Provider Comparison

A detailed comparison of PyMuPDF4LLM vs LlamaParse to help you choose the right provider for your use case.

Quick Comparison

Feature	PyMuPDF4LLM	LlamaParse
Speed	⚡ < 1s	🐌 5-30s
Cost	💰 Free	💳 API Credits
Setup	✅ None	🔑 API Key Required
Accuracy	⭐⭐⭐ Good	⭐⭐⭐⭐⭐ Excellent
Entity Extraction	❌ None	✅ Advanced
Table Analysis	⚠️ Basic	✅ Advanced
Image Handling	⚠️ Placeholders	✅ Full Extraction
Custom Instructions	❌ No	✅ Yes
Offline Operation	✅ Yes	❌ No
Multi-format	❌ PDF Only	✅ PDF, DOCX, PPTX, HTML

Detailed Feature Comparison

Text Extraction

PyMuPDF4LLM

Quality: Clean, well-formatted text
Speed: Lightning fast (< 1 second)
Formatting: Basic markdown conversion
Structure: Preserves headings and paragraphs
Languages: All PDF text encodings

result = docsray.extract("document.pdf", provider="pymupdf4llm")
text = result['extraction']['text']
# Clean, fast text extraction

LlamaParse

Quality: AI-enhanced text with context
Speed: Slower but comprehensive (5-30 seconds)
Formatting: Advanced markdown with structure
Structure: Deep understanding of document hierarchy
Languages: Multi-language support with translation

result = docsray.xray("document.pdf", provider="llama-parse")
text = result['analysis']['full_extraction']['text']
# Comprehensive, AI-analyzed text

Table Processing

PyMuPDF4LLM Table Handling

Capabilities:

✅ Detects table boundaries
✅ Extracts table content as text
⚠️ Basic row/column detection
❌ No structure preservation
❌ No header identification

Example Output:

Quarter | Revenue | Growth
Q1 2023 | $100M | 15%
Q2 2023 | $115M | 20%

LlamaParse Table Handling

Capabilities:

✅ Advanced structure analysis
✅ Header identification
✅ Cell relationship mapping
✅ Data type recognition
✅ HTML table output
✅ Structured JSON data

Example Output:

{
  "table": {
    "headers": ["Quarter", "Revenue", "Growth"],
    "rows": [
      {"Quarter": "Q1 2023", "Revenue": "$100M", "Growth": "15%"},
      {"Quarter": "Q2 2023", "Revenue": "$115M", "Growth": "20%"}
    ],
    "html": "<table><tr><th>Quarter</th>..."
  }
}

Entity Recognition

PyMuPDF4LLM

Entity Extraction: None
Recognition: No structured entity identification
Output: Plain text only

LlamaParse

Entity Types: 15+ types (PERSON, ORGANIZATION, DATE, MONETARY, etc.)
Accuracy: High with context understanding
Relationships: Maps entity relationships
Custom Entities: Configurable via instructions

result = docsray.xray("contract.pdf", provider="llama-parse")
entities = result['analysis']['extracted_content']['entities']

# Example entities:
# [{"type": "PERSON", "value": "John Smith", "context": "signatory"},
#  {"type": "MONETARY", "value": "$50,000", "context": "payment amount"}]

Image Processing

PyMuPDF4LLM

Detection: Identifies image locations
Extraction: No image file extraction
Description: Simple placeholders only
Output: [Image: Figure 1 - Chart]

LlamaParse

Detection: Advanced image recognition
Extraction: Full image files with metadata
Description: AI-generated detailed descriptions
Analysis: Content analysis and context understanding

result = docsray.xray("report.pdf", provider="llama-parse")
images = result['analysis']['full_extraction']['images']

# Example image data:
# {
#   "description": "Bar chart showing quarterly revenue growth",
#   "page": 3,
#   "metadata": {"width": 800, "height": 600, "format": "PNG"}
# }

Performance Comparison

Processing Speed

Document Size	PyMuPDF4LLM	LlamaParse
Small (1-5 pages)	0.1-0.3s	5-10s
Medium (5-20 pages)	0.3-1.0s	10-20s
Large (20+ pages)	1.0-5.0s	20-60s

Memory Usage

Document Size	PyMuPDF4LLM	LlamaParse
Small	10-30MB	50-100MB
Medium	30-100MB	100-300MB
Large	100-300MB	300-800MB

