Accenture lands at the top of the field, with a median rank of 1 (Q1 1, Q3 2) across 30 ranked answers from 6 models. Agreement is strong at the high end: four models — gpt-5.6-luna, gpt-5.6-sol, gpt-5.6-terra, and kimi-k3 — place it at a median rank of 1 with no spread (Q1 1, Q3 1), while claude-opus-5 sits slightly lower at median 2 (Q1 1, Q3 2) and gemini-3.7-flash is the clear outlier at median 3 (Q1 3, Q3 3). The recurring theme uniting the top rankings is scale of delivery and execution: strategy-through-implementation breadth, cloud and integration depth, a multi-billion-dollar AI investment, and hyperscaler alliances. Several models frame Accenture as the strongest default for moving beyond pilots to enterprise-wide deployment, though most also qualify that its strength skews toward execution rather than top-table strategy — a point noted explicitly by kimi-k3, claude-opus-5, and gemini-3.7-flash, with gpt-5.6-sol adding caveats around scope, cost, and complexity.
The sources behind these themes divide along two lines. Models placing Accenture at rank 1 lean heavily on first-party material — Accenture's own AI and data-AI service pages, Technology Vision 2024, the Reinvention in the Age of Generative AI insight, annual reports, and the $3 billion AI investment newsroom announcement — supplemented by third-party analysts such as Gartner, Everest Group, IDC, Forrester, and the Stanford AI Index. claude-opus-5 grounds its execution-focused view in earnings-release and investor material on generative AI bookings alongside NVIDIA/Microsoft alliance announcements, Gartner's Magic Quadrant, and HFS/Everest Group assessments. By contrast, gemini-3.7-flash, the lowest ranker, draws almost entirely on external outlets — Gartner, IDC MarketScape, Bloomberg, Financial Times, Forrester, and the Wall Street Journal — with no first-party Accenture citations, which aligns with its more measured positioning of the firm as an integration and infrastructure play rather than a strategy leader.
Accenture is presented as the strongest overall choice, combining strategy, technology implementation, and managed services at scale, particularly for moving beyond pilots to enterprise-wide deployment.
Accenture is framed as the strongest default choice given its broad combination of strategy, integration, cloud, and delivery scale, while noting clients must control scope, cost, and complexity.
Accenture is consistently the strongest all-around choice, pairing board-level strategy with large-scale delivery to move from pilots to enterprise-wide operating-model and workforce change.
Accenture is highlighted for unmatched implementation and delivery scale, multi-billion-dollar AI investment, and key platform alliances, though its brand skews toward execution rather than top-table strategy.
Accenture is characterized as having the largest scaled AI delivery capability — multi-billion-dollar gen-AI bookings, tens of thousands of AI-trained practitioners, and deep hyperscaler partnerships — making it the strongest choice for end-to-end execution rather than strategy alone.
Accenture is described as offering unmatched global scale and systems integration for massive IT and infrastructure overhauls, backed by multi-billion-dollar AI investment, though it leans more toward implementation than pure top-down strategy.
Each model’s own references for this brand, grouped by site. Tile area is that site’s share of the model’s references; tap one for the pages behind it.
accenture.com
accenture.com
accenture.com
newsroom.accenture.com
Sources are what each model recalled as having shaped its view — not verified citations. A model without web access reconstructs a reference from memory, so a link may not lead where the model thought it did.
also named McKinsey
McKinsey & Company holds a strong overall position, with a median rank of 2 (Q1 1, Q3 2) across 30 ranked answers from 6 models, and appears under both "McKinsey & Company" and "McKinsey." The models converge on a consistent underlying rationale: McKinsey is valued for C-suite strategy credibility, use-case prioritization, and operating-model redesign, with its QuantumBlack unit repeatedly cited as adding technical and data-science depth. This theme is supported by heavy recall of McKinsey's own research and capability pages — particularly the State of AI report and QuantumBlack insights — alongside third-party analyst and business-press sources such as Gartner, Forrester, Forbes, Harvard Business Review, IDC MarketScape, and coverage of McKinsey's internal Lilli tool (Reuters, CNBC).
