Six of fourteen models named Ashby first on the direct prompt; zero named Zoho Recruit. Ashby was named by twelve of the fourteen models and Zoho Recruit by twelve and Ashby carries 28 labels and Zoho Recruit 17, so the shares are not directly comparable.
Named in seven categories this edition.
Named in nine 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 AI recruiting assistants 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. Two of two in this category shown.
“including Greenhouse, Lever, and Ashby, may be in violation of the Fair Credit Reporting Act (FCRA)” Qwen 3.7 Flash · negative prompt · soft negative
“Ashby (potential FCRA issues in consumer reporting)” Grok 4.20 · negative prompt · soft negative
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Zoho Recruit is often the best overall choice because it combines an affordable, transparent price with built-in AI features” GLM 4.7 FlashX · budget 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.