LLMWhisperer vs. Mistral OCR: The Best Mistral AI OCR Alternative
Table of Contents
Introduction
The rapid evolution of large language models (LLMs) has sparked a wave of innovation in how we process and understand documents. Traditional OCR tools, once limited to plain text extraction, are now being augmented, or replaced entirely, by LLM-powered systems that can interpret complex layouts, extract structured data, and even understand the semantic context of scanned pages.
One of the recent players in this space is Mistral OCR, which has gained attention for its fast, markdown-centric extraction capabilities and its ability to turn PDFs into clean, readable text.
On the other side, we have LLMWhisperer, a component of the Unstract platform, purpose-built to go beyond basic text capture, preserving layout, identifying tables, detecting form fields like checkboxes and radio buttons, and handling challenging elements like handwriting, all while minimizing hallucinations.
Why does this comparison matter now?
As businesses increasingly rely on automation to process invoices, forms, and other document-heavy workflows, the tools they choose must deliver reliable, structured, and trustworthy outputs.
And while Mistral OCR is a good general-purpose solution, we wanted to see how it performs against LLMWhisperer in real-world scenarios where accuracy and structure are critical.
In this article, we’ll put both tools to the test using a series of challenging sample documents. We’ll explore their outputs and wrap up with practical recommendations for which tool to use and when.
Overview of Mistral OCR
Mistral OCR is a Character Recognition API developed by Mistral AI, designed to deliver high-fidelity understanding of complex documents. Unlike traditional OCR systems that focus solely on extracting text, Mistral OCR treats documents as multimodal entities, it is capable of parsing images, tables, mathematical expressions, and structured layouts.
Here is the Github repository where you will find all the codes written for this article.
Capabilities and Common Use Cases
Mistral OCR is suited for large-scale document processing where speed and multilingual capabilities are critical.
It supports a wide array of use cases including:
Scientific literature parsing – turning PDFs with equations, figures, and charts into structured outputs.
Customer support knowledge bases – transforming manuals and technical documentation into indexed, answer-ready content.
Cultural heritage preservation – digitizing historical documents and making them AI-readable.
Legal and regulatory compliance – extracting structured data from filings, contracts, and scanned forms.
Output Format: Markdown-Centric
One of Mistral OCR’s key design philosophies is delivering output in markdown format, a readable, structured representation of the original document that interleaves text and imagery.
This makes the outputs usable for downstream processing or human-in-the-loop review.
The structured markdown format includes:
Headings and subheadings
Table representations
Embedded figures/images
Lists and annotations
Equations as LaTeX blocks or images
Limitations
Despite its speed and general versatility, Mistral OCR has some limitations, particularly in enterprise or document automation contexts where fidelity to original formatting and structured data extraction is crucial:
Layout fidelity: Markdown output often loses precise spatial alignment of elements, making it unsuitable for pixel-accurate extraction (e.g., scanned forms).
Handwriting recognition is limited: The model performs best with printed text; cursive or messy handwriting often fails to produce meaningful output.
Potential hallucinations: Like many LLM-based tools, Mistral OCR can sometimes invent structure or mislabel document elements when confident data isn’t present.
Overview of LLMWhisperer
LLMWhisperer is Unstract’s flagship document understanding engine, an advanced, production-grade pipeline purpose-built for structured data extraction from unstructured or messy document inputs.
Positioned at the heart of the Unstract ecosystem, LLMWhisperer powers the most demanding use cases in document automation, compliance, and AI-native data ingestion.
Where traditional OCR tools aim to transcribe what they see, LLMWhisperer goes further: it interprets, structures, and contextualizes information, enabling downstream systems to take meaningful action with minimal human clean-up.
Seamless Integration with Unstract
LLMWhisperer is tightly integrated into the broader Unstract platform, benefiting from upstream capabilities like intelligent document routing, page classification, and post-processing agents.
Get started with Unstract
Unstract is an open-source, no-code platform for automating document-heavy workflows. It converts unstructured data—like invoices, ID cards, or contracts—into clean JSON for easy integration and automation.
This allows organizations to process large volumes of heterogeneous documents, such as scanned PDFs, printed forms, handwritten notes, and mixed-layout contracts, while receiving clean, structured outputs.
This integration expands LLMWhisperer capabilities to offer multiple structured output formats based on the downstream need:
JSON: For programmatic consumption in automation or AI pipelines.
HTML: For human review with high-fidelity visual formatting.
Markdown: Lightweight and developer-friendly text representation.
CSV: Ideal for tabular data export and integration with spreadsheets or databases.
Designed for Structured Extraction from Messy Inputs
LLMWhisperer excels in scenarios where traditional OCR models degrade: low-quality scans, inconsistent layouts, partially handwritten content, or form elements with no textual labels.
It uses layout-aware parsing, spatial grouping, and LLM-driven heuristics to not just “read” a document, but to understand its structure and intent.
Key Strengths
Layout Preservation: Outputs retain positional relationships between elements (columns, headers, nested blocks), enabling pixel-accurate extraction.
Form Field Extraction: Checkboxes, radio buttons, input fields, and handwritten annotations are detected, labeled, and converted into structured fields.
Table Fidelity: Complex tables, including nested rows, spanning cells, and irregular shapes, are reconstructed as accurate outputs.
Handwriting Recognition: Incorporates specialized handwriting detection and transcription modules, making it reliable for forms filled out by hand.
Hallucination Resistance: Built-in guardrails prevent the model from inventing content not present in the original document, a common pitfall in LLM-powered tools.
In essence, LLMWhisperer brings precision, structure, and reliability to the document understanding space, especially when dealing with real-world documents that are messy, inconsistent, or rich in layout and form semantics.
Test Methodology
To compare Mistral OCR and LLMWhisperer, we conducted hands-on evaluations using real-world, challenging documents designed to stress the capabilities of each system.
Our goal was not only to assess raw text extraction but also to evaluate how well each tool preserves document structure, handles non-textual elements, and avoids common LLM pitfalls such as hallucination.
Document Types Used
We selected a diverse set of input documents that reflect common enterprise use cases, including:
Scanned Forms: Multi-page PDF forms with mixed content, printed labels, filled-in fields, checkboxes, and signatures.
Handwritten Form: Low-resolution scans of handwritten forms, and fill-in-the-blank fields.
Complex Tables: Tabular documents with merged headers, irregular row spans, and embedded text blocks (e.g. financial statements).
Layout-rich Documents: Structured PDFs with columns, figures, and mixed visual hierarchies.
