Ravio vs Comprehensive
One of fourteen models named Ravio first on the direct prompt; two named Comprehensive. Ravio was named by eleven of the fourteen models and Comprehensive by eight and Ravio carries 29 labels and Comprehensive 16, so the shares are not directly comparable.
Named in three categories this edition.
Named in two 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
RavioFirst choices, of fourteen modelsComprehensive
Across every category in the October 2026 Edition, Ravio and Comprehensive were named in the same answer twenty-four times, of the 165 answers naming Ravio and the 39 naming Comprehensive. In those answers Comprehensive took the first choice three times and Ravio two.
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 | |
Ravio Comprehensive first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ravio on the left and Comprehensive on the right.The direct prompt
Ravio first, Comprehensive not the choice
1 of 14 modelsComprehensive was named in the answer but not as the choice, or not at all.Comprehensive first, Ravio an alternative
2 of 14 modelsRavio was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
8 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
Ravio4%#5 of 16Comprehensive4%#6 of 16Ravio9%#2 of 13Comprehensive9%#3 of 13Ravio0%#14 of 15Comprehensive0%#– of 15What 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
What the models said about Comprehensive
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Prioritize platforms like Comprehensive, Figures.hr, Salary.com CompAnalyst, Paycom, or Ravio, which are frequently recommended for this size” Grok 4.1 Fast · scale prompt · first choice
“Start with Comprehensive if you want an all-in-one platform for salary planning, equity management, and compensation reviews” Kimi K2 · paraphrase prompt · first choice
“I'd recommend evaluating Compport and Comprehensive first since both are specifically positioned for mid-market companies.” Claude Haiku 4.5 · 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.