Intelligent document processing · October 2026 Edition
Rossum vs Google Document AI
Four of fourteen models named Rossum first on the direct prompt; zero named Google Document AI. Rossum was named by fourteen of the fourteen models and Google Document AI by twelve and Rossum carries 37 labels and Google Document AI 23, so the shares are not directly comparable.
Named in one category this edition.
Google Document AI
accepted challengerNamed in one category this edition.
First-choice share16%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate8%13%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 11; printed, not drawn.
Labels3723A 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 Google Document AI · 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 modelsGoogle Document AIDirect401 against Rossum
Paraphrase30
Comparative001 against Google Document AI
Budget-constrained132 against Rossum
Scale-constrained00
Negative002 against Google Document AI
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 Google Document AI were named in the same answer thirty-eight times, of the 109 answers naming Rossum and the 78 naming Google Document AI. In those answers Google Document AI took the first choice two times and Rossum three.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Rossum and Google Document AI stood in it.
| Model | DirectGD | ParaphraseGD | ComparativeGD | Budget-constrainedGD | Scale-constrainedGD | NegativeGD |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | GD | ||||
| GPT-5.4 mini | | GD | ||||
| Gemini 3.5 Flash | | | | GD | GD | |
| Perplexity Sonar | | |||||
| Grok 4.1 Fast | GD | | GD | |||
| Mistral Small | | |||||
| DeepSeek V4 Flash | | GD | GD | GD | ||
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | GD | | ||
| Kimi K2 | | | ||||
| GLM 4.7 FlashX | | | | GD | ||
| MiniMax M2.5 | | GD | ||||
| GPT-6 Luna | GD | GD | GD | |||
| Muse Glimmer 30B | | | GD |
RossumGD Google Document AI first choice named as an alternative argued againstblank: not namedEach cell is one answer, Rossum on the left and Google Document AI on the right.The direct prompt
The plain question, one answer per model, grouped by where Rossum and Google Document AI stood in it.
Rossum first, Google Document AI not the choice
4 of 14 modelsGoogle Document AI 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
Neither was the first choice, one was named
8 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
Grok 4.1 FastABBYY Vantage alternatives: Amazon Textract, Google Document AI, 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
GPT-6 LunaABBYY Vantage alternatives: Azure AI Document Intelligence, Google Document AI
Neither was named
2 of 14 modelsThe answer made no first choice from these two in this category.Llama 4 MaverickNanonets
MiniMax M2.5no first choice alternatives: ABBYY Vantage, Amazon Textract, Automation Anywhere, Azure AI Document Intelligence, Docsumo, UiPath Document Understanding
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
Google Document AI leads by four points.
Google Document AI8%#4 of 14
Rossum4%#7 of 14Mid-marketThe figures above
The order flips: Rossum leads at mid-market.
Rossum16%#2 of 11Google Document AI6%#4 of 11
The full mid-market standing →Enterprise
Rossum leads by nine points.
Rossum9%#3 of 10Google Document AI0%#8 of 10
The full enterprise standing →What 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 Google Document AI
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“center on managed analysis APIs, so buyers assemble most of the operational queue, review, routing, and export application around them” Claude Haiku 4.5 · comparative prompt · soft negative
“Users report pricing issues that hinder adoption for smaller businesses; costs can escalate unpredictably” DeepSeek V4 Flash · negative prompt · soft negative
“Google Cloud Document AI” Gemini 3.5 Flash · negative prompt · soft negative
“My practical pick: Google Document AI if you want a strong cloud-native option with a free tier and transparent usage pricing.” GPT-5.4 mini · budget prompt · first choice
“Go with Google Cloud Document AI or Azure AI Document Intelligence. Their free tiers are highly generous” Gemini 3.5 Flash · budget prompt · first choice
“I would suggest starting with Google Cloud Document AI or Amazon Textract” MiniMax M2.5 · budget prompt · first choice
Also compared
Nanonets vs RossumNanonets vs Google Document AIRossum vs DocsumoRossum vs UiPath Document UnderstandingRossum vs Amazon TextractRossum vs ABBYY VantageDocsumo vs Google Document AIGoogle Document AI vs UiPath Document UnderstandingGoogle Document AI vs Amazon TextractGoogle Document AI 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.