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Recruiting CRM and sourcing · November 2026 Edition

Gem vs Fetcher

Eight of fourteen models named Gem first on the direct prompt; two named Fetcher. Gem was named by thirteen of the fourteen models and Fetcher by eleven and Gem carries 35 labels and Fetcher 18, so the shares are not directly comparable.

Gem

accepted challenger

Named in eight categories this edition.

Fetcher

accepted challenger

Named in two categories this edition.

First-choice share20%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%6%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#7A position in a field of 13; printed, not drawn.
Labels3518A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Gem reading right to left. Rank and label count are printed, not drawn.hireEZ was named alongside these two in twelve of the fourteen direct answers. Gem vs hireEZ · Gem vs Manatal · Gem vs Lever

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 Recruiting CRM and sourcing page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
GemFirst choices, of fourteen modelsFetcher
Direct821 against Fetcher
Paraphrase201 against Gem
Comparative20
Budget-constrained00
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 Gem and Fetcher stood in it.
ModelDirectFEParaphraseFEComparativeFEBudget-constrainedFEScale-constrainedFENegativeFE
Claude Haiku 4.5FE
GPT-5.4 mini
Gemini 3.5 FlashFEFE
Perplexity SonarFE
Grok 4.20FE
Mistral SmallFE
DeepSeek V4 FlashFE
Llama 4 MaverickFEFE
Qwen 3.7 FlashFE
Kimi K2
GLM 4.7 FlashXFE
MiniMax M2.5FE
GPT-6 Luna
Muse Glimmer 30BFEFE
GemFE Fetcher first choice named as an alternative argued againstblank: not namedEach cell is one answer, Gem on the left and Fetcher on the right.

The direct prompt

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

Both were the first choice

2 of 14 modelsThe answer named them together, and the judge labeled each a first choice.
Llama 4 MaverickFetcher, Findem, Gem, hireEZ
GLM 4.7 FlashXFetcher, Gem alternatives: hireEZ

Gem first, Fetcher an alternative

6 of 14 modelsFetcher was named in the answer but not as the choice, or not at all.
GPT-5.4 miniGem alternatives: LinkedIn Recruiter, Loxo, SeekOut, hireEZ
Mistral SmallGem, hireEZ alternatives: Fetcher, Findem
Qwen 3.7 FlashGem alternatives: ContactOut, Fetcher, Lusha, hireEZ
Kimi K2Gem alternatives: SeekOut, hireEZ
MiniMax M2.5Gem, SeekOut alternatives: Fetcher, Juicebox, hireEZ
Muse Glimmer 30BGem, hireEZ alternatives: Fetcher, Findem, Humanly, Manatal, TheHireHub.AI

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashLinkedIn Recruiter alternatives: Fetcher, Findem, SeekOut, ZoomInfo Talent, hireEZ
Perplexity SonarhireEZ alternatives: Gem, Lever, SeekOut, Workable
DeepSeek V4 FlashhireEZ alternatives: Fetcher, Gem, LinkedIn Recruiter, SeekOut
GPT-6 LunaLinkedIn Recruiter alternatives: Gem, SeekOut

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5LinkedIn Recruiter alternatives: Apollo.io, ZoomInfo
Grok 4.20hireEZ alternatives: Findem, SeekOut

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
Fetcher leads by two points.
Fetcher2%#7 of 10
Gem0%#9 of 10
The full small business standing →
Mid-marketThe figures above
The order flips: Gem leads at mid-market.
Gem20%#1 of 13
Fetcher4%#7 of 13
The full mid-market standing →
Enterprise
Gem leads by fifteen points.
Gem15%#2 of 13
Fetcher0%#– of 13
The full enterprise standing →

What the models said about Gem

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

“Excellent for outbound sourcing but is a point solution, not a full ATS+CRM” DeepSeek V4 Flash · paraphrase prompt · soft negative
“Gem and Fetcher are widely considered the top choices... If you can only afford one advanced tool right now, start with Gem.” Qwen 3.7 Flash · direct prompt · first choice
“Gem (Top Recommendation) ... The gold standard if budget allows and outbound sourcing is a primary channel.” Gemini 3.5 Flash · paraphrase prompt · first choice
“SeekOut or Gem offer the best balance of functionality, pricing, and ease of use” MiniMax M2.5 · direct prompt · first choice

What the models said about Fetcher

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

“note that it was reportedly retired in late 2026 per some sources—verify current status” Grok 4.20 · direct prompt · soft negative
“For most mid-market B2B companies, I recommend starting with Gem or Fetcher.” GLM 4.7 FlashX · direct prompt · first choice
“likely to be one of the following: Findem, Gem, hireEZ, or Fetcher” Llama 4 Maverick · direct prompt · first choice
“Fetcher, which takes the work out of sourcing entirely and is best for lean teams wanting delivered candidates” Llama 4 Maverick · budget 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.