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Intelligent document processing · November 2026 Edition

ABBYY Vantage vs Amazon Textract

Two of fourteen models named ABBYY Vantage first on the direct prompt; zero named Amazon Textract. ABBYY Vantage was named by fourteen of the fourteen models and Amazon Textract by twelve and ABBYY Vantage carries 50 labels and Amazon Textract 27, so the shares are not directly comparable.

ABBYY Vantage

criticized challenger

Named in one category this edition.

Amazon Textract

criticized challenger

Named in one category this edition.

First-choice share6%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate28%30%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#8A position in a field of 13; printed, not drawn.
Labels5027A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, ABBYY Vantage reading right to left. Rank and label count are printed, not drawn.Rossum was named alongside these two in eleven of the fourteen direct answers. Nanonets vs ABBYY Vantage · Nanonets vs Amazon Textract · Rossum vs ABBYY Vantage

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.
ABBYY VantageFirst choices, of fourteen modelsAmazon Textract
Direct203 against ABBYY Vantage · 1 against Amazon Textract
Paraphrase102 against ABBYY Vantage
Comparative801 against Amazon Textract
Budget-constrained026 against ABBYY Vantage · 2 against Amazon Textract
Scale-constrained00
Negative003 against ABBYY Vantage · 4 against Amazon Textract
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 November 2026 Edition, ABBYY Vantage and Amazon Textract were named in the same answer sixty-five times, of the 143 answers naming ABBYY Vantage and the 89 naming Amazon Textract. In those answers Amazon Textract took the first choice one time and ABBYY Vantage sixteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where ABBYY Vantage and Amazon Textract stood in it.
ModelDirectATParaphraseATComparativeATBudget-constrainedATScale-constrainedATNegativeAT
Claude Haiku 4.5AT
GPT-5.4 miniAT
Gemini 3.5 FlashATATATATAT
Perplexity SonarATATAT
Grok 4.20
Mistral Small
DeepSeek V4 FlashAT
Llama 4 Maverick
Qwen 3.7 FlashATATAT
Kimi K2AT
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 LunaATAT
Muse Glimmer 30B
ABBYY VantageAT Amazon Textract first choice named as an alternative argued againstblank: not namedEach cell is one answer, ABBYY Vantage on the left and Amazon Textract on the right.

The direct prompt

The plain question, one answer per model, grouped by where ABBYY Vantage and Amazon Textract stood in it.

ABBYY Vantage first, Amazon Textract not the choice

2 of 14 modelsAmazon Textract was named in the answer but not as the choice, or not at all.
GPT-5.4 miniABBYY Vantage alternatives: AWS IDP, Azure AI Document Intelligence, Rossum
Mistral SmallABBYY Vantage, Rossum alternatives: Apryse, Turian

Neither was the first choice, one was named

6 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashNanonets alternatives: ABBYY Vantage, Docsumo, Rossum
Kimi K2Rossum alternatives: ABBYY Vantage, Microsoft Azure AI Document Intelligence, Nanonets
GLM 4.7 FlashXUiPath Document Understanding alternatives: ABBYY Vantage, Automation Anywhere Intelligent Automation, Microsoft Azure AI Document Intelligence, ServiceNow Document Management
MiniMax M2.5Nanonets alternatives: ABBYY Vantage, Docsumo, Hyperscience Hypercell, Rossum, UiPath Document Understanding
GPT-6 LunaRossum alternatives: ABBYY Vantage, Nanonets, UiPath Document Understanding
Muse Glimmer 30BNanonets, Rossum alternatives: ABBYY Vantage, Automation Anywhere, Google Document AI, UiPath Document Understanding

Neither was named

6 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Nanonets alternatives: Doxis AI.dp, Rossum
Perplexity SonarNanonets alternatives: Docsumo, Microsoft Azure AI Document Intelligence, Parseur
Grok 4.20Rossum alternatives: ABBYY Vantage or FlexiCapture, Docsumo, Hyperscience Hypercell, UiPath Document Understanding
DeepSeek V4 FlashDocsumo alternatives: Nanonets, Rossum
Llama 4 MaverickNanonets alternatives: Apryse PDF SDK, Parseur
Qwen 3.7 FlashNanonets alternatives: Extend, Rossum, Turian

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
ABBYY Vantage leads by two points.
ABBYY Vantage2%#6 of 12
Amazon Textract0%#9 of 12
The full small business standing →
Mid-marketThe figures above
ABBYY Vantage leads by two points.
ABBYY Vantage6%#5 of 13
Amazon Textract4%#8 of 13
The full mid-market standing →
Enterprise
ABBYY Vantage leads by thirty-eight points.
ABBYY Vantage38%#1 of 11
Amazon Textract0%#10 of 11
The full enterprise standing →

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.

“What to avoid if you're on a tight budget - ABBYY, Hyperscience, Rossum, UiPath: These are enterprise-grade but quote-only” DeepSeek V4 Flash · budget prompt · hard negative
“Avoid If Budget Is Tight ... ABBYY Vantage | Enterprise quotes | Mid-market to enterprise focus” Kimi K2 · budget prompt · hard negative
“Avoid enterprise tools like ABBYY Vantage, UiPath, Rossum, or Hyperscience unless you scale up” Grok 4.20 · budget prompt · hard negative
“Need an enterprise IDP platform with broad document and process capabilities? Start with ABBYY Vantage, Hyperscience, and Tungsten TotalAgility.” GPT-6 Luna · comparative prompt · first choice
“If you want the best standalone IDP specialist, ABBYY, Hyperscience, and Rossum are usually near the top of the conversation.” GPT-5.4 mini · comparative prompt · first choice
“Deep OCR + pre‑trained models; leader in Gartner Magic Quadrant and Quadrant Knowledge Solutions SPARK Matrix” GLM 4.7 FlashX · comparative prompt · first choice

What the models said about Amazon Textract

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 if you do not have a dedicated DevOps team to maintain the code.” Qwen 3.7 Flash · negative prompt · hard negative
“Amazon Textract as a standalone, turnkey IDP platform. Treat it as a document-processing building block: you may need to add workflow orchestration, validation, and exception handling yourself.” GPT-6 Luna · negative prompt · soft negative
“Google Document AI, Amazon Textract, and Azure Document Intelligence can provide strong extraction, but you may need to build validation, exception queues... They are risky choices” Perplexity Sonar · negative prompt · soft negative
“"I have a developer" | Amazon Textract or Google Document AI | Rawest price per page ($0.0015). No markup.” Qwen 3.7 Flash · budget prompt · first choice
“Amazon Textract is often the safest budget-conscious choice for teams that want a scalable IDP foundation” GPT-5.4 mini · budget prompt · first choice
“Developers usually prefer AWS Textract or Azure AI Document Intelligence APIs to integrate into custom apps” Gemini 3.5 Flash · comparative 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.