Excel documents: Excel is still use in many organizations as a source of data.
Evaluation Criteria
We used five key evaluation metrics to score and compare each tool’s performance:
Criterion
Evaluation Questions
Layout Fidelity
– Is the spatial arrangement (e.g., columns, alignment, section groupings) accurately preserved? – Are elements like headers, footnotes, and sidebars handled intelligently?
Table Structure
– Are complex tables reconstructed with correct row/column relationships? – How are merged cells, nested tables, or split content handled?
Radio/Checkbox Detection
– Are form controls (checkboxes, radio buttons, toggles) detected and their states captured? – Does the output clearly indicate whether a checkbox was selected?
Handwriting Accuracy
– Is handwritten content transcribed correctly? – How well does the tool handle messy or stylized handwriting?
Hallucination-Free Output
– Does the model introduce any content not present in the original? – Are factual additions or assumptions (hallucinations) avoided?
Mistral OCR: Installation and Code Setup
Mistral OCR offers a fast and developer-friendly setup via API access through La Plateforme, Mistral’s unified AI interface.
You should also define an .env file for the API key:
MISTRAL_API_KEY=<YOUR_MISTRAL_API_KEY>
Below is an example of a Python script to send a PDF document to Mistral OCR and receive the structured Markdown output:
import base64
import os
import sys
from mistralai import Mistral
from dotenv import load_dotenv
# Load the environment variables
load_dotenv(".env")
# Function to encode the PDF to base64
def encode_pdf(pdf_path):
"""Encode the pdf to base64."""
try:
with open(pdf_path, "rb") as pdf_file:
return base64.b64encode(pdf_file.read()).decode('utf-8')
except FileNotFoundError:
print(f"Error: The file {pdf_path} was not found.")
return None
except Exception as e: # Added general exception handling
print(f"Error: {e}")
return None
# Function to OCR the PDF
def ocr_pdf(base64_pdf):
"""OCR the pdf."""
api_key = os.getenv("MISTRAL_API_KEY")
client = Mistral(api_key=api_key)
ocr_response = client.ocr.process(
model="mistral-ocr-latest",
document={
"type": "document_url",
"document_url": f"data:application/pdf;base64,{base64_pdf}"
},
include_image_base64=True
)
# Return the OCR response
return ocr_response
# Main function
if __name__ == "__main__":
# Get the path to the PDF file from the command line
pdf_path = sys.argv[1]
# Getting the base64 string
base64_pdf = encode_pdf(pdf_path)
# OCR the PDF
ocr_response = ocr_pdf(base64_pdf)
# Print the OCR response
print(ocr_response)
The script begins by loading the API key securely from a .env file using the dotenv package.
It then reads the PDF file provided via the command line, encodes it into a base64 string, and prepares it for API submission, this allows sending the file directly without uploading it to external storage.
The ocr_pdf function initializes a Mistral client with the API key and sends the encoded PDF to the mistral-ocr-latest model. It specifies the document type as document_url and includes a flag to return base64-encoded images for layout context.
Finally, the script prints the full OCR response to the terminal. This response includes the extracted text, structured layout data, and optional base64 images.
Mistral OCR returns the document in a Markdown-centric format, preserving elements like tables, headers, and embedded images inline with the text.
You should also define an .env file for the API key:
LLMWHISPERER_API_KEY=<YOUR_LLMWHISPERER_API_KEY>
Create the script to process a file with LLMWhisperer, in this case using the recent V2 of the API:
import os
from dotenv import load_dotenv
from unstract.llmwhisperer import LLMWhispererClientV2
from unstract.llmwhisperer.client_v2 import LLMWhispererClientException
import sys
# Load the API key from the .env file
load_dotenv()
# Function to process a document
def process_document(file_path):
# Initialize the client with your API key
client = LLMWhispererClientV2(base_url="https://llmwhisperer-api.us-central.unstract.com/api/v2",
api_key=os.getenv("LLMWHISPERER_API_KEY"))
# Call the sync method with the file path
try:
result = client.whisper(
file_path=file_path,
wait_for_completion=True,
wait_timeout=200,
)
return result['extraction']['result_text']
except LLMWhispererClientException as e:
print(e)
return None
# Main function
if __name__ == "__main__":
# Get the path to the PDF file from the command line
pdf_path = sys.argv[1]
# Call the function to process the document
result = process_document(pdf_path)
# Print the result
print(result)
The script begins by loading environment variables using python-dotenv, specifically fetching the API key (LLMWHISPERER_API_KEY) required to authenticate with the LLMWhisperer service.
The process_document function initializes a LLMWhispererClientV2 object, configured to point to the official Unstract LLMWhisperer endpoint. It then submits a document (PDF or other type) using the whisper method.
This method runs synchronously, waiting for the model to finish processing the file before returning. If successful, it extracts and returns the structured result_text from the document.
In the script’s main block, the path to a document is passed as a command-line argument. The script then calls process_document() with this path and prints the extracted result.
Comparative Findings
Let’s now run both of the scripts through a set of 6 different document types (5 PDF documents and 1 Excel file) to test how well LLMWhisperer and MistralOCR handle the various formats.
Apple Inc.
CONDENSED CONSOLIDATED STATEMENTS OF OPERATIONS (Unaudited)
(In millions, except number of shares which are reflected in thousands and per share amounts)
Three Months Ended Twelve Months Ended
September 24, September 25, September 24, September 25,
2022 2021 2022 2021
Net sales:
Products $ 70,958 $ 65,083 $ 316,199 $ 297,392
Services 19,188 18,277 78,129 68,425
Total net sales (1) 90,146 83,360 394,328 365,817
Cost of sales:
Products 46,387 42,790 201,471 192,266
Services 5,664 5,396 22,075 20,715
Total cost of sales 52,051 48,186 223,546 212,981
Gross margin 38,095 35,174 170,782 152,836
Operating expenses:
Research and development 6,761 5,772 26,251 21,914
Selling, general and administrative 6,440 5,616 25,094 21,973
Total operating expenses 13,201 11,388 51,345 43,887
Operating income 24,894 23,786 119,437 108,949
Other income/(expense), net (237) (538) (334) 258
Income before provision for income taxes 24,657 23,248 119,103 109,207
Provision for income taxes 3,936 2,697 19,300 14,527
Net income $ 20,721 $ 20,551 $ 99,803 $ 94,680
Earnings per share:
Basic $ 1.29 $ 1.25 $ 6.15 $ 5.67
Diluted $ 1.29 $ 1.24 $ 6.11 $ 5.61
Shares used in computing earnings per share:
Basic 16,030,382 16,487,121 16,215,963 16,701,272
Diluted 16,118,465 16,635,097 16,325,819 16,864,919
(1) Net sales by reportable segment:
Americas $ 39,808 $ 36,820 $ 169,658 $ 153,306
Europe 22,795 20,794 95,118 89,307
Greater China 15,470 14,563 74,200 68,366
Japan 5,700 5,991 25,977 28,482
Rest of Asia Pacific 6,373 5,192 29,375 26,356
Total net sales $ 90,146 $ 83,360 $ 394,328 $ 365,817
(1) Net sales by category:
iPhone $ 42,626 $ 38,868 $ 205,489 $ 191,973
Mac 11,508 9,178 40,177 35,190
iPad 7,174 8,252 29,292 31,862
Wearables, Home and Accessories 9,650 8,785 41,241 38,367
Services 19,188 18,277 78,129 68,425
Total net sales $ 90,146 $ 83,360 $ 394,328 $ 365,817
<<<
Comparative Analysis
Here is a short comparison analysis of the two outputs:
LLMWhisperer output is cleaner and more readable, preserving the original document’s layout, hierarchy, and logical groupings (e.g. sales, expenses, income).
