Teamtailor vs Recruitee
Five of fourteen models named Teamtailor first on the direct prompt; zero named Recruitee. Teamtailor was named by thirteen of the fourteen models and Recruitee by eight and Teamtailor carries 30 labels and Recruitee 13, so the shares are not directly comparable.
Named in two categories this edition.
Named in six 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.
By framing
TeamtailorFirst choices, of fourteen modelsRecruitee
Across every category in the October 2026 Edition, Teamtailor and Recruitee were named in the same answer twenty-three times, of the 75 answers naming Teamtailor and the 66 naming Recruitee. In those answers Recruitee took the first choice zero times and Teamtailor eight.
Every model, every framing
| 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 | | |||||
| GPT-6 Luna | | | ||||
| Muse Glimmer 30B | | | | |
Teamtailor Recruitee first choice named as an alternative argued againstblank: not namedEach cell is one answer, Teamtailor on the left and Recruitee on the right.The direct prompt
Teamtailor first, Recruitee an alternative
5 of 14 modelsRecruitee was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
2 of 14 modelsThe answer made no first choice from these two in this category.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.
By buyer segment
Teamtailor7%#6 of 14Recruitee2%#10 of 14Teamtailor23%#1 of 17Recruitee5%#6 of 17Teamtailor0%#– of 13Recruitee0%#– of 13What the models said about Teamtailor
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“flag Teamtailor for "limited customization on Teamtailor, restricting their ability to personalize the career site fully"” Muse Glimmer 30B · negative prompt · soft negative
“Limited customization options for career sites ... Integration issues” DeepSeek V4 Flash · negative prompt · soft negative
“platforms like Teamtailor and Recruitee are often cited for mid-market teams that want a great career site without an enterprise sales cycle” Muse Glimmer 30B · scale prompt · first choice
“Teamtailor (Best Overall for Employer Branding & Ease of Use) ... widely considered the gold standard for mid-market employer branding” Gemini 3.5 Flash · paraphrase prompt · first choice
“Look for modern, agile platforms (such as Gem, Clinch, or Teamtailor) where implementation takes weeks, not quarters.” Gemini 3.5 Flash · scale prompt · first choice
What the models said about Recruitee
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Recruitee and Workable are excellent all‑around choices that balance career site builder quality, ATS integration, and mid‑market pricing.” GLM 4.7 FlashX · paraphrase prompt · first choice
“platforms like Teamtailor and Recruitee are often cited for mid-market teams” Muse Glimmer 30B · scale prompt · first choice
“The best career site builder for a mid-sized B2B company would be Recruitee” Llama 4 Maverick · paraphrase 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.