Four of fourteen models named Rippling first on the direct prompt; one named Paylocity. Rippling was named by fourteen of the fourteen models and Paylocity by eleven and Rippling carries 42 labels and Paylocity 19, so the shares are not directly comparable.
Named in twenty-nine categories this edition.
Named in twenty 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 benefits administration page.
Across every category in the October 2026 Edition, Rippling and Paylocity were named in the same answer 165 times, of the 1329 answers naming Rippling and the 282 naming Paylocity. In those answers Paylocity took the first choice six times and Rippling forty-six.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“I wouldn't make it my default *standalone* enrollment add-on if you plan to keep a different HRIS, since Rippling says its benefits product does not work with other HR systems” GPT-6 Luna · paraphrase prompt · soft negative
No label in this category carried a quote.
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.