MistralOCR output is messy and markdown-based, with extra columns and formatting noise, making it harder to read or use without clean-up.
LLMWhisperer is ideal for human review and AI tasks, while MistralOCR may be better suited for spreadsheet use after significant formatting adjustments.
# CREDIT APPLICATION AND MASTER SALES AGREEMENT\n\nPlease send your payments to: 19800 MacArthur Blvd., Suite 510 | Irvine | CA | 92612-2480 (949) 999-9337 | AR@pcpipe.com\n\n| Full Business Legal Name: | Roger Deakins |\n| --- | --- |\n| Physical Address: | Roger Deakins |\n| Billing Address: | |\n| Phone 090-9093-8930 | Buyer\'s Email |\n| How long at current address? | Type of Business Entity: |\n| | ☑ Sole Proprietorship ☐ Partnership ☐ LLC ☐ Corporation |\n| How long under current management? | ☐ Association (nonprofit) ☐ Government or Public Agency ☐ Other |\n| Principal Business Activity: | Roger Deakins |\n| Owner/Officer | Title |\n| Officer | Manager |\n| DUNS No.: | Contractor License No. & State: |\n| 9040332 | 098424 |\n| If there is a parent company, please provide full legal name: | Vigilant Systems |\n| Purchase Order Required? | May we email your invoices? |\n| ☑ Yes ☐ No | ☐ Yes ☑ No |\n| Accounts Payable (A/P) Contact Name | A/P Contact Phone Number |\n| Please feel free to attach Trade References on a separate sheet. You must provide a valid Resale or Tax Exemption Certificate to exclude sales tax from your invoices. Accounts with no activity for three or more years will require a new signed Credit Application. | |\n\nThe information provided by the applicant (hereinafter, "Buyer") in this Credit Application and Master Sales Agreement (hereinafter, "Agreement") is for the purpose of establishing a commercial credit account with Pacific Corrugated Pipe Company, LLC and its affiliates, subsidiaries, successors, and assignees (hereinafter collectively, "Seller"). Buyer desires to purchase goods and/or services from Seller, and Buyer agrees, in consideration thereof, to be bound by Seller\'s terms and conditions of sale set forth in this Agreement. The undersigned warrants and represents that he/she is authorized to enter into this Agreement on behalf of the Buyer, and that all representations above are accurate, complete and truthful. By signing below, Buyer acknowledges that it has read, understands, and agrees to Seller\'s terms and conditions of sale in this Agreement.\n\n| Signature | Date |\n| --- | --- |\n| Print name | Title |\n\nPACIFIC CORRUGATED PIPE COMPANY CREDIT APPLICATION AND MASTER SALES AGREEMENT Page 1 of 4
PACIFIC CORRUGATED
PIPE COMPANY, LLC.
A Subsidiary of Lane Enterprises Holdings, LLC
CREDIT APPLICATION AND MASTER SALES AGREEMENT
Please send your payments to:
19800 MacArthur Blvd., Suite 510 | Irvine | CA | 92612-2480
(949) 999-9337 | AR@pcpipe.com
Full Business Legal Name:
Roger Deakins
Physical Address:
Roger Deakins
Billing Address:
Phone
090-9093-8930 Buyer's Email rdeakins@ville12.com
How long at current address? Type of Business Entity:
[X] Sole Proprietorship [ ] Partnership [ ] LLC [ ] Corporation
How long under current [ ] Association (nonprofit) [ ] Government or Public Agency [ ] Other
management?
Principal Business Activity:
Roger Deakins
Owner/Officer Title Address Phone
Officer 345-4443-4332
Manager 89, farmville road, FL 694903
DUNS No .: Contractor License No. & Is this a certified Disadvantaged Business Entity (DBE)?
State:
9040332 098424 [X] Yes [ ] No
If there is a parent company, please provide full legal name:
Vigilant Systems
Purchase Order Required? May we email your invoices? If yes, please provide email address:
[X] Yes [ ] No [ ] Yes [X] No
Accounts Payable (A/P) Contact Name A/P Contact Phone Number A/P Contact Email Address
Please feel free to attach Trade References on a separate sheet.
You must provide a valid Resale or Tax Exemption Certificate to exclude sales tax from your invoices.
Accounts with no activity for three or more years will require a new signed Credit Application.
The information provided by the applicant (hereinafter, "Buyer") in this Credit Application and Master Sales Agreement
(hereinafter, "Agreement") is for the purpose of establishing a commercial credit account with Pacific Corrugated Pipe
Company, LLC and its affiliates, subsidiaries, successors, and assignees (hereinafter collectively, "Seller"). Buyer desires
to purchase goods and/or services from Seller, and Buyer agrees, in consideration thereof, to be bound by Seller's terms
and conditions of sale set forth in this Agreement. The undersigned warrants and represents that he/she is authorized to
enter into this Agreement on behalf of the Buyer, and that all representations above are accurate, complete and truthful.
By signing below, Buyer acknowledges that it has read, understands, and agrees to Seller's terms and conditions of sale
in this Agreement.
Signature Date
Print name Title
PACIFIC CORRUGATED PIPE COMPANY CREDIT APPLICATION AND MASTER SALES AGREEMENT Page 1 of 4
<<<
Comparative Analysis
Here is a short comparison of the two outputs:
LLMWhisperer output closely mirrors the original PDF layout, preserving formatting, structure, and form fields, making it ideal for document automation, review, or AI processing.
