Zero of fourteen models named Zoho Recruit first on the direct prompt; zero named Beamery. Zoho Recruit was named by eleven of the fourteen models and Beamery by twelve and Zoho Recruit carries 22 labels and Beamery 21, so the shares are not directly comparable.
Named in nine 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 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 recruitment marketing and career sites 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 4.20 | ||||||
| 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.
“Prioritize platforms with strong, recent ratings (e.g., 4.5+ on Capterra/G2) like Zoho Recruit, Teamtailor, or iCIMS for marketing features.” Grok 4.20 · negative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Examples in this category often include Teamtailor, Beamery, Gem, or Phenom.” Qwen 3.7 Flash · scale prompt · first choice
“Deep Automation & Segmentation | Beamery or Phenom” Qwen 3.7 Flash · comparative 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.