Accuracy Comparison

Text Extraction Accuracy:

PyMuPDF4LLM: 90-95% for standard PDFs
LlamaParse: 95-99% with context understanding

Table Extraction Accuracy:

PyMuPDF4LLM: 70-80% structure preservation
LlamaParse: 90-95% structure preservation

Entity Recognition Accuracy:

PyMuPDF4LLM: 0% (not supported)
LlamaParse: 85-95% depending on document type

Cost Analysis

PyMuPDF4LLM Costs

Processing: $0 (completely free)
API Calls: No API usage
Infrastructure: Local processing only
Scaling: No additional costs

LlamaParse Costs

Processing: Based on LlamaIndex Cloud pricing
API Calls: Per-document charges
Caching: Reduces repeat processing costs
Scaling: Costs scale with usage

Cost per Document (Estimated):

Small documents: $0.01-0.05
Medium documents: $0.05-0.15
Large documents: $0.15-0.50

Use Case Decision Matrix

Choose PyMuPDF4LLM When:

✅ Speed is critical (need results in < 1 second)
✅ Processing many documents (batch operations)
✅ Cost is a concern (zero API charges)
✅ Simple text extraction (basic content needs)
✅ Development/testing (rapid iteration)
✅ Offline processing (no internet required)
✅ Metadata extraction (document properties)

Example scenarios:

Document preview in file managers
Search index generation
Quick document validation
Development workflows
High-volume document processing

Choose LlamaParse When:

✅ Comprehensive analysis needed
✅ Entity extraction required
✅ Complex tables must be preserved
✅ Image analysis important
✅ Custom instructions needed
✅ Production analysis (accuracy over speed)
✅ Multi-format support required

Example scenarios:

Legal contract analysis
Financial document processing
Research paper analysis
Form data extraction
Business intelligence workflows

Hybrid Approaches

Sequential Processing

# Fast overview first, detailed analysis second
def analyze_document(doc_path):
    # Quick overview with PyMuPDF4LLM
    overview = docsray.peek(doc_path, provider="pymupdf4llm")
    
    # Decide if detailed analysis is needed
    if overview['metadata']['page_count'] > 50:
        return overview  # Too large, keep basic
    
    # Comprehensive analysis with LlamaParse
    return docsray.xray(doc_path, provider="llama-parse")

Fallback Strategy

# Try LlamaParse, fallback to PyMuPDF4LLM
def robust_extraction(doc_path):
    try:
        return docsray.xray(doc_path, provider="llama-parse")
    except Exception as e:
        print(f"LlamaParse failed: {e}, falling back...")
        return docsray.extract(doc_path, provider="pymupdf4llm")

Parallel Processing

# Process with both providers simultaneously
import asyncio

async def dual_analysis(doc_path):
    fast_task = docsray.extract(doc_path, provider="pymupdf4llm")
    deep_task = docsray.xray(doc_path, provider="llama-parse")
    
    fast_result = await fast_task
    # Return fast result immediately, continue with deep analysis
    
    deep_result = await deep_task
    return {"fast": fast_result, "comprehensive": deep_result}

Format Support Comparison

PyMuPDF4LLM Supported Formats

✅ PDF - Full support
❌ DOCX - Not supported
❌ PPTX - Not supported
❌ HTML - Not supported
❌ RTF - Not supported

LlamaParse Supported Formats

✅ PDF - Full support
✅ DOCX - Full support
✅ PPTX - Full support
✅ HTML - Full support
✅ RTF - Full support
✅ TXT - Full support

Quality Metrics

Text Extraction Quality

Standard Business Documents:

PyMuPDF4LLM: 95% accuracy
LlamaParse: 98% accuracy

Complex Layouts (Multi-column, forms):

PyMuPDF4LLM: 80% accuracy
LlamaParse: 95% accuracy

Scanned Documents:

PyMuPDF4LLM: 0% (no OCR)
LlamaParse: 85% accuracy

Technical Documents:

PyMuPDF4LLM: 90% accuracy
LlamaParse: 97% accuracy

Table Extraction Quality

Simple Tables:

PyMuPDF4LLM: 85% structure preserved
LlamaParse: 98% structure preserved

Complex Tables (merged cells, nested headers):