MistralOCR output is dense and markdown-formatted, with inconsistent table formatting and missing field associations, leading to reduced readability and higher clean-up effort.
LLMWhisperer captures visual cues (checkboxes, labels, section breaks) more reliably, giving a more accurate representation of form intent and user input context.
MistralOCR may still be usable for quick raw text extraction, but it lacks the polish and structure needed for downstream tasks without significant post-processing.
Form Number: CA530082\n\nForm 5500-EZ\nAnnual Return of A One-Participant (Owners/Partners and\nTheir Spouses) Retirement Plan or A Foreign Plan\nThis form is required to be filed under section 6058(a) of the Internal Revenue Code.\nCertain foreign retirement plans are also required to file this form (see instructions)\nComplete all entries in accordance with the instructions to the Form 5500-EZ.\nGo to www.ire.gov/Form5500EZ for instructions and the latest information.\nOMB No. 1545-1610\n2023\nThis Form is Open\nto Public Inspection.\nForm 5500-EZ\nPrinted Revenue Service\nFor the calendar plan year 2023 or fiscal plan year beginning (MM/DD/YYYY) 01/02/2022and ending 01/02/2023\nA This return is: (1) the first return filed for the plan (3) the final return filed for the plan\n(2) an amended return (4) a short plan year return (less than 12 months)\nB Check box if filing under [ ] Form 5558 [ ] automatic extension\n[ ] special extension (enter description)\nC If this return is for a foreign plan, check this box (see instructions)\nD If this return is for the IRS Late Filer Penalty Relief Program, check this box\n(Must be filed on a paper Form with the IRS. See instructions).\nE If this is a retroactively adopted plan permitted by SECURE Act section 201, check here\nPart II Basic Plan Information - enter all requested information.\n1a Name of plan\nAnnual Return plan\n1b Three-digit\nplan number (PN) 586\n1c Date plan first became effective\n(MM/DD/YYYY)\n02/05/2022\n2a Employer\'s name Acme Corp Software\nTrade name of business (if different from name of employer)\n2b Employer Identification Number (EIN)\n(Do not enter your Social Security Number)\n735268329\nIn care of name\nMailing address (room, apt., suite no. and street, or P.O. box)\n235, Park Street Avenue, FL\nCity or town, state or province, country, and ZIP or foreign postal code (if foreign, see instructions)\nFL 63052\n3a Plan administrator\'s name (if same as employer, enter "Same")\nIn care of name\nMailing address (room, apt., suite no. and street, or P.O. box)\nCity or town, state or province, country, and ZIP or foreign postal code (if foreign, see instructions)\n4 If the employer\'s name, the employer\'s EIN, and/or the plan name has changed since the\nlast return filed for this plan, enter the employer\'s name and EIN, the plan name, and the\nplan number for the last return in the appropriate space provided\na Employer\'s name\n4b EIN 5732900\n4c Plan name\n4d PN\n5a(1) Total number of participants at the beginning of the plan year\n5a(1) 10\na(2) Total number of active participants at the beginning of the plan year\n5a(2) 9\nb(1) Total number of participants at the end of the plan year\n5b(1) 5\nb(2) Total number of active participants at the end of the plan year\n5b(2)\nc Number of participants who terminated employment during the plan year with accrued accrued\nbenefits that were less than 100% vested\n5c 2\nPart III Financial Information\n(1) Beginning of year\n(2) End of year\n6a Total plan assets\n6a $50000 $60000\nb Total plan liabilities\n6b $4000 $5000\nc Net plan assets (subtract line 6b from 6a)\n6c\nFor Privacy Act and Paperwork Reduction Act Notice, see the Instructions for Form 5500-EZ.\nCatalog Number 63263R Form 5500-EZ (2023)
Form Number: CA530082
Form 5500-EZ Annual Return of A One-Participant (Owners/Partners and OMB No. 1545-1610
Their Spouses) Retirement Plan or A Foreign Plan
This form is required to be filed under section 6058(a) of the Internal Revenue Code. 2023
Certain foreign retirement plans are also required to file this form (see instructions).
Department of the Treasury Complete all entries in accordance with the instructions to the Form 5500-EZ. This Form is Open
Internal Revenue Service
Go to www.irs.gov/Form5500EZ for instructions and the latest information. to Public Inspection.
Part Annual Return Identification Information
For the calendar plan year 2023 or fiscal plan year beginning (MM/DD/YYYY) 01/02/202 Zand ending 01/02/2023
A This return is: (1) the first return filed for the
[X] plan (3) [ ] the final return filed for the plan
(2) [ ] an amended return (4) [ ] a short plan year return (less than 12 months)
B Check box if filing under [ ] Form 5558 [ ] automatic extension
[ ] special extension (enter description)
C If this return is for a foreign plan, check this box (see instructions) [ ]
D If this return is for the IRS Late Filer Penalty Relief Program, check this box
(Must be filed on a paper Form with the IRS. See instructions). [ ]
E If this is a retroactively adopted plan permitted by SECURE Act section 201, check here [ ]
Part II Basic Plan Information - enter all requested information.
1a Name of plan 1b Three-digit
plan number (PN) 586
1c Date plan first became effective
Annual Return Plan
(MM/DD/YYYY)
02/05/2022
2a Employer's name 2b Employer Identification Number (EIN)
Acme Corp Software (Do not enter your Social Security Number)
Trade name of business (if different from name of employer) 735268329
2c Employer's telephone number
In care of name 011536259
2d Business code (see instructions)
Mailing address (room, apt., suite no. and street, or P.O. box)
235, Park Street Avenue, FL
City or town, state or province, country, and ZIP or foreign postal code (if foreign, see instructions)
FL 63052
3a Plan administrator's name (if same as employer, enter "Same") 3b Administrator's EIN
532678
In care of name 3c Administrator's telephone number
Mailing address (room, apt., suite no. and street, or P.O. box)
City or town, state or province, country, and ZIP or foreign postal code (if foreign, see instructions)
4 If the employer's name, the employer's EIN, and/or the plan name has changed since the
last return filed for this plan, enter the employer's name and EIN, the plan name, and the
plan number for the last return in the appropriate space provided
a Employer's name 4b EIN 5732900
4c Plan name 4d PN
5a(1) 10
5a(1) Total number of participants at the beginning of the plan year
5a(2) 8
a(2) Total number of active participants at the beginning of the plan year
5b(1) 5
b(1) Total number of participants at the end of the plan year
b(2) Total number of active participants at the end of the plan year 5b(2)
with accrued
c Number of participants who terminated employment during the plan year
benefits that were less than 100% vested 5c 2
Part III Financial Information
(1) Beginning of year (2) End of year
6a $ 50000 $ 60000
6a Total plan assets
6b $ 4000 $ 5000
b Total plan liabilities
6a) 6c
c Net plan assets (subtract line 6b from
see the Instructions for Form 5500-EZ. Catalog Number 63263R Form 5500-EZ (2023)
For Privacy Act and Paperwork Reduction Act Notice,
<<<
Comparative Analysis
Here is a short comparison of the two outputs:
LLMWhisperer output preserves the original PDF’s structure and layout, including section headers, field labels, checkboxes, and multi-column alignment, giving a near-replica of the official form. This is ideal for automation, auditing, or downstream LLM tasks.
