HR AI Index
Index Compensation and total rewards Compensation benchmarkin › Mercer vs Salary.com
Compensation benchmarking data · September 2026 Edition

Mercer vs Salary.com

Zero of twelve models named Mercer first on the direct prompt; zero named Salary.com. Mercer was named by ten of the twelve models and Salary.com by nine and Mercer carries 28 labels and Salary.com 16, so the shares are not directly comparable.

Mercer

criticized challenger

Named in four categories this edition.

Salary.com

accepted challenger

Named in three categories this edition.

First-choice share6%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate43%6%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#6A position in a field of 8; printed, not drawn.
Labels2816A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Mercer reading right to left. Rank and label count are printed, not drawn.Payscale was named alongside these two in ten of the twelve direct answers. Payscale vs Mercer · Payscale vs Salary.com · Pave vs Mercer

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the compensation benchmarking data page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
MercerFirst choices, of twelve modelsSalary.com
Direct005 against Mercer
Paraphrase312 against Mercer · 1 against Salary.com
Comparative20
Budget-constrained004 against Mercer
Scale-constrained001 against Mercer
Negative00
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Mercer and Salary.com were named in the same answer forty-two times, of the 109 answers naming Mercer and the 141 naming Salary.com. In those answers Salary.com took the first choice two times and Mercer five.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Mercer and Salary.com stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
Mercer Salary.com first choice named as an alternative argued againstblank: not namedEach cell is one answer, Mercer on the left and Salary.com on the right.

The direct prompt

The plain question, one answer per model, grouped by where Mercer and Salary.com stood in it.

Neither was the first choice, one was named

8 of 12 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniPayscale alternatives: Radford, Salary.com
Gemini 3.5 FlashPave alternatives: Carta Total Comp, Figures, Payscale, Radford, Ravio, Salary.com
Grok 4.1 FastPayscale alternatives: Pave, Salary.com
Mistral SmallSalaryCube alternatives: Pave, Payfactors, Salary.com
DeepSeek V4 FlashPayscale alternatives: ERI, Pave, Ravio, Salary.com
Qwen 3.7 FlashPayscale alternatives: Pave, Payfactors, Salary.com, SalaryCube
Kimi K2Payscale, SalaryCube alternatives: Mercer, Radford, Salary.com
GLM 4.7 FlashXPave alternatives: Aon Radford, CaptivateIQ, Ficstar, Mercer, OpenComp, Payscale Ascent, Ravio, Salary.com CompAnalyst

Neither was named

4 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Payscale alternatives: Lattice, SalaryCube, beqom
Perplexity SonarPayscale alternatives: Ravio, SalaryCube
Llama 4 MaverickPayscale
MiniMax M2.5Payscale alternatives: Comprehensive.io

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Salary.com leads by six points.
Salary.com6%#3 of 10
Mercer0%#9 of 10
The full small business standing →
Mid-marketThe figures above
The order flips: Mercer leads at mid-market.
Mercer6%#4 of 8
Salary.com2%#6 of 8
The full mid-market standing →
Enterprise
Mercer leads by twenty-three points.
Mercer26%#1 of 10
Salary.com3%#6 of 10
The full enterprise standing →

What the models said about Mercer

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.

“Avoid over-investing in Mercer/WTW unless you have global complexity” Kimi K2 · paraphrase prompt · hard negative
“Avoid traditional surveys (Mercer/Radford: $10k+/year) unless scaling.” Grok 4.1 Fast · budget prompt · hard negative
“high-end, legacy enterprise surveys like Mercer, Radford (Aon), or Willis Towers Watson (WTW)—which can easily cost $20,000 to $40,000+ annually—are generally out of reach” Gemini 3.5 Flash · budget prompt · soft negative
“Buy one major survey (like Robert Half or Mercer) to ensure you are paying within the 25th–75th percentiles” Qwen 3.7 Flash · paraphrase prompt · first choice
“The strongest fit from the results is Mercer or Salary.com/Compdata for broad benchmarking” Perplexity Sonar · paraphrase prompt · first choice
“Especially strong in pay and total rewards benchmarking, plus HR strategy.” GPT-5.4 mini · comparative prompt · first choice

What the models said about Salary.com

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.

“Best all-around for mid-sized companies: Compdata Max / Salary.com” Perplexity Sonar · paraphrase prompt · first choice
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.