Agreement on the reasoning is high, but placement varies more than the median suggests. Gemini-3.7-flash and claude-opus-5 rank it strongest (both median 1, with gemini at Q1 1/Q3 1 and no noted drawbacks), while gpt-5.6-luna, gpt-5.6-terra, and kimi-k3 land at median 2, and gpt-5.6-sol places it lowest at median 3 (Q1 3, Q3 4). The recurring caveat behind the lower placements is consistent across those models: McKinsey is seen as ranking below Accenture or larger systems integrators for hands-on, at-scale implementation and ongoing operations, with premium pricing and a thinner delivery bench sometimes requiring additional implementation partners.
McKinsey's QuantumBlack unit is framed as combining board-level strategy credibility with a large bench of data scientists, backed by influential research (State of AI), making it best for enterprise-wide value-case definition and operating-model redesign — though at premium cost and often needing implementation partners at scale.
McKinsey is consistently presented as pairing world-class C-suite strategy with deep technical execution via QuantumBlack, positioning it as the premier end-to-end choice for large-scale Fortune 500 transformation and change management, with no noted drawbacks.
McKinsey is described as strong at AI strategy, use-case prioritization, and operating-model change, but consistently ranked below Accenture because large-scale technical implementation and ongoing operations may require systems integrators or other partners.
McKinsey is framed as excellent for defining the AI agenda, prioritizing use cases, and operating-model/executive alignment, with QuantumBlack adding capability, while consistently cautioning clients to validate hands-on engineering and delivery capacity for large implementations.
McKinsey is characterized by unmatched C-suite credibility, strategy rigor, and QuantumBlack's technical depth plus influential State of AI research, but ranked second to Accenture due to premium pricing and a thinner hands-on, at-scale implementation bench.
McKinsey is repeatedly cited for executive alignment, operating-model redesign, and tying AI to measurable business value with QuantumBlack support, but ranked below larger systems integrators for implementation-heavy, at-scale programs that may need additional partners.
Each model’s own references for this brand, grouped by site. Tile area is that site’s share of the model’s references; tap one for the pages behind it.
mckinsey.com
mckinsey.com
mckinsey.com
mckinsey.com
mckinsey.com
Sources are what each model recalled as having shaped its view — not verified citations. A model without web access reconstructs a reference from memory, so a link may not lead where the model thought it did.
also named BCG
Boston Consulting Group lands mid-pack across the six models, with a median rank of 3 (Q1 3, Q3 4) over 30 ranked answers. There is moderate agreement on its placement but a visible spread in enthusiasm: gemini-3.7-flash ranks it highest at a median of 2 (Q1 2, Q3 2), kimi-k3 places it at 3 (Q1 3, Q3 3), and the remaining four models — claude-opus-5, gpt-5.6-luna, gpt-5.6-sol, and gpt-5.6-terra — each land at a median of 4, with gpt-5.6-terra the most consistent at that level (Q1 4, Q3 4). The firm is named as both Boston Consulting Group and BCG.
The recurring theme is a strategy-plus-build profile: models consistently credit BCG for elite corporate strategy, operating-model redesign, and value/ROI framing, paired with technical delivery through BCG X, and they cite the firm's research output, including the 10-20-70 framing and OpenAI/Anthropic partnerships. The countervailing theme, and the main reason it ranks behind McKinsey, Accenture, and Deloitte in several answers, is a perceived thinner global implementation, systems-integration, and managed-services footprint for full Fortune 500-scale rollouts. Sources cluster around BCG's own materials — BCG X, BCG's AI capabilities pages, the AI Radar, and the BCG Henderson Institute — supplemented by MIT Sloan Management Review (including joint MIT Sloan–BCG research), Harvard Business Review, and the Harvard Business School–BCG field experiment on GenAI productivity. Analyst and press validation appears through Gartner, IDC MarketScape, the Forrester Wave (AI Consultancies / AI Services), Forbes, the World Economic Forum, and Financial Times and Reuters coverage of BCG's AI revenue share.
BCG pairs elite corporate strategy with technical build and engineering through BCG X, emphasizing proprietary domain-specific AI solutions, business value/ROI, and responsible AI governance for large enterprises.
