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Background screening · November 2026 Edition

Certn vs AccuSourceHR

Three of fourteen models named Certn first on the direct prompt; one named AccuSourceHR. Certn was named by thirteen of the fourteen models and AccuSourceHR by eight and Certn carries 37 labels and AccuSourceHR 11, so the shares are not directly comparable.

Certn

accepted challenger

Named in one category this edition.

AccuSourceHR

accepted challenger

Named in one category this edition.

First-choice share13%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%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.
Labels3711A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Certn reading right to left. Rank and label count are printed, not drawn.Checkr was named alongside these two in twelve of the fourteen direct answers. Checkr vs Certn · Checkr vs AccuSourceHR · Certn vs GoodHire

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 background screening page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
CertnFirst choices, of fourteen modelsAccuSourceHR
Direct31
Paraphrase01
Comparative00
Budget-constrained40
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 Certn and AccuSourceHR stood in it.
ModelDirectACParaphraseACComparativeACBudget-constrainedACScale-constrainedACNegativeAC
Claude Haiku 4.5AC
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity SonarAC
Grok 4.20
Mistral SmallAC
DeepSeek V4 Flash
Llama 4 MaverickAC
Qwen 3.7 FlashAC
Kimi K2AC
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30BAC
CertnAC AccuSourceHR first choice named as an alternative argued againstblank: not namedEach cell is one answer, Certn on the left and AccuSourceHR on the right.

The direct prompt

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

Certn first, AccuSourceHR not the choice

3 of 14 modelsAccuSourceHR was named in the answer but not as the choice, or not at all.
Mistral SmallCertn, Checkr alternatives: ADP RUN, Mitratech AssureHire, Verified First
DeepSeek V4 FlashCertn, Checkr alternatives: Accurate Background, GoodHire, Turn
Kimi K2Certn, Checkr alternatives: First Advantage, GoodHire, Verified First

AccuSourceHR first, Certn not the choice

1 of 14 modelsCertn was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5AccuSourceHR alternatives: Accurate Background, Bchex, Mitratech AssureHire

Neither was the first choice, one was named

8 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniCheckr alternatives: Certn, GoodHire, VICTIG
Gemini 3.5 FlashCheckr alternatives: Accurate Background, Certn, Verified First
Perplexity SonarCheckr alternatives: AccuSourceHR, Certn, Mitratech AssureHire, Verified First
Grok 4.20Checkr alternatives: Certn, GoodHire
Llama 4 MaverickCheckr alternatives: AccuSourceHR, Bchex, Certn, ClearStar, First Advantage, GoodHire, Mitratech AssureHire, Turn, Verified First
GLM 4.7 FlashXCheckr alternatives: Certn, GoodHire
MiniMax M2.5Checkr alternatives: Accurate Background, Certn, GoodHire
Muse Glimmer 30BCheckr alternatives: AccuSourceHR, Accurate Background, BackgroundChecks.com, Certn, HireShield, Mitratech AssureHire, Turn, Verified First

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Qwen 3.7 FlashCheckster alternatives: BrightCheck, Certified Background, GoodHire
GPT-6 LunaAccurate Background alternatives: Checkr

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
Certn leads by four points.
Certn4%#3 of 10
AccuSourceHR0%#– of 10
The full small business standing →
Mid-marketThe figures above
Certn leads by nine points.
Certn13%#2 of 15
AccuSourceHR4%#5 of 15
The full mid-market standing →
Enterprise
Certn leads by two points.
Certn2%#5 of 10
AccuSourceHR0%#– of 10
The full enterprise standing →

What the models said about Certn

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

“Checkr or Certn offer the best combination of modern technology, transparent pricing, scalability, and integration capabilities” Kimi K2 · direct prompt · first choice
“Certn is the cheapest option at $13.99/month, making it a strong choice for budget-conscious businesses” Mistral Small · budget prompt · first choice
“If you need the absolute lowest price per report: Choose Certn.” Qwen 3.7 Flash · budget prompt · first choice

What the models said about AccuSourceHR

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 compliance and tailored support are your top priorities, go with AccuSourceHR.” Mistral Small · paraphrase prompt · first choice
“AccuSourceHR is the best fit for regulated mid-market hiring” Claude Haiku 4.5 · direct prompt · first choice
“Better alternatives mentioned in research include: Checkr, GoodHire, Backgrounds Online, AccuSourceHR, and Sterling” Kimi K2 · negative 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.