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Corporate cards · October 2026 Edition

Ramp vs Rippling Spend

Seven of fourteen models named Ramp first on the direct prompt; one named Rippling Spend. Ramp was named by fourteen of the fourteen models and Rippling Spend by eight and Ramp carries 64 labels and Rippling Spend 10, so the shares are not directly comparable.

Ramp

endorsed leader

Named in seventeen categories this edition.

Rippling Spend

accepted challenger

Named in three categories this edition.

First-choice share52%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate11%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#6A position in a field of 8; printed, not drawn.
Labels6410A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Ramp reading right to left. Rank and label count are printed, not drawn.BILL Spend & Expense was named alongside these two in eight of the fourteen direct answers. Ramp vs BILL Spend & Expense · Ramp vs Brex · Ramp vs Corpay

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 corporate cards page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
RampFirst choices, of fourteen modelsRippling Spend
Direct711 against Ramp
Paraphrase100
Comparative80
Budget-constrained901 against Ramp
Scale-constrained40
Negative205 against Ramp
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 Ramp and Rippling Spend stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Ramp Rippling Spend first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ramp on the left and Rippling Spend on the right.

The direct prompt

The plain question, one answer per model, grouped by where Ramp and Rippling Spend stood in it.

Both were the first choice

1 of 14 modelsThe answer named them together, and the judge labeled each a first choice.
DeepSeek V4 FlashRamp, Rippling Spend alternatives: Airbase, BILL Spend & Expense, Brex, U.S. Bank Commercial Rewards Card

Ramp first, Rippling Spend not the choice

6 of 14 modelsRippling Spend was named in the answer but not as the choice, or not at all.
GPT-5.4 miniRamp alternatives: BILL Spend & Expense, Brex, Rippling Finance
Gemini 3.5 FlashRamp alternatives: Airbase, BILL Spend & Expense, Brex
Perplexity SonarCorpay, Ramp alternatives: BILL Spend & Expense, Navan, U.S. Bank Commercial Rewards Card
Grok 4.1 FastRamp alternatives: BILL Spend & Expense, Brex, Corpay, Payhawk
Kimi K2Ramp alternatives: Brex, Corpay
GPT-6 LunaRamp alternatives: BILL Spend & Expense, Brex

Neither was the first choice, one was named

5 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5no first choice alternatives: Airbase, BILL Spend & Expense, Ramp, The BILL Divvy Card, Volopay Card
Mistral SmallAirbase, Brex alternatives: Corpay, Ramp
Qwen 3.7 FlashU.S. Bank Commercial Rewards Card alternatives: BILL Spend & Expense, Rho, Rippling Spend
GLM 4.7 FlashXAirbase, Corpay alternatives: Airwallex, American Express Business Platinum, Ramp
MiniMax M2.5Corpay alternatives: Airbase, Ramp

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Llama 4 Maverickno first choice
Muse Glimmer 30BCorpay alternatives: Airbase, U.S. Bank Commercial Rewards Card

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
Ramp leads by sixty-six points.
Ramp68%#1 of 7
Rippling Spend2%#– of 7
The full small business standing →
Mid-marketThe figures above
Ramp leads by fifty points.
Ramp52%#1 of 8
Rippling Spend2%#6 of 8
The full mid-market standing →
Enterprise
Ramp leads by thirty-two points.
Ramp34%#1 of 11
Rippling Spend2%#8 of 11
The full enterprise standing →

What the models said about Ramp

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

“Ramp and Brex are strong for spend controls but "lack the managed service layer and vendor network depth that larger organizations need"” Muse Glimmer 30B · direct prompt · soft negative
“If you want a truly fee‑free, easy‑to‑manage card with solid integration, Ramp, Brex, and Divvy are frequently mentioned by small businesses.” MiniMax M2.5 · budget prompt · first choice

What the models said about Rippling Spend

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

“Ramp or Rippling Spend offer the best balance of zero cost, strong automation, corporate liability, and meaningful cashback” DeepSeek V4 Flash · direct prompt · first choice
“If you use Rippling for HR, they can instantly issue and revoke cards based on an employee's employment status” Gemini 3.5 Flash · scale prompt · alternative
Also compared
Ramp vs BILL Spend & ExpenseRamp vs BrexRamp vs CorpayRamp vs AirbaseBILL Spend & Expense vs Rippling SpendBrex vs Rippling SpendCorpay vs Rippling SpendAirbase vs Rippling SpendAll fifteen head-to-head pages and the full corporate cards 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.