BCG blends strategy with BCG X build capability and highly cited AI research (e.g., 10-20-70 framing), but ranks behind McKinsey and Accenture because its large-scale global delivery bench is thinner and less proven at full enterprise-wide deployment.
BCG X provides genuine build capability alongside top-tier strategy, with roughly a fifth of revenue from AI work and notable OpenAI/Anthropic partnerships, but its delivery footprint is smaller than Accenture's or Deloitte's for large-scale integration and long-term run services.
BCG excels at business strategy, innovation, and business-model redesign around AI, but ranks below implementation-led firms because a Fortune 500 transformation may need more systems integration, engineering capacity, and managed services.
BCG combines senior-level strategy and operating-model redesign with product and technical build capabilities, strong for high-value differentiated AI use cases, though its global implementation and managed-services scale trails Accenture's and Deloitte's.
BCG is compelling for value-led AI strategy, portfolio prioritization, and business-model innovation supported by BCG X, but ranks below the top firms because Fortune 500-scale rollouts often need larger global implementation and managed-delivery depth.
Each model’s own references for this brand, grouped by site. Tile area is that site’s share of the model’s references; tap one for the pages behind it.
bcg.com
bcg.com
bcg.com
bcg.com
Sources are what each model recalled as having shaped its view — not verified citations. A model without web access reconstructs a reference from memory, so a link may not lead where the model thought it did.
Deloitte lands in the middle of the pack overall, with a median rank of 3.5 (Q1 3, Q3 4) across 30 ranked answers from 6 models. Model-level placement spans a moderate range: gpt-5.6-sol ranks it highest at a median of 2 (Q1 2, Q3 2), while claude-opus-5, gpt-5.6-luna, and gpt-5.6-terra cluster at a median of 3, and kimi-k3 and gemini-3.7-flash place it lower at medians of 4 and 5 respectively. The models converge on a shared characterization of breadth even as they diverge on how favorably to weigh it.
The dominant theme is Deloitte's end-to-end coverage tying AI to risk, regulatory, cyber, finance, tax, and workforce processes, making it a strong fit for regulated Fortune 500 enterprises—a framing echoed by gpt-5.6-sol, claude-opus-5, gpt-5.6-luna, and gpt-5.6-terra, several of which add the recurring caveat that delivery quality and AI depth vary by practice, member firm, and geography. The lower-ranking models reframe that same breadth as a limitation: kimi-k3 sees the AI work as more generalist and integration-led and less distinctive than MBB strategy houses or Accenture, while gemini-3.7-flash positions Deloitte as an execution and compliance partner rather than a frontier strategy or deployment leader. Sources underpinning these themes lean heavily on Deloitte's own materials—the State of Generative AI in the Enterprise reports, the AI Institute, the Trustworthy AI framework, Tech Trends, and various AI and Data service pages—supplemented by third-party analyst references including Gartner (Magic Quadrant for Data and Analytics Service Providers and the Market Guide for AI Consulting and System Integration Services), the IDC MarketScape, Forrester Wave, and Everest Group PEAK Matrix, with gemini-3.7-flash notably drawing more on Gartner and IDC MarketScape plus outlets such as Bloomberg, the Financial Times, and Fortune.
Deloitte is emphasized for integrating AI with enterprise processes—risk, cyber, finance, tax, regulatory and workforce transformation—making it strong for complex regulated companies, though delivery quality and strategic distinctiveness vary by team and region.
Deloitte is consistently framed as a broad, end-to-end choice combining strategy, technology, risk and regulatory/AI-governance capability (Deloitte AI Institute, Trustworthy AI) well-suited to regulated Fortune 500 industries, with the recurring caveat that delivery quality varies by practice and geography.
Deloitte is portrayed as offering broad multidisciplinary coverage (strategy, risk, cybersecurity, technology, compliance) valuable for highly regulated Fortune 500 firms, with the consistent caveat that experience and AI depth vary by member firm, geography and delivery team.
Deloitte is consistently positioned as a strong fit where AI must be tightly integrated with risk, regulatory, cyber, finance, tax and workforce processes across complex or regulated enterprises, with a recurring recommendation to vet the specific proposed team since experience varies by practice and geography.
