Six of twelve models named Crunchr first on the direct prompt; zero named Culture Amp. Crunchr was named by eleven of the twelve models and Culture Amp by nine and Crunchr carries 29 labels and Culture Amp 22, so the shares are not directly comparable.
Named in two categories this edition.
Named in ten 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 people analytics 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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“avoid it if you are outside Europe and do not need CSRD-style compliance reporting” Perplexity Sonar · negative prompt · hard negative
“I'd lean toward Rippling if you need an integrated HR platform, or Crunchr if you already have an HRIS” Kimi K2 · paraphrase prompt · first choice
“Crunchr is the top choice due to its focus on mid-market needs, quick setup, and GDPR compliance.” Mistral Small · direct prompt · first choice
“the best *default* choice is usually Crunchr if you want a dedicated people analytics platform” Perplexity Sonar · direct prompt · first choice
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.