Payactiv vs Rain
One of fourteen models named Payactiv first on the direct prompt; two named Rain. Payactiv was named by fourteen of the fourteen models and Rain by thirteen and Payactiv carries 51 labels and Rain 27, so the shares are not directly comparable.
Named in two categories this edition.
Named in two 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 earned wage access page.
By framing
PayactivFirst choices, of fourteen modelsRain
Across every category in the October 2026 Edition, Payactiv and Rain were named in the same answer sixty-seven times, of the 192 answers naming Payactiv and the 85 naming Rain. In those answers Rain took the first choice three times and Payactiv five.
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 | | |
Payactiv Rain first choice named as an alternative argued againstblank: not namedEach cell is one answer, Payactiv on the left and Rain on the right.The direct prompt
Payactiv first, Rain not the choice
1 of 14 modelsRain was named in the answer but not as the choice, or not at all.Rain first, Payactiv an alternative
2 of 14 modelsPayactiv was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
9 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
2 of 14 modelsThe answer made no first choice from these two in this category.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
Payactiv0%#5 of 7Rain0%#6 of 7Payactiv4%#4 of 10Rain4%#5 of 10What the models said about Payactiv
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“A long-standing player in the space that faces criticism for aggressive collection tactics on debts.” Qwen 3.7 Flash · negative prompt · hard negative
“exercise extreme caution with: EarnIn, MoneyLion, DailyPay, Empower, and PayActiv” DeepSeek V4 Flash · negative prompt · hard negative
“Avoid enterprise-focused ones like DailyPay or Payactiv if budget-tight” Grok 4.1 Fast · budget prompt · hard negative
“Start with Payactiv or Tapcheck for mid-market B2B—they balance cost, reviews, and fit without enterprise complexity.” Grok 4.1 Fast · direct prompt · first choice
“stick with employer‑integrated options (e.g., DailyPay, Payactiv, Branch Core)” MiniMax M2.5 · negative prompt · first choice
“such as DailyPay or Payactiv), it is generally safer than direct-to-consumer apps” Gemini 3.5 Flash · negative prompt · first choice
What the models said about Rain
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Rain - provides a payroll-integrated earned wage access solution designed for mid-market and enterprise employers.” Llama 4 Maverick · direct prompt · first choice
“ZayZoon and Rain are widely considered the top choices for mid-market B2B companies” Qwen 3.7 Flash · direct prompt · first choice
“Rain and Immediate use employer-pays models ... which is the cleanest from a regulatory and employee-relations standpoint” Claude Haiku 4.5 · 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.