Background screening · October 2026 Edition
Accurate Background vs Sterling
Two of fourteen models named Accurate Background first on the direct prompt; one named Sterling. Accurate Background was named by fourteen of the fourteen models and Sterling by thirteen and Accurate Background carries 37 labels and Sterling 25, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate11%32%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#6#8A position in a field of 12; printed, not drawn.
Labels3725A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Accurate Background reading right to left. Rank and label count are printed, not drawn.Checkr was named alongside these two in eleven of the fourteen direct answers. Checkr vs Accurate Background · Checkr vs Sterling · GoodHire vs Accurate Background
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.
Accurate BackgroundFirst choices, of fourteen modelsSterling
Direct212 against Sterling
Paraphrase00
Comparative00
Budget-constrained001 against Sterling
Scale-constrained00
Negative014 against Accurate Background · 5 against Sterling
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 October 2026 Edition, Accurate Background and Sterling were named in the same answer forty-two times, of the 91 answers naming Accurate Background and the 93 naming Sterling. In those answers Sterling took the first choice three times and Accurate Background one.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Accurate Background and Sterling stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | | ||||
| Gemini 3.5 Flash | | | ||||
| Perplexity Sonar | | | ||||
| Grok 4.1 Fast | | |||||
| 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 | | | |
Accurate Background Sterling first choice named as an alternative argued againstblank: not namedEach cell is one answer, Accurate Background on the left and Sterling on the right.The direct prompt
The plain question, one answer per model, grouped by where Accurate Background and Sterling stood in it.
Accurate Background first, Sterling not the choice
2 of 14 modelsSterling was named in the answer but not as the choice, or not at all.Claude Haiku 4.5Accurate Background, Checkr alternatives: Mitratech AssureHire
DeepSeek V4 FlashAccurate Background alternatives: Certn, Checkr, GoodHire
Sterling first, Accurate Background not the choice
1 of 14 modelsAccurate Background was named in the answer but not as the choice, or not at all.Grok 4.1 FastSterling alternatives: Checkr, GoodHire, HireRight
Neither was the first choice, one was named
10 of 14 modelsThe answer put something else first and named one of the two as an alternative.Gemini 3.5 FlashCheckr alternatives: Accurate Background, Certn, Verified First
Perplexity SonarCheckr alternatives: Accurate Background, GoodHire, Mitratech AssureHire
Mistral SmallMitratech AssureHire alternatives: Accurate Background, Sterling
Llama 4 MaverickMitratech AssureHire alternatives: Accurate Background, BackgroundChecks.com, Certn, GoodHire, HireShield
Qwen 3.7 FlashGoodHire alternatives: Accurate Background, Checkr, CredCheck, Dun & Bradstreet, Sovren, Verified First
Kimi K2Checkr alternatives: Accurate Background, Certn, GoodHire, iprospectcheck
GLM 4.7 FlashXCertn, Checkr alternatives: Accurate Background, Cisive, GoodHire
MiniMax M2.5Checkr, HireRight alternatives: Accurate Background, GoodHire, IntelliCorp
GPT-6 LunaCheckr alternatives: Accurate Background, First Advantage
Muse Glimmer 30BBchex, Verified First alternatives: Accurate Background, DISA Global Solutions, GoodHire, Mitratech AssureHire, Orange Tree
Neither was named
1 of 14 modelsThe answer made no first choice from these two in this category.GPT-5.4 miniCheckr alternatives: Atlantic Employee Screening, Certn, Hireology
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
Level: the same share of first choices.
Accurate Background0%#5 of 8Sterling0%#7 of 8Mid-marketThe figures above
Accurate Background leads by two points.
Accurate Background4%#6 of 12Sterling2%#8 of 12Enterprise
The order flips: Sterling leads at enterprise.
Sterling13%#4 of 10Accurate Background4%#6 of 10What the models said about Accurate Background
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Trustpilot reviews describe it as "the worst company" with reports taking months” GLM 4.7 FlashX · negative prompt · hard negative
“rely on automated systems to match records, and this process can result in inaccurate or misleading reports” Claude Haiku 4.5 · negative prompt · soft negative
“Multiple negative reviews about extremely long wait times (sometimes over a month)” MiniMax M2.5 · negative prompt · soft negative
“My top recommendation for most mid-market B2B companies: Accurate Background” DeepSeek V4 Flash · direct prompt · first choice
“Accurate Background is best for mid-market employers across multiple industries” Claude Haiku 4.5 · direct prompt · first choice
“Accurate Background - for mid-market companies that need background screening bundled with drug testing and occupational health services.” Llama 4 Maverick · direct prompt · alternative
What the models said about Sterling
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Trustpilot reviews highlight disorganization, incompetence, extremely slow turnaround times (weeks/months), poor customer support” GLM 4.7 FlashX · negative prompt · hard negative
“$4.75 million FCRA class action settlement for providing outdated information to employers” DeepSeek V4 Flash · negative prompt · hard negative
“Recently sued in 2024 for allegedly falsely reporting criminal history and arrest warrants” MiniMax M2.5 · negative prompt · hard negative
“Stick with well-established, accredited providers like Sterling, HireRight, GoodHire, or TransUnion SmartMove” Mistral Small · negative prompt · first choice
“Sterling (now part of First Advantage) \u2013 Strongest fit for mid-market B2B.” Grok 4.1 Fast · direct prompt · first choice
“Best for: Large enterprises needing comprehensive health screenings - Limitations: Quote-based pricing” Kimi K2 · comparative prompt · alternative
Also compared
Checkr vs Accurate BackgroundCheckr vs SterlingGoodHire vs Accurate BackgroundGoodHire vs SterlingBackgroundChecks.com vs Accurate BackgroundBackgroundChecks.com vs SterlingCertn vs Accurate BackgroundCertn vs SterlingVerified First vs Accurate BackgroundVerified First vs SterlingAccurate Background vs HireRightHireRight vs SterlingAll twenty-eight head-to-head pages and the full background screening standing
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.