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Recruitment marketing and career sites · November 2026 Edition

Manatal vs Workable

Zero of fourteen models named Manatal first on the direct prompt; two named Workable. Manatal was named by twelve of the fourteen models and Workable by eleven and Manatal carries 17 labels and Workable 20, so the shares are not directly comparable.

Manatal

accepted challenger

Named in nine categories this edition.

Workable

accepted challenger

Named in fourteen categories this edition.

First-choice share11%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%5%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#5A position in a field of 17; printed, not drawn.
Labels1720A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Manatal reading right to left. Rank and label count are printed, not drawn.Teamtailor was named alongside these two in nine of the fourteen direct answers. Teamtailor vs Manatal · Teamtailor vs Workable · Manatal vs Zoho Recruit

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.
ManatalFirst choices, of fourteen modelsWorkable
Direct02
Paraphrase00
Comparative00
Budget-constrained621 against Workable
Scale-constrained00
Negative00
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.

Across every category in the November 2026 Edition, Manatal and Workable were named in the same answer 122 times, of the 314 answers naming Manatal and the 367 naming Workable. In those answers Workable took the first choice eighteen times and Manatal thirty-nine.

Every model, every framing

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

The direct prompt

The plain question, one answer per model, grouped by where Manatal and Workable stood in it.

Workable first, Manatal not the choice

2 of 14 modelsManatal was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Teamtailor, Workable alternatives: Greenhouse, Jobvite
MiniMax M2.5Workable alternatives: Beamery, Greenhouse, Teamtailor

Neither was the first choice, one was named

5 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniTeamtailor alternatives: Jobvite, Workable
Perplexity SonarTeamtailor alternatives: Ashby, Clinch, Lever, Recruitee, SmartDreamers, SmartRecruiters, Workable
Kimi K2Greenhouse alternatives: Gem, Recruitee, SmartRecruiters, Teamtailor, Workable
GLM 4.7 FlashXTeamtailor alternatives: Gem, Recruitee, Workable, Zoho Recruit
Muse Glimmer 30BRecruitee, Teamtailor alternatives: TalentLyft, Workable

Neither was named

7 of 14 modelsThe answer made no first choice from these two in this category.
Gemini 3.5 FlashGem alternatives: Ashby, Clinch, Jobvite, Lever, Teamtailor
Grok 4.20Greenhouse alternatives: Breezy HR, Freshteam, JobScore, Teamtailor, Zoho Recruit
Mistral SmallGreenhouse alternatives: Beamery, Freshteam, JobDiva, TalentLyft
DeepSeek V4 FlashAshby alternatives: Gem, Greenhouse, Lever, SmartRecruiters
Llama 4 Maverickno first choice
Qwen 3.7 FlashGreenhouse, Lever alternatives: Beamery, Phenom Talent Experience Cloud, SmartDreamers
GPT-6 LunaGem alternatives: Phenom

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
Manatal leads by seven points.
Manatal19%#1 of 12
Workable12%#4 of 12
The full small business standing →
Mid-marketThe figures above
Manatal leads by four points.
Manatal11%#2 of 17
Workable7%#5 of 17
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Manatal0%#– of 15
Workable0%#– of 15
The full enterprise standing →

What the models said about Manatal

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 small businesses, options like Manatal ($15/user/month), Workable ($299/month), or Teamtailor are recommended for their ease of setup and relatively lower costs.” Llama 4 Maverick · budget prompt · first choice
“Best Overall Value (Lowest Cost for Features) Platform: Manatal ... widely regarded as the best solution for Small and Medium Businesses (SMBs)” Qwen 3.7 Flash · budget prompt · first choice

What the models said about Workable

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

“its listed entry price is $299/month for companies with 1–20 employees, so it may be more than a tight budget calls for” GPT-6 Luna · budget prompt · soft negative
“options like Manatal ($15/user/month), Workable ($299/month), or Teamtailor are recommended for their ease of setup and relatively lower costs” Llama 4 Maverick · budget 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.