Pave vs Ravio
Two of fourteen models named Pave first on the direct prompt; one named Ravio. Pave was named by thirteen of the fourteen models and Ravio by eleven and Pave carries 34 labels and Ravio 29, so the shares are not directly comparable.
Named in six categories this edition.
Named in three categories 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 management page.
By framing
PaveFirst choices, of fourteen modelsRavio
Across every category in the October 2026 Edition, Pave and Ravio were named in the same answer 107 times, of the 219 answers naming Pave and the 165 naming Ravio. In those answers Ravio took the first choice two times and Pave thirty-nine.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | |||
| Grok 4.1 Fast | | | | | | |
| Mistral Small | | | | | | |
| DeepSeek V4 Flash | | | | |||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | | |||||
| Kimi K2 | | | | | ||
| GLM 4.7 FlashX | | | ||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | | | ||||
| Muse Glimmer 30B | | |
Pave Ravio first choice named as an alternative argued againstblank: not namedEach cell is one answer, Pave on the left and Ravio on the right.The direct prompt
Pave first, Ravio not the choice
2 of 14 modelsRavio was named in the answer but not as the choice, or not at all.Ravio first, Pave an alternative
1 of 14 modelsPave was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 of 14 modelsThe answer made no first choice from these two in this category.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.
By buyer segment
What the models said about Pave
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“These are generally considered "comp-only" tools and are better suited for companies with fewer than 1,000 employees.” Mistral Small · negative prompt · soft negative
“Pave (Best for Modern B2B & Tech Companies) ... Mid-market companies find the sweet spot in platforms like CaptivateIQ (for sales) and Pave or HiBob” Gemini 3.5 Flash · direct prompt · first choice
“If you need real-time tech benchmarking and want to heavily recruit with transparent visual equity breakdowns, Pave is a frontrunner.” Gemini 3.5 Flash · comparative prompt · first choice
“Best for: real-time market pricing + modern comp planning ... Best for modern comp teams wanting speed and analytics: Pave” GPT-5.4 mini · comparative prompt · first choice
What the models said about Ravio
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“better suited for companies with fewer than 1,000 employees. They may become less predictable or scalable for larger organizations.” Mistral Small · negative prompt · soft negative
“Fine for small teams (<1,000 users), but avoid for enterprises” Grok 4.1 Fast · negative prompt · soft negative
“I would pick Aeqium for compensation review workflow flexibility, or Ravio if your highest priority is market benchmarking and salary banding” Perplexity Sonar · paraphrase prompt · first choice
“Ravio and CompUp are particularly strong choices if you need advanced features for complex, multi-country compensation planning” Mistral Small · paraphrase 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.