# Comprehensive.io vs Radford: which do AI models recommend for compensation benchmarkin, September 2026

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

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Comprehensive.io | 12% | #3 of 8 | 0% | 12 | 11 of 12 |
| Radford | 4% | #5 of 8 | 37% | 27 | 11 of 12 |

## The direct prompt, model by model

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

- "three free salary benchmarking tools cover most needs at zero cost: Pave (tech roles, under 200 employees), Comprehensive.io (US tech daily data), and BLS OEWS" (Claude Haiku 4.5, budget prompt, first choice)
- "Free daily-refreshed tech salary data from 6,000+ U.S. companies (no participation needed) ... Great for startups/lean teams." (Grok 4.1 Fast, budget prompt, first choice)
- "Pave and Comprehensive.io are currently leading the market for affordable or free options" (Qwen 3.7 Flash, budget 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.