Four of twelve models named Rippling first on the direct prompt; zero named Benefitfocus. Rippling was named by eleven of the twelve models and Benefitfocus by ten and Rippling carries 43 labels and Benefitfocus 23, so the shares are not directly comparable.
Named in twenty-eight 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 twelve 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.
| 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 |
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. Five of five in this category shown.
“"Worst support EVER" – very poor responsiveness after open enrollment” GLM 4.7 FlashX · negative prompt · hard negative
“Avoid for Support-Reliant Organizations” DeepSeek V4 Flash · negative prompt · hard negative
“## Top Recommendation: Benefitfocus” GLM 4.7 FlashX · paraphrase prompt · first choice
“You want a carrier‑grade, self‑service portal for employees | Benefitfocus” GLM 4.7 FlashX · direct prompt · alternative
“Excels in employee experience and engagement, with strong plan navigation tools.” MiniMax M2.5 · comparative prompt · alternative
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.