Four of fourteen models named Greenhouse first on the direct prompt; two named Gem. Greenhouse was named by ten of the fourteen models and Gem by ten and Greenhouse carries 20 labels and Gem 14, so the shares are not directly comparable.
Named in fourteen categories this edition.
Named in eight 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.
“Start with Lever or Greenhouse if you want an all-in-one solution that combines ATS and basic recruitment marketing.” Qwen 3.7 Flash · direct 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.
“Avature or Gem-style stacks are stronger, but they're more often chosen by teams with more complex recruiting operations” GPT-5.4 mini · direct prompt · soft negative
“Examples in this category often include Teamtailor, Beamery, Gem, or Phenom.” Qwen 3.7 Flash · scale 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.