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Staffing agency platforms · November 2026 Edition

Bullhorn vs JobAdder

Nine of fourteen models named Bullhorn first on the direct prompt; one named JobAdder. Bullhorn was named by fourteen of the fourteen models and JobAdder by ten and Bullhorn carries 45 labels and JobAdder 12, so the shares are not directly comparable.

Bullhorn

accepted challenger

Named in five categories this edition.

JobAdder

accepted challenger

Named in two categories this edition.

First-choice share24%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate13%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#5A position in a field of 15; printed, not drawn.
Labels4512A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Bullhorn reading right to left. Rank and label count are printed, not drawn.Zoho Recruit vs Bullhorn · Zoho Recruit vs JobAdder · Bullhorn vs Recruit CRM

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 staffing agency platforms page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
BullhornFirst choices, of fourteen modelsJobAdder
Direct91
Paraphrase311 against Bullhorn
Comparative901 against Bullhorn
Budget-constrained004 against Bullhorn
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.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Bullhorn and JobAdder 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
Bullhorn JobAdder first choice named as an alternative argued againstblank: not namedEach cell is one answer, Bullhorn on the left and JobAdder on the right.

The direct prompt

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

Bullhorn first, JobAdder an alternative

9 of 14 modelsJobAdder was named in the answer but not as the choice, or not at all.
GPT-5.4 miniBullhorn alternatives: Avionté, Ceipal ATS, JobDiva, Zoho Recruit
Perplexity SonarBullhorn alternatives: Recruiterflow, Vincere
Grok 4.20Bullhorn alternatives: Avionté, Ceipal ATS, Recruiterflow, Zoho Recruit
Mistral SmallBullhorn, Zoho Recruit
DeepSeek V4 FlashBullhorn alternatives: Crelate, JobDiva, Recruit CRM, Zoho Recruit
Kimi K2Bullhorn alternatives: 100Hires, Avionté, JobDiva, Recruit CRM
GLM 4.7 FlashXBullhorn alternatives: Avionté, Ceipal, JobAdder, Longlist
MiniMax M2.5Bullhorn, Zoho Recruit alternatives: Vincere
Muse Glimmer 30BBullhorn alternatives: 100Hires, Avionté, Ceipal, Crelate, JobAdder, JobDiva, Loxo, TempWorks

JobAdder first, Bullhorn an alternative

1 of 14 modelsBullhorn was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5JobAdder, Recruit CRM alternatives: Bullhorn, Recruiterflow

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashConexis VMS alternatives: Bullhorn, Fiverr, Recruit CRM, Recruiterflow, RecruitiFi, Scout Exchange, Worksuite
Llama 4 MaverickVincere alternatives: 100Hires, Bullhorn, Zoho Recruit
Qwen 3.7 FlashVincere alternatives: Bullhorn, Crelate

Neither was named

1 of 14 modelsThe answer made no first choice from these two in this category.
GPT-6 LunaBeeline alternatives: SAP Fieldglass, Workday VNDLY

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
Bullhorn leads by nine points.
Bullhorn9%#4 of 11
JobAdder0%#– of 11
The full small business standing →
Mid-marketThe figures above
Bullhorn leads by twenty points.
Bullhorn24%#2 of 15
JobAdder4%#5 of 15
The full mid-market standing →
Enterprise
Bullhorn leads by forty-seven points.
Bullhorn47%#1 of 10
JobAdder0%#– of 10
The full enterprise standing →

What the models said about Bullhorn

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

“If you need market-proven scale, deep staffing-specific compliance and a large partner ecosystem, start with Bullhorn. It is the default mid-size / large agency platform.” Muse Glimmer 30B · paraphrase prompt · first choice
“For most mid-market B2B staffing agencies, Bullhorn remains the safest choice due to its market dominance, extensive integrations, and proven scalability.” Kimi K2 · direct prompt · first choice
“the most established, enterprise-leaning staffing platform; strong end-to-end ATS/CRM, automation, and a large ecosystem/marketplace” GPT-5.4 mini · comparative prompt · first choice

What the models said about JobAdder

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

“If you're looking for modern design and ease of use, JobAdder or Recruit CRM are strong choices.” Claude Haiku 4.5 · direct prompt · first choice
“Request a demo from JobAdder first... this is currently the safest bet.” Qwen 3.7 Flash · paraphrase prompt · first choice
“best for small and mid-size perm and temp agencies, especially in ANZ and the UK” Muse Glimmer 30B · comparative prompt · alternative
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.