Eleven of fourteen models named Checkr first on the direct prompt; zero named BackgroundChecks.com. Checkr was named by fourteen of the fourteen models and BackgroundChecks.com by eleven and Checkr carries 63 labels and BackgroundChecks.com 20, so the shares are not directly comparable.
Named in two categories this edition.
Named in one category this edition.
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.
Across every category in the November 2026 Edition, Checkr and BackgroundChecks.com were named in the same answer forty-four times, of the 194 answers naming Checkr and the 55 naming BackgroundChecks.com. In those answers BackgroundChecks.com took the first choice three times and Checkr twenty.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Checkr drew 469 of the 1,051 complaints the CFPB received in 2025 about the 8 employment background check company names studied, 44.6%, more than any other screening company” Muse Glimmer 30B · negative prompt · hard negative
“Even large, fully accredited employment background screeners (e.g., *Checkr, First Advantage, HireRight, Sterling*) are legitimate, FCRA-compliant CRAs, but job seekers and employers frequently register complaints about them” Gemini 3.5 Flash · negative prompt · soft negative
“Tops CFPB complaints (469 in 2025, ~45% of total for major firms). Users report frequent inaccuracies, delays in results, and frustrating support.” Grok 4.20 · negative prompt · soft negative
“Checkr is widely considered the "Apple" of background checks—featuring a mobile-first applicant experience, an open API, and aggressive use of AI” Qwen 3.7 Flash · comparative prompt · first choice
“mid-market modern screening platforms (like Checkr, Rippling built-in checks, or GoodHire) often outperform legacy enterprise vendors” Gemini 3.5 Flash · scale prompt · first choice
“I'd recommend starting with Accurate ... or Checkr if you prefer a modern, automation-focused platform. Both are well-suited” Claude Haiku 4.5 · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Allegations of overcharging; holding reports hostage until additional business details are provided” GLM 4.7 FlashX · negative prompt · hard negative
“Trustpilot score ~2.7/5... Complaints center on inaccurate data, poor customer service” Grok 4.20 · negative prompt · hard negative
“BBB customer review ratings average 1/5 stars with multiple scam allegations.” Muse Glimmer 30B · negative prompt · hard negative
“GoodHire or backgroundchecks.com (from the Sterling group) stand out as strong, affordable options” Grok 4.20 · budget prompt · first choice
“Best for Custom Packages ... well-regarded for allowing small businesses to customize exactly what they screen for” Qwen 3.7 Flash · budget prompt · alternative
“backgroundchecks.com is best for small companies needing customizable screening packages” Llama 4 Maverick · comparative prompt · alternative
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.