Deel vs Rippling
Eleven of fourteen models named Deel first on the direct prompt; one named Rippling. Both were named by all fourteen models and Deel carries 64 labels and Rippling 43, so the shares are not directly comparable.
Named in seventeen categories this edition.
Named in twenty-nine categories 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 global payroll and employer of record page.
By framing
DeelFirst choices, of fourteen modelsRippling
Across every category in the October 2026 Edition, Deel and Rippling were named in the same answer 311 times, of the 543 answers naming Deel and the 1329 naming Rippling. In those answers Rippling took the first choice fifty-five times and Deel forty-nine.
Every model, every framing
| 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 | | | | |
Deel Rippling first choice named as an alternative argued againstblank: not namedEach cell is one answer, Deel on the left and Rippling on the right.The direct prompt
Deel first, Rippling an alternative
11 of 14 modelsRippling was named in the answer but not as the choice, or not at all.Rippling first, Deel an alternative
1 of 14 modelsDeel was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.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
What the models said about Deel
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Highest Caution: Active Legal/Ethical Controversies ... Rippling sued Deel alleging corporate espionage” Kimi K2 · negative prompt · hard negative
“Deel dominates the freelancer space but faces scrutiny regarding its EOR services for full-time employees... "billing shocks"...Avoid If: You need flexible, human-negotiated contract terms” Qwen 3.7 Flash · negative prompt · soft negative
“I would put Deel in the "do extra diligence" bucket\u2014not because there's established evidence here of payroll failures, but because of its unusually contentious, ongoing litigation” GPT-6 Luna · negative prompt · soft negative
“repeatedly ranked among the leading EOR providers; it is described as strong for global hiring, compliance, payroll support, and centralized workforce management” Perplexity Sonar · comparative prompt · first choice
“Deel – Best Overall ... If you want the best overall solution: Deel offers the most mature platform with the widest feature set.” Kimi K2 · direct prompt · first choice
“Covers 150+ countries; strong in scalability, contractor management, and compliance. Often top-rated for speed and global reach.” Grok 4.1 Fast · comparative prompt · first choice
What the models said about Rippling
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Highest Caution: Active Legal/Ethical Controversies ... Deel & Rippling” Kimi K2 · negative prompt · hard negative
“list Deel, Remote, Oyster, Skuad, Globalization Partners, Pebl, Atlas, Rippling, and Multiplier as high automation, AI-driven options note they often struggle with hidden fees” Muse Glimmer 30B · negative prompt · soft negative
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.