Seven of fourteen models named Rippling first on the direct prompt; three named ADP Workforce Now. Rippling was named by thirteen of the fourteen models and ADP Workforce Now by eleven and Rippling carries 43 labels and ADP Workforce Now 26, so the shares are not directly comparable.
Named in twenty-nine categories this edition.
Named in nineteen 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 payroll software page.
Across every category in the October 2026 Edition, Rippling and ADP Workforce Now were named in the same answer 304 times, of the 1329 answers naming Rippling and the 616 naming ADP Workforce Now. In those answers ADP Workforce Now took the first choice forty-one times and Rippling sixty-five.
| 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.
No label in this category carried a quote.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“ADP Workforce Now, Paycom, and Paycor are often top recommendations due to their scalability, comprehensive features, and strong support.” MiniMax M2.5 · direct 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.