AI accounting assistants · September 2026 Edition
Ramp vs Vic.ai
Zero of twelve models named Ramp first on the direct prompt; one named Vic.ai. Ramp was named by nine of the twelve models and Vic.ai by eleven and Ramp carries 15 labels and Vic.ai 34, so the shares are not directly comparable.
Named in seventeen categories this edition.
Named in three categories this edition.
First-choice share9%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate7%6%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#4A position in a field of 14; printed, not drawn.
Labels1534A 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.Sage Intacct was named alongside these two in eleven of the twelve direct answers. Sage Intacct vs Ramp · Sage Intacct vs Vic.ai · Zoho Books vs Ramp
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 AI accounting assistants page.
By framing
How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
RampFirst choices, of twelve modelsVic.ai
Direct01
Paraphrase42
Comparative001 against Vic.ai
Budget-constrained00
Scale-constrained00
Negative001 against Ramp · 1 against Vic.ai
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 Ramp and Vic.ai 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 | | | |
Ramp Vic.ai first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ramp on the left and Vic.ai on the right.The direct prompt
The plain question, one answer per model, grouped by where Ramp and Vic.ai stood in it.
Vic.ai first, Ramp not the choice
1 of 12 modelsRamp was named in the answer but not as the choice, or not at all.Claude Haiku 4.5Vic.ai alternatives: Booke AI, DualEntry, Integra Balance AI, Sage Intacct
Neither was the first choice, one was named
8 of 12 modelsThe answer put something else first and named one of the two as an alternative.Gemini 3.5 FlashLedge, Numeric alternatives: Ramp, Tabs, Vic.ai
Perplexity SonarSage Intacct alternatives: DualEntry, Numeric, Vic.ai
Mistral SmallSage Intacct alternatives: Truewind, Vic.ai
DeepSeek V4 FlashRho, Sage Intacct alternatives: Oracle NetSuite, Vic.ai, Zeni
Llama 4 MaverickSage Intacct alternatives: Truewind, Vic.ai
Qwen 3.7 FlashDualEntry alternatives: Sage Intacct, Vic.ai
Kimi K2Sage Intacct alternatives: DualEntry, Numeric, Vic.ai
MiniMax M2.5Sage Intacct alternatives: DualEntry, Truewind, Vic.ai
Neither was named
3 of 12 modelsThe answer made no first choice from these two in this category.GPT-5.4 miniSage Intacct alternatives: FloQast, Hyperbots A/P Suite, Microsoft Dynamics 365 Business Central, Oracle NetSuite
Grok 4.1 FastSage Intacct alternatives: Docyt, Oracle NetSuite, QuickBooks Online, Xero
GLM 4.7 FlashXSage Intacct alternatives: Docyt, Zeni
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.
Ramp0%#– of 11Vic.ai0%#10 of 11Mid-marketThe figures above
Enterprise
The order flips: Vic.ai leads at enterprise.
Vic.ai12%#1 of 8Ramp2%#– of 8What the models said about Ramp
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Researchers found a prompt-injection vulnerability where hidden instructions inside a spreadsheet could trick the AI into exfiltrating sensitive financial data” DeepSeek V4 Flash · negative prompt · soft negative
“I would recommend starting with Ramp Intelligence if you need broad spend management capabilities, or Ledge if your primary focus is month-end close automation” MiniMax M2.5 · paraphrase prompt · first choice
“Ramp is the safest recommendation because it covers the broadest operational accounting surface area while still being explicitly positioned for mid-market teams” Perplexity Sonar · paraphrase prompt · first choice
What the models said about Vic.ai
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Ranked low in privacy audits ... they train AI on customer data without easy opt-out” Grok 4.1 Fast · negative prompt · soft negative
“Vic.ai requires significant invoice volume to justify its cost” Qwen 3.7 Flash · comparative prompt · soft negative
“Vic.ai is recommended for mid-market or enterprise AP teams, as it's autonomous invoice processing for organizations processing thousands of bills every month” Claude Haiku 4.5 · direct prompt · first choice
“For most mid-sized B2B companies, Vic.ai is the top recommendation due to its specialized focus on AP automation and proven scalability.” Mistral Small · paraphrase prompt · first choice
“I recommend starting with Vic.ai for accounts payable and Docyt for end‑to‑end bookkeeping.” GLM 4.7 FlashX · paraphrase prompt · first choice
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.