Index › HR › Compensation and total rewards › Compensation benchmarkin › Glassdoor vs Willis Towers Watson
Compensation benchmarking data · November 2026 Edition
Glassdoor vs Willis Towers Watson
Zero of fourteen models named Glassdoor first on the direct prompt; zero named Willis Towers Watson. Glassdoor was named by ten of the fourteen models and Willis Towers Watson by eight and Glassdoor carries 16 labels and Willis Towers Watson 13, so the shares are not directly comparable.
Named in four categories this edition.
Willis Towers Watson
criticized challengerNamed in one category this edition.
First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate38%46%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#8A position in a field of 9; printed, not drawn.
Labels1613A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Glassdoor 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 Glassdoor · Payscale vs Willis Towers Watson · Pave vs Glassdoor
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.
GlassdoorFirst choices, of fourteen modelsWillis Towers WatsonDirect001 against Glassdoor · 1 against Willis Towers Watson
Paraphrase011 against Willis Towers Watson
Comparative01
Budget-constrained201 against Willis Towers Watson
Scale-constrained004 against Glassdoor
Negative001 against Glassdoor · 3 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 Glassdoor and Willis Towers Watson stood in it.
| Model | DirectWT | ParaphraseWT | ComparativeWT | Budget-constrainedWT | Scale-constrainedWT | NegativeWT |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | WT | |||||
| Gemini 3.5 Flash | WT | WT | | |||
| Perplexity Sonar | WT | |||||
| Grok 4.20 | WT | WT | | |||
| Mistral Small | ||||||
| DeepSeek V4 Flash | | WT | ||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | | | ||||
| Kimi K2 | WT | | ||||
| GLM 4.7 FlashX | | |||||
| MiniMax M2.5 | | |||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B | WT |
GlassdoorWT Willis Towers Watson first choice named as an alternative argued againstblank: not namedEach cell is one answer, Glassdoor on the left and Willis Towers Watson on the right.The direct prompt
The plain question, one answer per model, grouped by where Glassdoor and Willis Towers Watson stood in it.
Neither was named
14 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
Gemini 3.5 FlashPave alternatives: Carta Total Compensation, Payscale, Salary.com CompAnalyst
Perplexity SonarPayscale alternatives: Pave, Ravio, SalaryCube
Grok 4.20Payscale alternatives: Pave, Ravio, Salary.com CompAnalyst
Mistral SmallPave alternatives: Mercer Total Remuneration Survey, Payscale
DeepSeek V4 FlashPayscale alternatives: Mercer Benchmark Database, Pave, Ravio, SalaryCube
Llama 4 MaverickSalaryCube alternatives: Mercer Total Remuneration Survey, Pave, Payscale
Qwen 3.7 FlashComprehensive.io alternatives: Figures, Pave, Payscale, Salary.com CompAnalyst
Kimi K2Payscale alternatives: Mercer Total Remuneration Survey, Pave, Radford, Salary.com CompAnalyst
GLM 4.7 FlashXFigures, SalaryCube Comp Planning alternatives: Pave, 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
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
Glassdoor leads by three points.
Glassdoor3%#5 of 10Willis Towers Watson0%#– of 10
The full small business standing →Mid-marketThe figures above
Glassdoor leads by two points.
Glassdoor4%#5 of 9Willis Towers Watson2%#8 of 9
The full mid-market standing →Enterprise
The order flips: Willis Towers Watson leads at enterprise.
Willis Towers Watson5%#6 of 9
Glassdoor0%#– of 9What the models said about Glassdoor
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Avoid those relying heavily on unverified crowdsourced data (e.g., from Glassdoor or Levels.fyi), as it can be biased or inflated.” Grok 4.20 · scale prompt · hard negative
“you're too large for free/consumer tools (Glassdoor, BLS) — those won't have the granularity or credibility you need” DeepSeek V4 Flash · scale prompt · hard negative
“start with BLS + Glassdoor combined. This combination provides accurate, defensible data for most common positions at zero cost.” MiniMax M2.5 · budget prompt · first choice
“start with free tools (BLS OEWS + Glassdoor) to get a solid baseline” GLM 4.7 FlashX · budget prompt · first choice
“Best For: Quick market checks, especially for office jobs and white-collar roles.” Qwen 3.7 Flash · budget prompt · alternative
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
Payscale vs GlassdoorPayscale vs Willis Towers WatsonPave vs GlassdoorPave vs Willis Towers WatsonComprehensive.io vs GlassdoorComprehensive.io vs Willis Towers WatsonSalary.com CompAnalyst vs GlassdoorSalary.com CompAnalyst vs Willis Towers WatsonGlassdoor vs Mercer Total Remuneration SurveyGlassdoor vs RadfordMercer Total Remuneration Survey vs Willis Towers WatsonRadford vs Willis Towers WatsonAll twenty-eight head-to-head pages and the full compensation benchmarkin standing
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.