Index › HR › Compensation and total rewards › Compensation benchmarkin › Payscale vs Willis Towers Watson
Compensation benchmarking data · November 2026 Edition

Payscale vs Willis Towers Watson

Seven of fourteen models named Payscale first on the direct prompt; zero named Willis Towers Watson. Payscale was named by fourteen of the fourteen models and Willis Towers Watson by eight and Payscale carries 32 labels and Willis Towers Watson 13, so the shares are not directly comparable.

Payscale

accepted challenger

Named in four categories this edition.

Willis Towers Watson

criticized challenger

Named in one category this edition.

First-choice share18%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate9%46%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#8A position in a field of 9; printed, not drawn.
Labels3213A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Payscale reading right to left. Rank and label count are printed, not drawn.Pave was named alongside these two in fourteen of the fourteen direct answers. Payscale vs Pave · Payscale vs Comprehensive.io · Payscale vs Salary.com CompAnalyst

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen 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 fourteen models made each the first choice, per way of asking, and how many argued against it.
PayscaleFirst choices, of fourteen modelsWillis Towers Watson
Direct701 against Willis Towers Watson
Paraphrase311 against Willis Towers Watson
Comparative01
Budget-constrained002 against Payscale · 1 against Willis Towers Watson
Scale-constrained001 against Payscale
Negative003 against Willis Towers Watson
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Payscale and Willis Towers Watson stood in it.
ModelDirectWTParaphraseWTComparativeWTBudget-constrainedWTScale-constrainedWTNegativeWT
Claude Haiku 4.5
GPT-5.4 miniWT
Gemini 3.5 FlashWTWT
Perplexity SonarWT
Grok 4.20WTWT
Mistral Small
DeepSeek V4 FlashWT
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2WT
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30BWT
PayscaleWT Willis Towers Watson first choice named as an alternative argued againstblank: not namedEach cell is one answer, Payscale on the left and Willis Towers Watson on the right.

The direct prompt

The plain question, one answer per model, grouped by where Payscale and Willis Towers Watson stood in it.

Payscale first, Willis Towers Watson not the choice

7 of 14 modelsWillis Towers Watson was named in the answer but not as the choice, or not at all.
Perplexity SonarPayscale alternatives: Pave, Ravio, SalaryCube
Grok 4.20Payscale alternatives: Pave, Ravio, Salary.com CompAnalyst
DeepSeek V4 FlashPayscale alternatives: Mercer Benchmark Database, Pave, Ravio, SalaryCube
Kimi K2Payscale alternatives: Mercer Total Remuneration Survey, Pave, Radford, Salary.com CompAnalyst
MiniMax M2.5CompAnalyst, Payscale alternatives: Pave
GPT-6 LunaPayscale alternatives: Pave
Muse Glimmer 30BPayscale alternatives: CompAnalyst, Comprehensive.io, Pave, Salary.com CompAnalyst, SalaryCube

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashPave alternatives: Carta Total Compensation, Payscale, Salary.com CompAnalyst
Mistral SmallPave alternatives: Mercer Total Remuneration Survey, Payscale
Llama 4 MaverickSalaryCube alternatives: Mercer Total Remuneration Survey, Pave, Payscale
Qwen 3.7 FlashComprehensive.io alternatives: Figures, Pave, Payscale, Salary.com CompAnalyst

Neither was named

3 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Pave, SalaryCube alternatives: CompUp, OpenComp, Payscale Ascent
GPT-5.4 miniSalary.com CompAnalyst alternatives: Compensation-IQ, Pave, Payscale Ascent
GLM 4.7 FlashXFigures, SalaryCube Comp Planning alternatives: Pave, Salary.com CompAnalyst

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
Payscale leads by twelve points.
Payscale12%#2 of 10
Willis Towers Watson0%#– of 10
The full small business standing →
Mid-marketThe figures above
Payscale leads by sixteen points.
Payscale18%#1 of 9
Willis Towers Watson2%#8 of 9
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Payscale5%#5 of 9
Willis Towers Watson5%#6 of 9
The full enterprise standing →

What the models said about Payscale

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

“Data self-reported by employees (e.g., Glassdoor, Payscale). This is often inflated, noisy, and difficult to verify” Gemini 3.5 Flash · scale prompt · soft negative
“Best for: Growing companies ready to invest in compensation infrastructure” MiniMax M2.5 · budget prompt · soft negative
“full benchmarking requires subscriptions (avoid if budget is tight)” Grok 4.20 · budget prompt · soft negative
“Payscale for broad mid-market coverage ... Payscale Knowledge / Payscale. Real-time data with survey sourcing and broad job family coverage.” Muse Glimmer 30B · direct prompt · first choice
“Most mid-market companies find that Payscale strikes the best balance of cost, coverage, and functionality” Kimi K2 · direct prompt · first choice
“Payscale (or Payscale + CompAnalyst) – Strongest overall fit for most mid-sized B2B companies” Grok 4.20 · paraphrase prompt · first choice

What the models said about Willis Towers Watson

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

“explicitly advise skipping these providers unless you have a significant budget and need deep integration capabilities” Muse Glimmer 30B · negative prompt · hard negative
“avoid traditional enterprise survey providers (like Radford/Aon, Mercer, or Willis Towers Watson” Gemini 3.5 Flash · budget prompt · hard negative
“Avoid for Mid-Market: Radford (Aon), Mercer, or Willis Towers Watson” Grok 4.20 · direct prompt · hard negative
“Need HR, compensation, or talent data? | Mercer, Korn Ferry, Aon/WTW” Kimi K2 · comparative prompt · first choice
“Best all-around: Mercer or WTW” GPT-5.4 mini · paraphrase prompt · first choice
“Highly trusted by board compensation committees and HR executives worldwide.” Gemini 3.5 Flash · paraphrase prompt · alternative
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.