# Radford: how AI models rank it, September 2026

HR AI Recommendation Index, September 2026 Edition. Named in 36 judge labels across 2 categories by 11 of 12 models. Page: https://hr-ai-index.com/vendors/radford/

## Standing by category

| Category | Share | Rank | Negative rate | Labels |
|---|---|---|---|---|
| Compensation benchmarking data | 4% | 6 | 37% | 27 |
| Compensation management | 0% | 42 | 11% | 9 |

## What the models said for it

- "I'd typically recommend starting with Radford if you're in tech/SaaS, or Mercer for broader B2B sectors" (Claude Haiku 4.5, Compensation benchmarkin)
- "Mercer or Radford-style benchmark data is often the strongest foundation" (GPT-5.4 mini, Compensation benchmarkin)
- "Tech-First / Specialized Platforms (Radford, Pave, CompAnalyst, Option Impact) — Best for: Scalability, ease of use, and equity/tech-heavy roles." (Qwen 3.7 Flash, Compensation benchmarkin)
- "Alternatively, rely on traditional, highly vetted corporate surveys from firms like Radford (Aon), WTW, or Mercer." (Gemini 3.5 Flash, Compensation benchmarkin)

## And against it

- "What to Avoid on a Limited Budget ... enterprise-grade pricing with far more data than most small/mid-market companies need" (Kimi K2, Compensation benchmarkin)
- "Avoid traditional surveys (Mercer/Radford: $10k+/year) unless scaling." (Grok 4.1 Fast, Compensation benchmarkin)
- "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, Compensation benchmarkin)
- "legacy enterprise surveys like Mercer, Radford (Aon), or Willis Towers Watson (WTW)... are generally out of reach" (Gemini 3.5 Flash, Compensation benchmarkin)

## Record

- Method: https://hr-ai-index.com/methodology/
- Raw judge labels and full responses: https://hr-ai-index.com/data/
- License: CC BY 4.0. Cite as HR AI Recommendation Index, September 2026 Edition, hr-ai-index.com.