Data as of Aug 25, 2026 · Based on 1,226 AI responses · See how Parse measures this
AI Document Parsing & Extraction APIs
ParseChatGPT + Google AIParse
https://parse.gl
Measured across ChatGPT & Google AI
Mailgun| # | Brand | What AI says | Mention rate |
|---|---|---|---|
| 1 |  Alphabet | 43% | |
| 2 |  Microsoft | 38% | |
| 3 |  ParseurVersatile, no-code AI parser for extracting data from emails and attachments. | Versatile, no-code AI parser for extracting data from emails and attachments. | 38% |
| 4 |  Amazon | 33% | |
| 5 |  Docparser | 27% | |
| 6 |  Airparser | 25% | |
| 7 |  Nanonets | 24% | |
| 8 |  Parsio | 21% | |
| 9 |  Mailparser | 17% | |
| 10 | MailgunPreferred for high-volume, developer-centric  Inbound email routing and parsing pipelines. | Preferred for high-volume, developer-centric Inbound email routing and parsing pipelines. | 16% |
| 11 |  Tabula | 16% | |
| 12 |  Zapier | 15% | |
| 13 |  Rossum | 15% | |
| 14 |  Reducto | 14% | |
| 15 | LlamaIndex | 13% | |
| 16 |  camelot-py | 13% | |
| 17 |  Abbyy | 13% | |
| 18 |  UnstructuredLibrary optimized for partitioning documents into tables, headers, and text blocks. | Library optimized for partitioning documents into tables, headers, and text blocks. | 9% |
| 19 |  Docsumo | 9% | |
| 20 |  Energent AI | 8% | |
| 21 |  Mindee | 8% | |
| 22 |  Nylas | 8% | |
| 23 |  Adobe | 8% | |
| 24 |  PDFPlumber | 7% | |
| 25 |  AlgoDocs | 6% |
Who wins on each AI
ChatGPT tends to favor developer-oriented APIs while Google AI Overviews prioritizes no-code and ready-to-integrate business tools for the same parsing requirements.
Sources AI cited
parseur.com is the page AI reaches for most here, cited in 42% of analyzed answers.
Mention rate rank dropped from #1 to #7 in this ranking between Oct 2025 and Aug 2026
Rose from #19 to #1 in mention-rate rankings between Oct 2025 and Aug 2026
“Emerging table-parsing tool” → “Standard choice for RAG and LLM document ingestion”
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
 Google Cloud Document AI | 53% | 46% | |
 Azure AI Document Intelligence | 50% | 37% | |
| LlamaParse | 35% | 37% | |
 Amazon Textract | 49% | 23% | |
| Parseur | 0% | 29% |
The two models disagree most about LlamaIndex (ChatGPT #21, Google #5) and Mailgun (ChatGPT #8, Google #20).
LlamaParse currently anchors the AI document parsing landscape as the top-cited solution for developers and enterprises alike. This niche remains heavily contested, with Mailgun and Docling closely following in mention rates as of August 2026.
Across 1,226 AI responses, Alphabet is mentioned most, named in 43% of them, followed by Microsoft (38%) and Parseur (38%).
Parse measures each brand's mention rate — the share of answers naming it — across 1,226 AI responses to this market's buyer questions. Answers are collected daily and the ranking is published weekly.
Brands enter the ranking when AI answers mention them. Parse collects answers daily and publishes the re-measured set weekly, so new brands appear as AI starts recommending them.
MailgunParseurThe discussion shifted from simple rule-based email parsers to developer-first APIs and AI-enriched ingestion pipelines. ChatGPT and Google AI Overviews consistently highlight
My goal is to parse and process incoming emails automatically. What is the best email parsing API?
The discussion shifted from simple rule-based email parsers to developer-first APIs and AI-enriched ingestion pipelines. ChatGPT and Google AI Overviews consistently highlight
LlamaParseDoclingAI tools for table preservation have evolved from local Python libraries like Camelot to robust cloud APIs and layout-aware models. Platforms like emerged as the current leaders for table-preserving parsing in 2026.
Brands mentioned
LlamaParseAI tools for table preservation have evolved from local Python libraries like Camelot to robust cloud APIs and layout-aware models. Platforms like Docling emerged as the current leaders for table-preserving parsing in 2026.
