# Comprehensive.io vs Mercer Total Remuneration Survey: which do AI models recommend for compensation benchmarkin, October 2026

HR AI Recommendation Index, October 2026 Edition, Compensation benchmarking data. Zero of fourteen models named Comprehensive.io first on the direct prompt; zero named Mercer Total Remuneration Survey. Page: https://hr-ai-index.com/rewards/compensation-benchmarking-data/comprehensive-io-vs-mercer-total-remuneration-survey/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Comprehensive.io | 7% | #6 of 13 | 0% | 10 | 10 of 14 |
| Mercer Total Remuneration Survey | 3% | #8 of 13 | 52% | 33 | 14 of 14 |

## The direct prompt, model by model

- GPT-5.4 mini: neither first, one named (first choices: Payscale) (alternatives: Mercer Total Remuneration Survey, Radford, WTW)
- GPT-6 Luna: neither first, one named (first choices: Pave) (alternatives: Mercer Total Remuneration Survey, Payscale, Radford)
- Claude Haiku 4.5: neither named (first choices: Payscale) (alternatives: Comprehensive, Lattice, Pave, SalaryCube)
- Gemini 3.5 Flash: neither named (first choices: Pave) (alternatives: Figures, Payscale, Ravio, RepVue, Salary.com CompAnalyst)
- Perplexity Sonar: neither named (first choices: Ravio) (alternatives: CompUp, Pave, Payfactors, Payscale Ascent, Salary.com CompAnalyst)
- Grok 4.1 Fast: neither named (first choices: SalaryCube) (alternatives: Payscale, Salary.com CompAnalyst)
- Mistral Small: neither named (first choices: Ravio, SalaryCube) (alternatives: CompUp, Figures)
- DeepSeek V4 Flash: neither named (first choices: Payscale) (alternatives: Pave, Salary.com CompAnalyst)
- Llama 4 Maverick: neither named
- Qwen 3.7 Flash: neither named (first choices: Lattice) (alternatives: Comprehensive, Deel HR, Pave, SalaryCube)
- Kimi K2: neither named (first choices: Payscale, SalaryCube) (alternatives: Figures, Pave, Ravio, Salary.com CompAnalyst)
- GLM 4.7 FlashX: neither named (first choices: Pave, Payscale Ascent) (alternatives: Figures, Salary.com CompAnalyst)
- MiniMax M2.5: neither named (first choices: SalaryCube) (alternatives: CompUp, Lattice, Salary.com CompAnalyst)
- Muse Glimmer 30B: neither named (first choices: SalaryCube) (alternatives: Pave, Salary.com CompAnalyst)

## What the models said about Comprehensive.io

- "starting with free tools like Pave's free tier, Comprehensive.io, or the BLS is recommended" (Claude Haiku 4.5, budget prompt, first choice)
- "use Pave, Comprehensive.io, or Ravio (free tiers) for deeper, role‑specific insights" (GLM 4.7 FlashX, budget prompt, first choice)
- "Pave Market Data Lite (free) or Comprehensive.io (free)" (DeepSeek V4 Flash, budget prompt, first choice)

## What the models said about Mercer Total Remuneration Survey

- "Avoid: Traditional survey providers like Mercer, Korn Ferry, or Willis Towers Watson" (GLM 4.7 FlashX, paraphrase prompt, hard negative)
- "You typically want to avoid enterprise-level firms (like Mercer or Willis Towers Watson)" (Qwen 3.7 Flash, budget prompt, hard negative)
- ""Why you should avoid traditional providers (Mercer, Radford, Willis Towers Watson)"" (Muse Glimmer 30B, negative prompt, hard negative)
- "The best compensation survey data for a mid-sized B2B company would be provided by companies like Mercer and Payscale, which are comprehensive for mid-to-large-sized companies." (Llama 4 Maverick, paraphrase prompt, first choice)
- "Mercer and WTW, by contrast, collect employer-submitted, verified payroll data directly from HR information systems (HRIS)... considered the definitive "source of truth"" (Gemini 3.5 Flash, comparative prompt, first choice)
- "The global leader in human capital and compensation benchmarking... widely considered the gold standard for salary, benefits" (Qwen 3.7 Flash, comparative prompt, first choice)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.