Intelligent document processing · October 2026 Edition
Rossum vs ABBYY Vantage
Four of fourteen models named Rossum first on the direct prompt; two named ABBYY Vantage. Both were named by all fourteen models and Rossum carries 37 labels and ABBYY Vantage 38, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
First-choice share16%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate8%24%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#7A position in a field of 11; printed, not drawn.
Labels3738A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Rossum reading right to left. Rank and label count are printed, not drawn.Nanonets was named alongside these two in ten of the fourteen direct answers. Nanonets vs Rossum · Nanonets vs ABBYY Vantage · Rossum vs Docsumo
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 intelligent document processing page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
RossumFirst choices, of fourteen modelsABBYY Vantage
Direct421 against Rossum · 2 against ABBYY Vantage
Paraphrase302 against ABBYY Vantage
Comparative04
Budget-constrained102 against Rossum · 2 against ABBYY Vantage
Scale-constrained00
Negative003 against ABBYY Vantage
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.
Across every category in the October 2026 Edition, Rossum and ABBYY Vantage were named in the same answer seventy-three times, of the 109 answers naming Rossum and the 119 naming ABBYY Vantage. In those answers ABBYY Vantage took the first choice sixteen times and Rossum seven.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Rossum and ABBYY Vantage 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 | | | ||||
| GPT-6 Luna | | | ||||
| Muse Glimmer 30B | | | |
Rossum ABBYY Vantage first choice named as an alternative argued againstblank: not namedEach cell is one answer, Rossum on the left and ABBYY Vantage on the right.The direct prompt
The plain question, one answer per model, grouped by where Rossum and ABBYY Vantage stood in it.
Rossum first, ABBYY Vantage an alternative
4 of 14 modelsABBYY Vantage was named in the answer but not as the choice, or not at all.GPT-5.4 miniRossum alternatives: ABBYY Vantage, Automation Anywhere, Hyperscience Hypercell, UiPath Document Understanding
Mistral SmallRossum, UiPath Document Understanding alternatives: Flowwright, Nanonets, OpenText Intelligent Document Processing
Qwen 3.7 FlashRossum alternatives: Nanonets, UiPath Document Understanding
Muse Glimmer 30BRossum alternatives: Doxis AI.dp, Klippa DocHorizon, Nanonets
ABBYY Vantage first, Rossum an alternative
2 of 14 modelsRossum was named in the answer but not as the choice, or not at all.Grok 4.1 FastABBYY Vantage alternatives: Amazon Textract, Google Document AI, Rossum, UiPath Document Understanding
GPT-6 LunaABBYY Vantage alternatives: Azure AI Document Intelligence, Google Document AI
Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Claude Haiku 4.5Nanonets alternatives: ABBYY Vantage, Amazon Textract, Rossum
Gemini 3.5 FlashNanonets alternatives: Affinda, Docsumo, Rossum
Perplexity SonarNanonets alternatives: ABBYY Vantage, Automation Anywhere, Rossum, UiPath Document Understanding
DeepSeek V4 FlashDocsumo, Nanonets alternatives: ABBYY Vantage, Rossum
Kimi K2Nanonets alternatives: ABBYY Vantage, Rossum, UiPath Document Understanding
GLM 4.7 FlashXNanonets alternatives: DigiParser, Rossum
MiniMax M2.5no first choice alternatives: ABBYY Vantage, Amazon Textract, Automation Anywhere, Azure AI Document Intelligence, Docsumo, UiPath Document Understanding
Neither was named
1 of 14 modelsThe answer made no first choice from these two in this category.Llama 4 MaverickNanonets
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
Rossum leads by two points.
Rossum4%#7 of 14ABBYY Vantage2%#10 of 14Mid-marketThe figures above
Rossum leads by twelve points.
Rossum16%#2 of 11ABBYY Vantage4%#7 of 11Enterprise
The order flips: ABBYY Vantage leads at enterprise.
ABBYY Vantage43%#1 of 10Rossum9%#3 of 10What the models said about Rossum
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Avoid enterprise platforms like ABBYY, Rossum, or UiPath for now” Qwen 3.7 Flash · budget prompt · hard negative
“Rossum is worth evaluating, but it was acquired by Coupa on May 12, 2026. I'd check product roadmap, contract terms, and integration support” GPT-6 Luna · direct prompt · soft negative
“dramatically cheaper than enterprise IDP platforms like Rossum ($18,000+/year minimum)” DeepSeek V4 Flash · budget prompt · soft negative
“Rossum is the most cited mid-market pick. Low-code invoice workflows, usable review flows plus workflow automation, from $18,000/year.” Muse Glimmer 30B · direct prompt · first choice
“Rossum - Offers low-code invoice workflows and usable review flows plus workflow automation, starting at $18,000/year.” Llama 4 Maverick · budget prompt · first choice
“UiPath and Rossum are top choices due to their accuracy, scalability, and ease of integration” Mistral Small · direct prompt · first choice
What the models said about ABBYY Vantage
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid enterprise platforms like ABBYY, Rossum, or UiPath for now, as their annual minimums ($18k+)” Qwen 3.7 Flash · budget prompt · hard negative
“Avoid ABBYY/UiPath/Hyperscience unless you have dedicated IT resources” Kimi K2 · paraphrase prompt · hard negative
“users commonly praise accuracy/integration, but also report complex initial setup, OCR issues, and workflow configuration challenges” GPT-5.4 mini · negative prompt · soft negative
“A Gartner Magic Quadrant Leader with 35+ years of heritage and 200+ pre-trained document types... best for large enterprises with diverse document types” Claude Haiku 4.5 · comparative prompt · first choice
“The safest enterprise choice for breadth — 35+ year heritage, 150–200+ pre-trained document "skills," 200+ language support.” DeepSeek V4 Flash · comparative prompt · first choice
“If you need enterprise governance and analytics, ABBYY Vantage and Hyperscience lead for regulated, high-volume work.” Muse Glimmer 30B · comparative prompt · first choice
Also compared
Nanonets vs RossumNanonets vs ABBYY VantageRossum vs DocsumoRossum vs Google Document AIRossum vs UiPath Document UnderstandingRossum vs Amazon TextractDocsumo vs ABBYY VantageGoogle Document AI vs ABBYY VantageUiPath Document Understanding vs ABBYY VantageAmazon Textract vs ABBYY VantageAll twenty-one head-to-head pages and the full document processing 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.