4 AI Assistants, 11 Executive and Consulting Cost Questions

Answers from four API configurations of AI assistants, collected on October 8, 2026 (11:49 to 12:06 UTC), with Google AI Overviews pulled the same day. Each configuration was asked 11 cost questions that Google shows under People Also Ask, ten times each.

The author sells fractional COO, fractional CMO, post-merger advisory, and strategy services. This article links to the author’s fractional COO, fractional CMO, and strategy service pages, which may benefit from search traffic it earns. The author’s own domain appears among the references presented by Perplexity and Google’s AI Overviews for some of these questions (counts in the references section). This analysis does not test what those services cost or are worth. The question set, the calls, and the analysis rules were fixed before the main data pull. Five post-collection changes are listed under Method: a parser fix, a calibration check coded by the study’s AI producer instead of a person, one preregistered check that was not met as written, a rounding implementation difference that changed no figure, and the title confirmation.

These are AI-generated statements, not market prices. The study does not test whether any answer is accurate.

The questions and the configurations

The 11 questions are People Also Ask cost questions found on Google results for fractional CMO, fractional COO, and business consultant searches. A fixed selection rule picked them before any assistant was called. The count column shows how many of 124 Google result pages, captured October 7, 2026, listed the question under People Also Ask. The page-level selection file lists all 124 pages and which selected questions each one showed. The rule kept price questions seen on at least two pages, or on one page if they named the COO or CMO role, after the exclusions listed on the protocol page.

Each question went to four API configurations, ten times each, with no researcher-added system message and with the providers’ default settings.

Every call returned the requested model ID, and the returned IDs are in the data package. That made 440 API answers. Add 33 Google AI Overview pulls, three per question, and the total is 473 records, with no record missing or discarded (two AI Overview pulls returned no overview, as reported below). A fifth configuration was dropped before collection, because its provider’s terms do not allow the study to name it.

API configurations are not the consumer chat apps. The apps can add instructions, memory, search, and settings that these calls did not have.

How the answers were compared

From each answer, the parser took the first price range it gave, along with that range’s unit (per hour, per month, or per year) and its midpoint. If a range ended in “+”, such as “$10,000+ per month”, it has no upper figure, so it got no midpoint.

For each question, the modal unit is the unit most answers led with. A configuration was eligible on a question if at least 8 of its 10 answers gave a closed range in that unit. Its median midpoint is the median of those midpoints.

Where three or more configurations were eligible, the cross-configuration ratio is the highest eligible median divided by the lowest.

For the repeated-call analysis only, a cell is any configuration and unit with at least 8 closed ranges among its 10 calls, whether or not that unit is the question’s modal unit.

Results by question

AI-generated statements collected through an API on Oct 8, 2026; not market prices.

The full exclusion counts for every question and configuration are in the appendix.

Six questions had a ratio. Five of the six were at 1.5× or more. The other five questions had too few eligible configurations to compute a ratio, so they carry no headline.

Where the configurations differed most

Fractional CMO per month (Q07). Three configurations were eligible. Their medians were $4,125 (Anthropic), $8,500 (Google), and $12,500 (Perplexity), a ratio of 3.03×. OpenAI’s answers were excluded: 8 of its 10 ended their first range with “+”. The data does not show why.

Fractional CMO, no unit stated (Q04). Medians were $4,500 (Anthropic), $9,000 (Perplexity), and $10,000 (Google), a ratio of 2.22×. OpenAI led with an hourly range in 6 of 10 answers.

Business consultant (Q02 and Q05). All four configurations were eligible on both questions. Anthropic gave the lowest hourly medians, $75 and $100. Google gave the highest, $200 and $225. The ratios were 2.67× and 2.25×. These were the only two questions where all four configurations were eligible.

Fractional CMO per hour (Q01). Medians were $275 to $325, a ratio of 1.18×. This was the only computed ratio below 1.5×.

Fractional COO (Q10). Medians were $8,500 (Google), $10,000 (OpenAI), and $13,000 (Perplexity), a ratio of 1.53×. Anthropic led with an hourly range in 8 of 10 answers.

Asking the same question ten times

Repeated calls with the same question did not always return the same closed range.

In 21 of 27 eligible configuration-unit cells, the closed ranges available from ten calls had a midpoint max/min ratio of at least 1.25, or, where a unique modal normalized range existed, at least 30% of the normalized ranges differed from it. For this test, each range’s endpoints were rounded to the nearest $10 below $1,000 and to the nearest $100 at or above $1,000, so small formatting differences do not create different ranges. The rounding grid changes at $1,000. Under the preset sensitivity check with a single $100 grid, the count is 19 of 27. Two of the 27 cells, Google on Q07 and Google on Q08, had no single most common range. Both count on the max/min test, at 1.33 and 2.50. One of the 27 cells is Perplexity on Q03, which qualified in the monthly unit rather than the question’s modal annual unit.