MistralOCR output is dense and flattened, losing visual hierarchy and often grouping or omitting field labels. While it extracts most of the raw text content, it lacks structured formatting, making parsing difficult.
LLMWhisperer handles scanned handwritten forms well, maintaining field relationships and numeric entries (e.g., plan assets, participant counts) even in noisy or low-quality inputs.
MistralOCR remains suitable for basic raw text retrieval, but its output will need heavy post-processing for structured applications or data extraction pipelines.
# FICUS BANK\n\n4321 Random Boulevard • Somecity, ST 12340\n\nSave this Loan Estimate to compare with your Closing Disclosure.\n\n## Loan Estimate\n\n**DATE ISSUED** 2/15/2013\n\n**APPLICANTS** Michael Jones and Mary Stone 123 Anywhere Street Anytown, ST 12345\n\n**PROPERTY** 456 Somewhere Avenue Anytown, ST 12345\n\n**SALE PRICE** $180,000\n\n**LOAN TERM** 30 years\n\n**PURPOSE** Purchase\n\n**PRODUCT** Fixed Rate\n\n**LOAN TYPE** ☑ Conventional ☐ FHA ☐ VA ☐ ***_***_____\n\n**LOAN ID #** 123456789\n\n**RATE LOCK** ☐ NO ☑ YES, until 4/16/2013 at 5:00 p.m. EDT\n\nBefore closing, your interest rate, points, and lender credits can change unless you lock the interest rate. All other estimated closing costs expire on 3/4/2013 at 5:00 p.m. EDT\n\n## Loan Terms\n\n| | | Can this amount increase after closing? |\n| --- | --- | --- |\n| **Loan Amount** | **$162,000** | **NO** |\n| **Interest Rate** | **3.875%** | **NO** |\n| **Monthly Principal & Interest** | **$761.78** | **NO** |\n\nSee Projected Payments below for your Estimated Total Monthly Payment\n\n**Does the loan have these features?**\n\n**YES** • As high as $3,240 if you pay off the loan during the first 2 years\n\n**Balloon Payment** **NO**\n\n## Projected Payments\n\n| Payment Calculation | Years 1-7 | Years 8-30 |\n| --- | --- | --- |\n| Principal & Interest | $761.78 | $761.78 |\n| Mortgage Insurance | + 82 | + -- |\n| Estimated Escrow Amount can increase over time | + 206 | + 206 |\n| Estimated Total Monthly Payment | $1,050 | $968 |\n\n**This estimate includes** ☑ Property Taxes ☐ Homeowner's Insurance\n\n**In escrow?**\n\n**YES** ☐ Other\n\nSee Section G on page 2 for escrowed property costs. You must pay for other property costs separately.\n\n## Estimated Taxes, Insurance & Assessments\n\nAmount can increase over time\n\n**$206** a month\n\n**Estimated Cash to Close**\n\n**$16,054** Includes $5,672 in Loan Costs + $2,382 in Other Costs - $0 in Lender Credits. See page 2 for details.\n\n**In Lender Credits.** See page 2 for details.\n\n**In escrow?**\n\n**YES** ☐ Other\n\nSee Section G on page 2 for escrowed property costs. You must pay for other property costs separately.\n\nVisit www.consumerfinance.gov/mortgage-estimate for general information and tools.\n\n**LOAN ESTIMATE**\n\n**PAGE 1 OF 3** • **LOAN ID # 123456789**", images=[], dimensions=OCRPageDimensions(dpi=200, height=2339, width=1653)), OCRPageObject(index=1, markdown="# Closing Cost Details\n\n| Loan Costs | |\n| --- | --- |\n| A. Origination Charges | $\\$ 1,802$ |\n| .25 \\% of Loan Amount (Points) | $\\$ 405$ |\n| Application Fee | $\\$ 300$ |\n| Underwriting Fee | $\\$ 1,097$ |\n\n| B. Services You Cannot Shop For | $\\$ 672$ |\n| --- | --- |\n| Appraisal Fee | $\\$ 405$ |\n| Credit Report Fee | $\\$ 30$ |\n| Flood Determination Fee | $\\$ 20$ |\n| Flood Monitoring Fee | $\\$ 32$ |\n| Tax Monitoring Fee | $\\$ 75$ |\n| Tax Status Research Fee | $\\$ 110$ |\n\n| C. Services You Can Shop For | $\\$ 3,198$ |\n| --- | --- |\n| Pest Inspection Fee | $\\$ 135$ |\n| Survey Fee | $\\$ 65$ |\n| Title - Insurance Binder | $\\$ 700$ |\n| Title - Lender's Title Policy | $\\$ 535$ |\n| Title - Settlement Agent Fee | $\\$ 502$ |\n| Title - Title Search | $\\$ 1,261$ |\n\n| D. TOTAL LOAN COSTS $(A+B+C)$ | $\\$ 5,672$ |\n| --- | --- |\n| | |\n\n| Other Costs | |\n| --- | --- |\n| E. Taxes and Other Government Fees | $\\$ 85$ |\n| Recording Fees and Other Taxes | $\\$ 85$ |\n| Transfer Taxes | $\\$ 867$ |\n| F. Prepaids | $\\$ 665$ |\n| Homeowner's Insurance Premium ( 6 months) | $\\$ 605$ |\n| Mortgage Insurance Premium ( months) | $\\$ 262$ |\n| Prepaid Interest ( $\\$ 17.44$ per day for 15 days @ 3.875\\%) | |\n| Property Taxes ( months) | $\\$ 413$ |\n| G. Initial Escrow Payment at Closing | $\\$ 202$ |\n| Homeowner's Insurance $\\$ 100.83$ per month for 2 mo. | $\\$ 211$ |\n| Mortgage Insurance per month for mo. | |\n| Property Taxes $\\$ 105.30$ per month for 2 mo. | $\\$ 1,017$ |\n\n| H. Other | $\\$ 1,017$ |\n| --- | --- |\n| Title - Owner's Title Policy (optional) | $\\$ 1,017$ |\n\n| I. TOTAL OTHER COSTS (E + F + G + H) | $\\$ 2,382$ |\n| --- | --- |\n| J. TOTAL CLOSING COSTS | $\\$ 8,054$ |\n| D + I | $\\$ 8,054$ |\n| Lender Credits | |\n| Calculating Cash to Close | |\n| Total Closing Costs (J) | $\\$ 8,054$ |\n| Closing Costs Financed (Paid from your Loan Amount) | $\\$ 0$ |\n| Down Payment/Funds from Borrower | $\\$ 18,000$ |\n| Deposit | $-\\$ 10,000$ |\n| Funds for Borrower | $\\$ 0$ |\n| Seller Credits | $\\$ 0$ |\n| Adjustments and Other Credits | $\\$ 0$ |\n| Estimated Cash to Close | $\\$ 16,054$ |
