Zero of fourteen models named Radford first on the direct prompt; zero named Mercer Total Remuneration Survey. Radford was named by twelve of the fourteen models and Mercer Total Remuneration Survey by fourteen and Radford carries 28 labels and Mercer Total Remuneration Survey 33, so the shares are not directly comparable.
Named in three categories this edition.
Named in one category this edition.
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.
Across every category in the October 2026 Edition, Radford and Mercer Total Remuneration Survey were named in the same answer seventy-nine times, of the 97 answers naming Radford and the 118 naming Mercer Total Remuneration Survey. In those answers Mercer Total Remuneration Survey took the first choice nine times and Radford five.
| Model | DirectRA | ParaphraseRA | ComparativeRA | Budget-constrainedRA | Scale-constrainedRA | NegativeRA |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | RA | RA | ||||
| Gemini 3.5 Flash | RA | RA | RA | |||
| Perplexity Sonar | RA | |||||
| Grok 4.1 Fast | RA | |||||
| Mistral Small | RA | RA | ||||
| DeepSeek V4 Flash | RA | RA | RA | |||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | RA | RA | RA | RA | ||
| Kimi K2 | RA | RA | ||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | RA | |||||
| GPT-6 Luna | RA | |||||
| Muse Glimmer 30B | RA |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of six in this category shown.
“"Why you should avoid traditional providers (Mercer, Radford, Willis Towers Watson)"” Muse Glimmer 30B · negative prompt · hard negative
“incredibly robust but requires manual survey participation, a higher budget, and more administrative effort than modern API-based systems” Gemini 3.5 Flash · paraphrase prompt · soft negative
“$15K–$80K+/yr with mandatory survey participation. Overkill for most mid-market B2B unless you're in tech/life sciences specifically.” DeepSeek V4 Flash · direct prompt · soft negative
“This makes Radford a top pick for companies looking for reliable, free salary benchmarking data without a significant financial investment.” Mistral Small · budget prompt · first choice
“Radford or Mercer are the top choices due to their industry-specific data and reliability” Mistral Small · paraphrase prompt · first choice
“1. Radford (Aon) — *Best for B2B Tech/SaaS*” Kimi K2 · paraphrase prompt · first choice
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: 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
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.