Finance AI Index
Index Spend and procurement Corporate cards › Rippling Spend vs American Express Corporate Card
Corporate cards · September 2026 Edition

Rippling Spend vs American Express Corporate Card

Zero of twelve models named Rippling Spend first on the direct prompt; zero named American Express Corporate Card. Rippling Spend was named by nine of the twelve models and American Express Corporate Card by nine and Rippling Spend carries 16 labels and American Express Corporate Card 15, so the shares are not directly comparable.

Rippling Spend

accepted challenger

Named in three categories this edition.

Named in one category this edition.

First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%13%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#6A position in a field of 9; printed, not drawn.
Labels1615A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Rippling Spend reading right to left. Rank and label count are printed, not drawn.Ramp was named alongside these two in twelve of the twelve direct answers. Ramp vs Rippling Spend · Ramp vs American Express Corporate Card · BILL Spend & Expense vs Rippling Spend

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 twelve models made each the first choice, per way of asking, and how many argued against it.
Rippling SpendFirst choices, of twelve modelsAmerican Express Corporate Card
Direct00
Paraphrase01
Comparative011 against American Express Corporate Card
Budget-constrained10
Scale-constrained00
Negative001 against American Express Corporate Card
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Rippling Spend and American Express Corporate Card 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
Rippling Spend American Express Corporate Card first choice named as an alternative argued againstblank: not namedEach cell is one answer, Rippling Spend on the left and American Express Corporate Card on the right.

The direct prompt

The plain question, one answer per model, grouped by where Rippling Spend and American Express Corporate Card stood in it.

Neither was the first choice, one was named

2 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Ramp alternatives: Airbase, American Express Corporate Card, Paylocity for Finance, Spendesk
GPT-5.4 miniRamp alternatives: Airwallex, American Express Corporate Card, BILL Spend & Expense

Neither was named

10 of 12 modelsThe answer made no first choice from these two in this category.
Gemini 3.5 FlashRamp alternatives: Airbase, Brex, Corpay
Perplexity SonarRamp alternatives: Airbase, Corpay
Grok 4.1 FastRamp alternatives: BILL Spend & Expense, Brex
Mistral SmallAirbase, Ramp alternatives: Spendesk and Paylocity for Finance, U.S. Bank Commercial Rewards Card
DeepSeek V4 FlashRamp alternatives: Airbase, BILL Spend & Expense
Llama 4 MaverickRamp alternatives: Airbase, U.S. Bank Commercial Rewards Card
Qwen 3.7 FlashRamp alternatives: Airbase, Brex
Kimi K2Ramp alternatives: Airbase, Brex, Corpay
GLM 4.7 FlashXRamp alternatives: Airbase, Brex, Navan, U.S. Bank Commercial Rewards Card
MiniMax M2.5Airbase, Corpay alternatives: BILL Spend & Expense, Brex, Ramp

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.
Rippling Spend0%#– of 6
American Express Corporate Card0%#– of 6
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
Rippling Spend2%#5 of 9
American Express Corporate Card2%#6 of 9
The full mid-market standing →
Enterprise
Rippling Spend leads by two points.
Rippling Spend2%#7 of 10
American Express Corporate Card0%#10 of 10
The full enterprise standing →

What the models said about Rippling Spend

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

“Start with Ramp if you want modern expense management features, or Rippling if you need higher cashback rates” GLM 4.7 FlashX · budget prompt · first choice

What the models said about American Express Corporate Card

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

“Programs/Issuers with Frequent Red Flags: - American Express Corporate Cards: Low BBB rating (1.08/5 from 500+ reviews)” Grok 4.1 Fast · negative prompt · hard negative
“Amex excels in premium travel rewards but lags in automation (manual reports common)” Grok 4.1 Fast · comparative prompt · soft negative
“Choose Amex Corporate if you want premium travel benefits, strong program structure” GPT-5.4 mini · comparative prompt · first choice
“my default recommendation would be American Express Corporate Cards” GPT-5.4 mini · paraphrase prompt · first choice
“usually best if you value premium travel perks ... but it tends to fit larger companies and comes with higher fees” GPT-5.4 mini · direct prompt · alternative
Also compared

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.