FICUS BANK
4321 Random Boulevard . Somecity, ST 12340 Save this Loan Estimate to compare with your Closing Disclosure.
Loan Estimate LOAN TERM 30 years
PURPOSE Purchase
DATE ISSUED 2/15/2013 PRODUCT Fixed Rate
APPLICANTS Michael Jones and Mary Stone LOAN TYPE [X] Conventional [ ] FHA [ ] VA [ ]
123 Anywhere Street LOAN ID # 123456789
Anytown, ST 12345 RATE LOCK [ ] NO [X] YES, until 4/16/2013 at 5:00 p.m. EDT
PROPERTY 456 Somewhere Avenue Before closing, your interest rate, points, and lender credits can
Anytown, ST 12345 change unless you lock the interest rate. All other estimated
SALE PRICE $180,000 closing costs expire on 3/4/2013 at 5:00 p.m. EDT
Loan Terms Can this amount increase after closing?
Loan Amount $162,000 NO
Interest Rate 3.875% NO
Monthly Principal & Interest $761.78 NO
See Projected Payments below for your
Estimated Total Monthly Payment
Does the loan have these features?
Prepayment Penalty YES . As high as $3,240 if you pay off the loan during the
first 2 years
Balloon Payment NO
Projected Payments
Payment Calculation Years 1-7 Years 8-30
Principal & Interest $761.78 $761.78
Mortgage Insurance + 82 + -
Estimated Escrow + 206 + 206
Amount can increase over time
Estimated Total
Monthly Payment $1,050 $968
This estimate includes In escrow?
[X] Property Taxes YES
Estimated Taxes, Insurance $206 YES
[X]
& Assessments Homeowner's Insurance
Amount can increase over time a month [ ] Other:
See Section G on page 2 for escrowed property costs. You must pay for other
property costs separately.
Costs at Closing
Estimated Closing Costs $8,054 Includes $5,672 in Loan Costs + $2,382 in Other Costs - $0
in Lender Credits. See page 2 for details.
Estimated Cash to Close $16,054 Includes Closing Costs. See Calculating Cash to Close on page 2 for details.
Visit www.consumerfinance.gov/mortgage-estimate for general information and tools.
LOAN ESTIMATE PAGE 1 OF 3 . LOAN ID # 123456789
<<<
Closing Cost Details
Loan Costs Other Costs
A. Origination Charges $1,802 E. Taxes and Other Government Fees $85
.25 % of Loan Amount (Points) $405 Recording Fees and Other Taxes $85
Application Fee $300 Transfer Taxes
Underwriting Fee $1,097
F. Prepaids $867
Homeowner's Insurance Premium ( 6 months) $605
Mortgage Insurance Premium ( months)
Prepaid Interest ( $17.44 per day for 15 days @ 3.875%) $262
Property Taxes ( months)
G. Initial Escrow Payment at Closing $413
Homeowner's Insurance $100.83 per month for 2 mo. $202
Mortgage Insurance per month for mo.
B. Services You Cannot Shop For $672 Property Taxes $105.30 per month for 2 mo. $211
Appraisal Fee $405
Credit Report Fee $30
Flood Determination Fee $20
Flood Monitoring Fee $32
Tax Monitoring Fee $75
Tax Status Research Fee $110 H. Other $1,017
Title - Owner's Title Policy (optional) $1,017
I. TOTAL OTHER COSTS (E + F + G + H) $2,382
C. Services You Can Shop For $3,198
J. TOTAL CLOSING COSTS $8,054
Pest Inspection Fee $135
D + $8,054
Survey Fee $65
Title - Insurance Binder $700 Lender Credits
Title - Lender's Title Policy $535
Title - Settlement Agent Fee $502 Calculating Cash to Close
Title - Title Search $1,261
Total Closing Costs (J) $8,054
Closing Costs Financed (Paid from your Loan Amount) $0
Down Payment/Funds from Borrower $18,000
Deposit - $10,000
Funds for Borrower $0
Seller Credits $0
Adjustments and Other Credits $0
D. TOTAL LOAN COSTS (A + B + C) $5,672 Estimated Cash to Close $16,054
LOAN ESTIMATE PAGE 2 OF 3 . LOAN ID # 123456789
<<<
Comparative Analysis
Here is a short comparison of the two outputs:
LLMWhisperer output preserves the form’s original layout and structured formatting, including headers, field groupings, checkbox states, and multi-column alignments. This results in a highly legible, form-like digital replica that’s ideal for automation, audits, or downstream LLM tasks.
MistralOCR output is flattened into markdown, which captures raw text and some table formatting but loses precise alignment and structure. Checkbox indicators (e.g. ☑, ☐) are retained, but contextual relationships between fields are less clear.
LLMWhisperer clearly separates sections like “Loan Terms”, “Projected Payments”, and “Closing Cost Details”, maintaining their hierarchy and labels, which makes it easier to extract structured data directly from the output.
MistralOCR does capture most numerical and textual content, but interleaves information and occasionally omits or misplaces details like field headers or line-level alignment, making post-processing more challenging.
LLMWhisperer reproduces form tables more faithfully, e.g., preserving calculations and line items in the “Loan Costs” and “Other Costs” tables, including complex breakdowns and summations.
MistralOCR remains usable for raw content retrieval, but its output lacks precision required for structured data pipelines, and would require significant transformation or rule-based cleaning to be automation-ready.
