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Index › Talent acquisition › Recruitment marketing › Zoho Recruit vs Phenom
Recruitment marketing and career sites · October 2026 Edition

Zoho Recruit vs Phenom

Zero of fourteen models named Zoho Recruit first on the direct prompt; one named Phenom. Zoho Recruit was named by fourteen of the fourteen models and Phenom by thirteen and Zoho Recruit carries 15 labels and Phenom 30, so the shares are not directly comparable.

Zoho Recruit

accepted challenger

Named in nine categories this edition.

Phenom

criticized challenger

Named in six categories this edition.

First-choice share10%5%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%37%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#8A position in a field of 17; printed, not drawn.
Labels1530A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Zoho Recruit reading right to left. Rank and label count are printed, not drawn.Teamtailor was named alongside these two in twelve of the fourteen direct answers. Teamtailor vs Zoho Recruit · Teamtailor vs Phenom · Zoho Recruit vs Workable

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Zoho RecruitFirst choices, of fourteen modelsPhenom
Direct016 against Phenom
Paraphrase022 against Phenom
Comparative04
Budget-constrained601 against Phenom
Scale-constrained001 against Phenom
Negative001 against Phenom
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Zoho Recruit and Phenom stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Zoho Recruit Phenom first choice named as an alternative argued againstblank: not namedEach cell is one answer, Zoho Recruit on the left and Phenom on the right.

The direct prompt

The plain question, one answer per model, grouped by where Zoho Recruit and Phenom stood in it.

Phenom first, Zoho Recruit not the choice

1 of 14 modelsZoho Recruit was named in the answer but not as the choice, or not at all.
Perplexity SonarPhenom alternatives: Joveo, Teamtailor, Workable

Neither was named

13 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Greenhouse, SmartRecruiters alternatives: Jobilla, Teamtailor
GPT-5.4 miniJobvite alternatives: Appcast, Teamtailor
Gemini 3.5 FlashGem, Teamtailor alternatives: Greenhouse, Lever, Pinpoint
Grok 4.1 FastSmartRecruiters alternatives: Lever, Teamtailor
Mistral SmallGem alternatives: SmartRecruiters
DeepSeek V4 Flash100Hires, Teamtailor alternatives: Recruitee
Llama 4 Maverickno first choice
Qwen 3.7 Flash100Hires alternatives: Gem, SmartRecruiters, Teamtailor
Kimi K2Teamtailor alternatives: Greenhouse, Lever, Workable
GLM 4.7 FlashXTeamtailor alternatives: Ashby, Lever
MiniMax M2.5Teamtailor alternatives: Beamery, SmartRecruiters
GPT-6 LunaGem alternatives: Teamtailor
Muse Glimmer 30BJobvite, Workable alternatives: Teamtailor

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Zoho Recruit leads by twelve points.
Zoho Recruit12%#2 of 14
Phenom0%#14 of 14
The full small business standing →
Mid-marketThe figures above
Zoho Recruit leads by five points.
Zoho Recruit10%#2 of 17
Phenom5%#8 of 17
The full mid-market standing →
Enterprise
The order flips: Phenom leads at enterprise.
Phenom35%#1 of 13
Zoho Recruit0%#– of 13
The full enterprise standing →

What the models said about Zoho Recruit

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“For companies with very limited budgets, Zoho Recruit or Breezy HR are excellent starting points since they offer robust free tiers.” Claude Haiku 4.5 · budget prompt · first choice
“If you need a system from scratch: Choose Zoho Recruit. It provides the most feature-rich environment for $0–$50/month.” Qwen 3.7 Flash · budget prompt · first choice

What the models said about Phenom

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.

“the best approach is to avoid high-end, enterprise-only "Recruitment Marketing Platforms" (RMPs) like Phenom, Beamery, or Symphony Talent” Gemini 3.5 Flash · budget prompt · hard negative
“Be cautious of platforms like Beamery, Avature, or Phenom People. ... built for the Fortune 500” Gemini 3.5 Flash · direct prompt · hard negative
“Phenom is a massive, AI-powered "Talent Experience Management" (TXM) platform. It is best known for creating highly personalized, consumer-grade front-end career sites.” Gemini 3.5 Flash · comparative prompt · first choice
“I recommend Phenom or MokaHR for a mid-sized B2B company seeking a powerful, AI-driven career site builder” Mistral Small · paraphrase prompt · first choice
“1. Phenom or Symphony Talent - These are among the top-ranked branded careers site builders for 2026” Claude Haiku 4.5 · paraphrase prompt · first choice
Also compared

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.