The last row is the narrowest eligible cell, included for contrast.

The unit changes the answer

The same question did not always get the same unit.

• On Q08 (“How much does a fractional COO charge?”), OpenAI led with an hourly range in all 10 answers, while Google and Perplexity led with monthly ranges.
• On Q10, Anthropic led with an hourly range in 8 of 10 answers.
• On Q03 (“How much does a CMO cost?”), OpenAI, Google, and Anthropic mostly led with annual-unit ranges. Perplexity led with a monthly range in all 10 answers.

Q03 and Q09 do not say “fractional”. Their annual-unit answers are reported separately here and are never treated as fractional pricing.

Open-ended ranges

The share of answers whose first range ended in “+” differed by configuration.

Open-ended ranges have no midpoint under the fixed rules, which is why OpenAI was excluded so often.

A check set before the data pull counted open-ended ranges at their stated figures. With that change, Q06 (2.38×) and Q11 (1.52×) become computable. The six computed ratios above are unchanged. The check also adds OpenAI and Perplexity medians on several questions, for example OpenAI $9,000 on Q07 and Perplexity $275,000 on Q09. Under this check, the count of questions at 1.5× or more would rise from five to seven (Q06 at 2.38× and Q11 at 1.52× become computable). These figures are a sensitivity result, not a finding.

What Google’s AI Overviews showed

AI Overviews appeared in 31 of 33 pulls. The first range and unit from each were recorded. The table shows the six questions with a computed ratio. These are descriptive and are not compared statistically.

References presented

The study recorded and analyzed reference domains only for Perplexity and AI Overviews. The study counted the domains each one listed. It did not read or grade the pages.

Perplexity. On 9 of 11 questions, every reference domain that appeared in any run appeared in all 10 runs of that question. On Q01, salary.com appeared in 7 runs and chatterbuzzmedia.com in 3. On Q08, eca-partners.com appeared in 7 runs. Across all questions, the most frequent domains were mentorcruise.com and fractionus.com, at 90 runs each, followed by linkedin.com at 70.

AI Overviews. Across 31 pulls, the most frequent reference domains were gofractional.com (21), linkedin.com (19), fractionus.com (18), graystoneconsulting.co (11), rankedcmo.com (11), fractionalcxo.to (10), and matthewdeal.com (10).

The author’s domain. The domain kamyarshah.com appeared in 7 of 31 AI Overview pulls (on Q02, Q05, Q08, and Q11) and in all 40 Perplexity runs of Q08 to Q11. This is the author’s own site, as stated in the disclosure at the top. A reference count shows what was listed, not what the page said or whether the answer relied on it.

What the numbers mean (operating judgment)

An AI price answer is a starting point for further research, not a market price or a provider quote. In this data, the answer depended on which configuration was asked, which unit it chose, and which of ten calls it was. A buyer who asks once receives one draw from that configuration’s possible responses. In 21 of the 27 eligible cells, the repeated answers met at least one of the study’s preset variability thresholds.

As operating judgment, buyers should compare scope, expected hours, and responsibilities rather than relying on the title alone. A fractional CMO at two days a month and one at two days a week are different purchases. A single number hides that difference.

How to handle it (operating judgment)

Buyers should define the outcomes and hours before comparing any quotes. One possible operating template is a Balanced Scorecard with two or three selected measures in each of four perspectives: financial, customer, internal process, and learning and growth. A scope worksheet then fixes days per month, decision rights, and reporting.

For example, a hypothetical $30M services firm might compare three fractional CMO proposals. If each proposal is priced against the same scorecard and the same days per month, the firm compares like with like. If it starts from an AI estimate, it compares a range nobody scoped.

Buyers asking an assistant should ask it to state the unit, the scope, and the source behind the figure.

What this data does not tell you

• These are not market prices, and they are not what any provider charges.
• The study does not test accuracy.
• The calls went to API configurations, not consumer chat apps.
• The 440 API answers came from one collection window, about 17 minutes on October 8, 2026. The 33 Google AI Overview pulls were made the same day.
• Only four configurations were tested, all at default settings.
• A reference count does not show what the cited page says.

Questions about this analysis can be sent through the contact page.

Method

Selection and collection. A fixed rule selected the 11 questions from People Also Ask data retrieved through a licensed search-data provider. The question manifest, call schedule, and code were archived before the main data pull. All 440 calls were shuffled into one sequence with a fixed random seed and made through an API router, pinned to each model’s own provider, with fallbacks disabled.