Packing List
Shipper/ Exporter: Faculty of Arts Ultimate Consignee: Herald Corp Bill To: Faculty of Arts Intermediate Consignee
5 Washington 28 OLD 5 Washington Square S,
Square S, New York, BROMPTON New York, NY 10012, USA
NY 10012, USA ROAD, SOUTH
KENSINGTON
Commercial Invoice No .: 894933 Total number of Packages: 32 Transportation: AIR CARGO UPS
Order No .: Total Gross Weight (Lbs):
Total Gross Weight (Kgs): 35
AWB/BL Number: Total Net Weight (Lbs):
Date Of Shipment:
Total net Weight (Kgs): Conditions of Sale and Terms of Payment: If unpaid after 15 days, ALL
Currency: 34343454 Total Cubic Feet:
Freight: 12/12/2023 Total Cubic Meters: 1020 may dispose of the goods
Dollars per Clause 5.
Per package
Item gross weight
Shipment Item Description, Sales Order No., Customer Shipped Packaging
Number Dimensions
Line No. PO No. Quantity Type Inches centimeters
L W H L W H LBS. KGS.
12 1 Print packaging, 23445 10 Box 12 12 12 10
13 2 Print packaging, 345232 10 Box 16 16 16 20
14 3 Black Ink cartridges, 342900 20 Glass 10 8 8 15
Country of Origin:
Marks: USA
Note: These commodities, Technology or software were exported from the United States in accordance with the Export Administration Regulations.
Diversion contrary to U.S. law is prohibited.
Signature: Date:
<<<
Comparative Analysis
Here is a short comparison of the two outputs:
LLMWhisperer output accurately reconstructs the original document’s layout, preserving field labels, values, and visual grouping despite skewed input. Key metadata like “Shipper/Exporter”, invoice number, weights, and dimensional metrics are clearly separated and aligned for easy extraction.
MistralOCR output becomes highly repetitive and redundant, with field-value pairs duplicated dozens of times, likely due to skewed OCR artifacts. The layout collapses into a dense, flattened markdown blob with minimal structure and extreme noise.
LLMWhisperer captures numerical precision across grouped fields, including net/gross weights, volumetric metrics (cubic feet/meters), and packaging counts. These are preserved in tabular-style formatting, making downstream parsing feasible.
MistralOCR fails to de-duplicate or disambiguate fields, resulting in dozens of repeated blocks like “Total Lbs (Kgs): – Meters: 12 – Gross Weight: 15”, losing clarity and introducing conflicting values.
LLMWhisperer maintains semantic hierarchy and document boundaries, including logical divisions between header metadata and totals. Even with a skewed scan, its output mimics the intended visual structure.
MistralOCR output is unusable in its raw form for automation, due to severe duplication, formatting noise, and loss of document structure. Manual cleaning or heavy post-processing would be required to extract reliable data.
Document 6 – Excel File
For this document type, we will use this Excel file:
File "D:\GitHub\LLMWhisperer-MistralOCR-Comparison\mistral_ocr_example.py", line 52, in <module>
ocr_response = ocr_pdf(base64_pdf)
^^^^^^^^^^^^^^^^^^^
File "D:\GitHub\LLMWhisperer-MistralOCR-Comparison\mistral_ocr_example.py", line 30, in ocr_pdf
ocr_response = client.ocr.process(
^^^^^^^^^^^^^^^^^^^
File "d:\GitHub\LLMWhisperer-MistralOCR-Comparison\.venv\Lib\site-packages\mistralai\ocr.py", line 129, in process
raise models.SDKError(
mistralai.models.sdkerror.SDKError: API error occurred: Status 400
{"object":"error","message":"Invalid document type. application/vnd.openxmlformats-officedocument.spreadsheetml.sheet is not supported.","type":"invalid_file","param":null,"code":"1901"}
As you can see from the error message, MistralOCR doesn’t support Excel files natively.
Then we process it with LLMWhisperer:
python llmwhisperer_example.py files\uber.xlsx
And we get the following output:
Sheet name:Income statements
Uber Technologies, Inc. (NYSE:UBER) > Financials > Income Statement
─────────────────────────────────────────── ──────────────────────────────────
In Millions of the reported currency, except per share items. Template: Standard Restatement: Latest Filings
──────────────────────────────────
Period Type: Annual Order: Latest on Right
──────────────────────────────────
Currency: Reported Currency Conversion: Today's Spot Rate
──────────────────────────────────
Units: S&P Capital IQ (Default) Decimals: Capital IQ (Default)
──────────────────────────────────
Source: Capital IQ & Proprietary Da
───────────────────────────────────────────
──────────────────────────
Income Statement
─────────────────
Reclassified Reclassified Reclassified Reclassified Reclassified
12 months 12 months 12 months 12 months 12 months 12 months 12 months
For the Fiscal Period Ending Dec-31-2016 Dec-31-2017 Dec-31-2018 Dec-31-2019 Dec-31-2020 Dec-31-2021 Dec-31-2022
Currency USD USD USD USD USD USD USD
Revenue 3,845.0 7,932.0 10,433.0 13,000.0 11,139.0 17,455.0 31,877.0
Other Revenue - - - - - - -
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Total Revenue 3,845.0 7,932.0 10,433.0 13,000.0 11,139.0 17,455.0 31,877.0
Cost Of Goods Sold 3,109.0 5,514.0 6,302.0 8,363.0 6,801.0 11,228.0 22,072.0
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gross Profit 736.0 2,418.0 4,131.0 4,637.0 4,338.0 6,227.0 9,805.0
Selling General & Admin Exp. 2,575.0 4,564.0 5,036.0 7,925.0 6,144.0 7,105.0 7,892.0
R & D Exp. 864.0 1,201.0 1,505.0 4,836.0 2,120.0 2,054.0 2,798.0
Depreciation & Amort. 320.0 510.0 426.0 472.0 575.0 902.0 947.0
Other Operating Expense/(Income) - - - - - - -
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Other Operating Exp., Total 3,759.0 6,275.0 6,967.0 13,233.0 8,839.0 10,061.0 11,637.0
Operating Income (3,023.0) (3,857.0) (2,836.0) (8,596.0) (4,501.0) (3,834.0) (1,832.0)
Interest Expense (334.0) (479.0) (648.0) (559.0) (458.0) (483.0) (565.0)
Interest and Invest. Income 22.0 71.0 104.0 234.0 55.0 37.0 139.0
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Net Interest Exp. (312.0) (408.0) (544.0) (325.0) (403.0) (446.0) (426.0)
Income/(Loss) from Affiliates - - (42.0) (34.0) (34.0) (37.0) 107.0
Currency Exchange Gains (Loss) (91.0) 42.0 (45.0) (40.0) (128.0) (67.0) (147.0)
Other Non-Operating Inc. (Exp.) 208.0 (129.0) (428.0) 82.0 59.0 (230.0) (213.0)
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
EBT Excl. Unusual Items (3,218.0) (4,352.0) (3,895.0) (8,913.0) (5,007.0) (4,614.0) (2,511.0)
Restructuring Charges - - - - (362.0) - -
Impairment of Goodwill - - - - - - -
Gain (Loss) On Sale Of Invest. - - 1,996.0 2.0 (1,815.0) 1,626.0 (6,822.0)
Gain (Loss) On Sale Of Assets - - 3,214.0 - 204.0 1,684.0 14.0
Asset Writedown - (223.0) (197.0) - - - -
Other Unusual Items - - 152.0 444.0 - 242.0 -
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
EBT Incl. Unusual Items
Comparative Analysis
Here is a short comparison of the two outputs:
LLMWhisperer successfully processes Excel files, extracting structured tabular data from the spreadsheet with clear column headers, numeric precision, and financial hierarchy intact. It interprets fiscal periods, currency units, and even metadata like source and reporting standards.