PyMuPDF4LLM: 60% structure preserved
LlamaParse: 90% structure preserved

Financial Tables:

PyMuPDF4LLM: 70% accuracy
LlamaParse: 95% accuracy

Recommendation Framework

Start with This Decision Tree:

Do you need results in < 1 second?
├─ Yes → PyMuPDF4LLM
└─ No
   ├─ Do you need entity extraction?
   │  ├─ Yes → LlamaParse
   │  └─ No
   │     ├─ Do you need complex table analysis?
   │     │  ├─ Yes → LlamaParse  
   │     │  └─ No
   │     │     ├─ Is cost a major concern?
   │     │     │  ├─ Yes → PyMuPDF4LLM
   │     │     │  └─ No → LlamaParse (better quality)

Best Practice Recommendations

Development: Start with PyMuPDF4LLM for rapid iteration
Production: Use LlamaParse for comprehensive analysis
Batch Processing: PyMuPDF4LLM for speed, LlamaParse for accuracy
User-Facing: PyMuPDF4LLM for previews, LlamaParse for detailed views
Cost-Sensitive: PyMuPDF4LLM with LlamaParse for critical documents only

Migration Path

From PyMuPDF4LLM to LlamaParse

# Current PyMuPDF4LLM usage
result = docsray.extract("doc.pdf", provider="pymupdf4llm")
text = result['extraction']['text']

# Migrate to LlamaParse for better results
result = docsray.xray("doc.pdf", provider="llama-parse")
text = result['analysis']['full_extraction']['text']
entities = result['analysis']['extracted_content']['entities']
tables = result['analysis']['full_extraction']['tables']

Gradual Migration Strategy

Phase 1: Keep PyMuPDF4LLM for all operations
Phase 2: Add LlamaParse for critical documents
Phase 3: Use LlamaParse as primary, PyMuPDF4LLM as fallback
Phase 4: Full LlamaParse adoption with cost optimization

Next Steps

Learn about Tools Overview to understand which tools work best with each provider
See Performance Optimization for provider-specific tuning
Check Caching Guide to optimize LlamaParse costs
Review API Reference for all configuration options

Provider Comparison

Quick Comparison​

Detailed Feature Comparison​

Text Extraction​

PyMuPDF4LLM​

LlamaParse​

Table Processing​

PyMuPDF4LLM Table Handling​

LlamaParse Table Handling​

Entity Recognition​

PyMuPDF4LLM​

LlamaParse​

Image Processing​

PyMuPDF4LLM​

LlamaParse​

Performance Comparison​

Processing Speed​

Memory Usage​

Accuracy Comparison​

Cost Analysis​

PyMuPDF4LLM Costs​

LlamaParse Costs​

Use Case Decision Matrix​

Choose PyMuPDF4LLM When:​

Choose LlamaParse When:​

Hybrid Approaches​

Sequential Processing​

Fallback Strategy​

Parallel Processing​

Format Support Comparison​

PyMuPDF4LLM Supported Formats​

LlamaParse Supported Formats​

Quality Metrics​

Text Extraction Quality​

Table Extraction Quality​

Recommendation Framework​

Start with This Decision Tree:​

Best Practice Recommendations​

Migration Path​

From PyMuPDF4LLM to LlamaParse​

Gradual Migration Strategy​

Next Steps​

Quick Comparison

Detailed Feature Comparison

Text Extraction

PyMuPDF4LLM

LlamaParse

Table Processing

PyMuPDF4LLM Table Handling

LlamaParse Table Handling

Entity Recognition

PyMuPDF4LLM

LlamaParse

Image Processing

PyMuPDF4LLM

LlamaParse

Performance Comparison

Processing Speed

Memory Usage

Accuracy Comparison

Cost Analysis

PyMuPDF4LLM Costs

LlamaParse Costs

Use Case Decision Matrix

Choose PyMuPDF4LLM When:

Choose LlamaParse When:

Hybrid Approaches

Sequential Processing

Fallback Strategy

Parallel Processing

Format Support Comparison

PyMuPDF4LLM Supported Formats

LlamaParse Supported Formats

Quality Metrics

Text Extraction Quality

Table Extraction Quality

Recommendation Framework

Start with This Decision Tree:

Best Practice Recommendations

Migration Path

From PyMuPDF4LLM to LlamaParse

Gradual Migration Strategy

Next Steps