# Payscale vs Radford: which do AI models recommend for compensation benchmarkin, September 2026

HR AI Recommendation Index, September 2026 Edition, Compensation benchmarking data. Nine of twelve models named Payscale first on the direct prompt; zero named Radford. Page: https://hr-ai-index.com/rewards/compensation-benchmarking-data/payscale-vs-radford/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Payscale | 24% | #1 of 8 | 21% | 28 | 12 of 12 |
| Radford | 4% | #5 of 8 | 37% | 27 | 11 of 12 |

## The direct prompt, model by model

- Claude Haiku 4.5: payscale first (first choices: Payscale) (alternatives: Lattice, SalaryCube, beqom)
- GPT-5.4 mini: payscale first (first choices: Payscale) (alternatives: Radford, Salary.com)
- Perplexity Sonar: payscale first (first choices: Payscale) (alternatives: Ravio, SalaryCube)
- Grok 4.1 Fast: payscale first (first choices: Payscale) (alternatives: Pave, Salary.com)
- DeepSeek V4 Flash: payscale first (first choices: Payscale) (alternatives: ERI, Pave, Ravio, Salary.com)
- Llama 4 Maverick: payscale first (first choices: Payscale)
- Qwen 3.7 Flash: payscale first (first choices: Payscale) (alternatives: Pave, Payfactors, Salary.com, SalaryCube)
- Kimi K2: payscale first (first choices: Payscale, SalaryCube) (alternatives: Mercer, Radford, Salary.com)
- MiniMax M2.5: payscale first (first choices: Payscale) (alternatives: Comprehensive.io)
- Gemini 3.5 Flash: neither first, one named (first choices: Pave) (alternatives: Carta Total Comp, Figures, Payscale, Radford, Ravio, Salary.com)
- Mistral Small: neither named (first choices: SalaryCube) (alternatives: Pave, Payfactors, Salary.com)
- GLM 4.7 FlashX: neither named (first choices: Pave) (alternatives: Aon Radford, CaptivateIQ, Ficstar, Mercer, OpenComp, Payscale Ascent, Ravio, Salary.com CompAnalyst)

## What the models said about Payscale

- "Avoid crowdsourced data (like Glassdoor or basic Payscale) as your primary source; it is often outdated or contains inflated self-reported figures." (Gemini 3.5 Flash, scale prompt, soft negative)
- "I'd suggest starting with Payscale or CompAnalyst as they offer the best balance of cost, data quality, and relevance to B2B roles" (MiniMax M2.5, paraphrase prompt, first choice)
- "OR Payscale (if you are traditional B2B/professional services) to establish your baseline corporate job architecture and salary bands" (Gemini 3.5 Flash, paraphrase prompt, first choice)
- "I'd suggest starting with Payscale/CompAnalyst as it's specifically mentioned as ideal for broad US mid-market companies" (MiniMax M2.5, direct prompt, first choice)

## What the models said about Radford

- "What to Avoid on a Limited Budget ... enterprise-grade pricing with far more data than most small/mid-market companies need" (Kimi K2, budget prompt, hard negative)
- "Avoid traditional surveys (Mercer/Radford: $10k+/year) unless scaling." (Grok 4.1 Fast, budget prompt, hard negative)
- "it requires active participation (submitting your own data) and can be expensive and labor-intensive to manage compared to automated tools" (Gemini 3.5 Flash, paraphrase prompt, soft negative)
- "I'd typically recommend starting with Radford if you're in tech/SaaS, or Mercer for broader B2B sectors" (Claude Haiku 4.5, paraphrase prompt, first choice)
- "Mercer or Radford-style benchmark data is often the strongest foundation" (GPT-5.4 mini, paraphrase prompt, first choice)
- "Tech-First / Specialized Platforms (Radford, Pave, CompAnalyst, Option Impact) — Best for: Scalability, ease of use, and equity/tech-heavy roles." (Qwen 3.7 Flash, scale prompt, alternative)

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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.