MistralOCR does not support Excel files at all, returning a hard API error indicating that application/vnd.openxmlformats-officedocument.spreadsheetml.sheet is not a supported MIME type. The pipeline halts entirely and provides no output for this file format.
LLMWhisperer converts the spreadsheet into a human-readable financial report, preserving layout features like multi-year comparisons, currency formatting, and clearly separated revenue, cost, and profit lines; mimicking a professional financial statement.
MistralOCR fails to offer a fallback or workaround, meaning Excel support would require format conversion (e.g., saving as PDF) before even attempting extraction, increasing pre-processing burden.
LLMWhisperer provides value for structured financial data ingestion, producing reliable output directly usable for analytics, dashboards, or automated pipelines.
MistralOCR’s lack of Excel compatibility limits its applicability for enterprise use cases involving tabular financial, inventory, or logistics data typically stored in spreadsheets.
Comparative Findings Summary
Feature / Document Type
Mistral OCR
LLMWhisperer (Unstract)
Layout Preservation
⚠️ Collapses layout on scans or OCR noise
✅ Reconstructs layout even in skewed scans
Table Extraction
⚠️ Struggles with structured tables
✅ Schema-aware tabular output
Checkboxes / Radios
⚠️ Detected but lacks structure
✅ Parsed with semantic structure
Handwriting Support
⚠️ Very limited, poor accuracy
✅ Parses mixed print + handwriting
Document Boundaries
❌ Flattened, lacks clear sections
✅ Maintains headers, sections, totals
Field Deduplication
❌ Redundant values repeated excessively
✅ Clean de-duplication and semantic grouping
Numerical Accuracy
⚠️ Often ambiguous due to layout collapse
✅ Preserves precision across columns
Image/Scan Robustness
⚠️ Struggles with low quality or skew
✅ Layout and data still reconstructed
Excel File Support
❌ Not supported (API error)
✅ Reads .xlsx directly, extracts data
Hallucination Risk
❌ High — spurious or repeated data
✅ Controlled, factual extraction
LLMWhisperer (Unstract) consistently outperforms Mistral OCR across all document types.
It accurately preserves layout, handles skewed scans, extracts tables with schema-awareness, and cleanly parses fields; including checkboxes, handwritten notes, and Excel files. Its output is structured with strong deduplication and numerical precision.
In contrast, Mistral OCR often collapses layout, produces redundant and noisy markdown, fails on Excel input, and struggles with unstructured or low-quality scans. While it does detect checkboxes, it lacks contextual grouping and has a high hallucination risk.
Use Cases and Recommendations
Choosing the right tool depends on the complexity of your documents and the end use of the extracted data.
When to Use Mistral OCR:
✅ For clean, digital documents (e.g., basic PDFs with standard fonts and layout).
⚡ When you need fast Markdown output without additional processing.
📄 Suitable for simple extraction tasks where layout fidelity and field grouping aren’t critical.
When to Choose LLMWhisperer (Unstract):
🧾 For scanned, skewed, or handwritten documents requiring layout-aware parsing.
📊 When tables, checkboxes, or multi-format inputs (PDF, DOCX, XLSX) are involved.
🧠 Ideal for automation pipelines where structured or schema-mapped output is required.
🏗️ Useful in document-heavy domains like logistics, legal, healthcare, and finance where accuracy and data structure are essential.
In summary, use Mistral OCR for fast, simple jobs, and LLMWhisperer when precision, structure, and robustness are key.
Cost and Deployment Considerations
Mistral OCR
Deployment: SaaS-only, no On-Premise support.
Pricing: Flat-rate model at $1/1,000 pages for OCR, $3/1,000 pages for annotations.
Customization: Limited to built-in SaaS capabilities, with minimal tuning options.
Mistral OCR is simple and cost-effective for lightweight needs but lacks deployment versatility.
LLMWhisperer (Unstract)
Deployment: Highly flexible, since it supports On-Premise deployments, SaaS plans, and enterprise/private cloud options.
Pricing: Tiered per processing mode, ranging from $1 to $15 per 1,000 pages, depending on quality and form understanding.
Customization: Full control with On-Premise setups, plus rich API options and structured output formats.
LLMWhisperer supports diverse deployment strategies and pricing models, making it well-suited for both developers and enterprise teams.
Conclusion
Mistral OCR is a fast and easy solution for basic text extraction needs which makes it ideal for straightforward, low-complexity use cases.
However, for organizations that require accurate layout retention, form parsing, structured outputs, and minimal hallucination, LLMWhisperer stands out as the superior choice.
With enterprise-ready features, On-Premise deployment options, and support for complex document types, LLMWhisperer is purpose-built for production environments and high-stakes automation workflows.
UNSTRACT
End Manual Document Processing
Leveraging AI to Convert Unstructured Documents into Usable Data
Nuno Bispo is a Senior Software Engineer with more than 15 years of experience in software development.
He has worked in various industries such as insurance, banking, and airlines, where he focused on building software using low-code platforms.
For the past years, Nuno has been improving his skills in Python and Django and has worked as a freelance consultant on many international projects and written several innovative articles on his blog.
Currently, Nuno works as an Integration Architect for a major multinational corporation.
He has a degree in Computer Engineering.
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