# Salary.com CompAnalyst vs Radford: 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 Salary.com CompAnalyst first on the direct prompt; zero named Radford. Page: https://hr-ai-index.com/rewards/compensation-benchmarking-data/salary-com-companalyst-vs-radford/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Salary.com CompAnalyst | 9% | #4 of 13 | 4% | 24 | 12 of 14 |
| Radford | 5% | #7 of 13 | 50% | 28 | 12 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)
- Gemini 3.5 Flash: neither first, one named (first choices: Pave) (alternatives: Figures, Payscale, Ravio, RepVue, Salary.com CompAnalyst)
- Perplexity Sonar: neither first, one named (first choices: Ravio) (alternatives: CompUp, Pave, Payfactors, Payscale Ascent, Salary.com CompAnalyst)
- Grok 4.1 Fast: neither first, one named (first choices: SalaryCube) (alternatives: Payscale, Salary.com CompAnalyst)
- DeepSeek V4 Flash: neither first, one named (first choices: Payscale) (alternatives: Pave, Salary.com CompAnalyst)
- Kimi K2: neither first, one named (first choices: Payscale, SalaryCube) (alternatives: Figures, Pave, Ravio, Salary.com CompAnalyst)
- GLM 4.7 FlashX: neither first, one named (first choices: Pave, Payscale Ascent) (alternatives: Figures, Salary.com CompAnalyst)
- MiniMax M2.5: neither first, one named (first choices: SalaryCube) (alternatives: CompUp, Lattice, Salary.com CompAnalyst)
- GPT-6 Luna: neither first, one named (first choices: Pave) (alternatives: Mercer Total Remuneration Survey, Payscale, Radford)
- Muse Glimmer 30B: neither first, one named (first choices: SalaryCube) (alternatives: Pave, Salary.com CompAnalyst)
- Claude Haiku 4.5: neither named (first choices: Payscale) (alternatives: Comprehensive, Lattice, Pave, SalaryCube)
- Mistral Small: neither named (first choices: Ravio, SalaryCube) (alternatives: CompUp, Figures)
- Llama 4 Maverick: neither named
- Qwen 3.7 Flash: neither named (first choices: Lattice) (alternatives: Comprehensive, Deel HR, Pave, SalaryCube)

## What the models said about Salary.com CompAnalyst

- "Avoid expensive enterprise solutions like PayScale (~$12,000+/year) or Salary.com" (MiniMax M2.5, budget prompt, hard negative)
- "Best all-around validated survey for mid-size: Compdata Max / Salary.com. Explicitly called out as best all-around for mid-sized companies." (Muse Glimmer 30B, paraphrase prompt, first choice)
- "Best Balance of Depth & Accessibility ... Salary.com or Mercer will give you the broad industry coverage you need" (Kimi K2, paraphrase prompt, first choice)

## What the models said about Radford

- ""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)

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.