Deloitte is credited with enormous breadth, its AI Institute, generative AI research and strength in regulated sectors tying AI to risk/tax/audit, but repeatedly ranked lower because its AI work is perceived as less distinctive and more generalist/integration-led than the MBB strategy houses or Accenture.
Deloitte is described as offering comprehensive breadth in AI governance, regulatory compliance and risk via its Trustworthy AI framework and AI Institute/Academy, ideal for heavily regulated enterprises, but ranked fifth because it is seen as an execution/compliance partner rather than a frontier strategy or deployment leader.
Each model’s own references for this brand, grouped by site. Tile area is that site’s share of the model’s references; tap one for the pages behind it.
www2.deloitte.com
www2.deloitte.com
deloitte.com
deloitte.com
Sources are what each model recalled as having shaped its view — not verified citations. A model without web access reconstructs a reference from memory, so a link may not lead where the model thought it did.
also named IBM
IBM Consulting occupies a consistent mid-tier position across the models, with a median rank of 5 (Q1 5, Q3 5) over 19 ranked answers from 6 models. The agreement here is unusually tight: every one of the six models—claude-opus-5, gemini-3.7-flash, gpt-5.6-luna, gpt-5.6-sol, gpt-5.6-terra, and kimi-k3—landed on a median rank of 5, with no dispersion between the first and third quartiles at either the aggregate or individual level. The firm is referenced under two names, IBM Consulting and IBM.
The thematic consensus is equally strong. Models uniformly credit IBM with technical and engineering depth, hybrid-cloud capability, watsonx assets, data governance, and suitability for regulated and legacy-heavy environments—claude-opus-5 notes competitive pricing, while gpt-5.6-sol and gpt-5.6-terra emphasize fit where IBM or Red Hat is already strategic. The recurring reason for the lower placement is perceived vendor bias toward IBM's own stack and weaker vendor-neutrality, cited by claude-opus-5, gemini-3.7-flash, gpt-5.6-luna, gpt-5.6-sol, and gpt-5.6-terra; claude-opus-5 additionally flags weaker C-suite/board-level strategy influence, and kimi-k3 points to less breadth and large-scale transformation track record than bigger firms. These themes draw on a mix of IBM's own materials—IBM watsonx, IBM Consulting AI Services, the IBM Institute for Business Value, and IBM Annual Report and earnings commentary—alongside third-party analyst sources including Gartner, Forrester, IDC (including the IDC MarketScape), Everest Group PEAK Matrix assessments, and HFS Research.
Consistently frames IBM Consulting as strong on engineering depth, hybrid-cloud, and watsonx assets with competitive pricing, but ranks it lower due to perceived bias toward IBM's own stack and weaker C-suite/board-level strategy influence.
Emphasizes deep hybrid-cloud and technical engineering strength for legacy modernization in regulated environments, while flagging potential vendor bias toward IBM's own platform ecosystem.
Presents IBM as credible for hybrid cloud, data governance, and regulated enterprise environments, but ranks it lower because its fit is strongest for clients already aligned with IBM's ecosystem rather than those seeking vendor-neutral strategy.
Positions IBM as strong for technically complex, hybrid-cloud, regulated, and legacy-heavy transformations, ranking it lower because its advantages depend on architecture aligning with IBM/Red Hat and it is seen as less vendor-neutral.
Describes IBM as credible for hybrid-cloud, data-platform, and legacy modernization needs, especially where IBM is already strategic, but ranks it fifth for being more platform-centric and less vendor-neutral.
Recognizes IBM's watsonx heritage and legacy-modernization strength, but ranks it fifth because its brand is tied to IBM's own stack and it lacks the breadth and large-scale transformation track record of bigger firms.
Each model’s own references for this brand, grouped by site. Tile area is that site’s share of the model’s references; tap one for the pages behind it.
ibm.com
ibm.com
ibm.com
ibm.com
ibm.com
Sources are what each model recalled as having shaped its view — not verified citations. A model without web access reconstructs a reference from memory, so a link may not lead where the model thought it did.
where
where
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