Extraction and audit. A rule-based parser extracted the first range, unit, currency, and open-ended flag from each answer. Two auditor models from families not under test coded a random sample of 100 answers independently, and a third model arbitrated disagreements. The auditor models are not named, under the site’s rule against naming tools.

• The parser matched the arbitrated audit on all fields in 100 of 100 sampled answers (95% lower bound 0.963).
• Auditor agreement on the unit was κ 0.984.
• The parser matched 45 of 45 answers in a preset check set.
• AI Overview extraction matched the audited coding on 29 of the 31 pulls that returned an AI Overview. The two differences were the third pulls for Q03 and Q09, both on questions without a computed ratio. Neither pull appears in the AI Overview table, and reference domains were checked separately.
• Reference extraction was correct on 20 of 20 checked.

Changes after collection, each dated and archived:

• Calibration coder. The protocol called for a person to code 30 answers as a calibration check. At the author’s instruction, the study’s AI producer (Claude, made by Anthropic, whose model is one of the four tested) coded them instead. The parser matched on 29 of 30. This is not an independent human check.
• Parser fix. Five of 440 answers wrote dollar signs as `\$`, and the parser missed their upper figure. All three audit models had coded these five as ranges. The fix was applied and everything was re-run. No headline figure changed. Eligible cells rose from 26 to 27, and cells with notable within-cell spread rose from 20 to 21.
• One kill rule not met as written. One preregistered kill rule required auditor agreement (κ) of at least 0.60 on whether an answer contained a price range. Across the 100-answer random sample and 15 additional audited diagnostic records, both auditors marked every answer as containing a range, for raw agreement of 115 of 115. Because there was no variation in that classification, κ could not be computed. The kill rule therefore was not met as written. After seeing the data, the producer decided that the study would proceed and recorded that decision as a protocol deviation.
• Rounding implementation. The preset endpoint-rounding rule used half-up rounding, but the analysis code rounded ties to the even value. Fifteen records had tie endpoints. All 27 eligible cells were checked with half-up rounding. No cell classification or reported figure changed, and the count stayed at 21.
• Title. The title was confirmed after results were available. It is a shortened form of the title set before collection, cut to fit the site’s 70-character limit, and makes no new claim.

The public data package is a CSV of all 473 normalized records. Each record has the configuration, question, requested and returned model ID, lead range, unit, midpoint, reference domains, and a hash of the answer text. It does not include raw answer text. Because raw answer text is not public, outside readers can recompute the reported summaries from the normalized records but cannot independently verify the extraction from the public package alone. Exclusion counts are in the appendix below. The question-selection rule and a summary of every amendment are on the protocol page. The protocol is summarized on the public protocol page.

Companion analyses from the same data:

• COO Cost Questions: How 4 AI Assistants Answered
• Business Consultant Fees: How 4 AI Assistants Answered
• Which Sites AI Tools List for Executive and Consulting Cost Questions
• The Same AI Price Question, Asked 10 Times

Appendix: all 27 eligible cells

Each row is one configuration and unit with at least 8 closed ranges among its 10 calls. The last column is the share of closed ranges that differed from the cell’s most common range, after rounding endpoints to the nearest $10 below $1,000 and the nearest $100 at or above. The rounding grid changes at $1,000. A single $100-grid sensitivity is reported in the repeated-calls section.

Appendix: exclusion counts per question

Each cell shows mutually exclusive dispositions out of 10, as valid / other unit / open-ended in the modal unit. An answer that was both in another unit and open-ended is counted under other unit. The configuration-level percentages in the open-ended section count every first range ending in “+”, regardless of unit, so they cannot be obtained by summing the open-ended column here.

“Other unit” includes answers with no stated unit. Perplexity’s 10 “other unit” answers on Q03 were monthly ranges. Nine of them were closed, so they form an eligible within-configuration cell in the monthly unit. That cell counts toward the 27 cells in the repeated-calls section, but not toward any cross-configuration ratio.

Google did not review or endorse this analysis. OpenAI, Google, Anthropic, and Perplexity did not review or endorse it. Model names are used only to identify the configurations tested.

The author sells fractional COO, fractional CMO, post-merger advisory, and strategy services. This analysis does not test what those services cost or are worth.

Kamyar Shah Fractional COO, Fractional CMO & Business Consultant

Fractional COO, Fractional CMO, and Executive Coach, Kamyar Shah, founder of World Consulting Group with over 25 years of experience helping organizations achieve operational excellence and sustainable growth. He has led 650+ consulting engagements producing more than $300M+ in measurable results. Kamyar contributes regularly to KamyarShah.com and Coruzant.

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