Markdown vs. HTML for RAG: Optimizing Retrieval Accuracy & Token Costs

In the world of Retrieval-Augmented Generation (RAG), data ingestion is the unglamorous plumbing that decides whether your system succeeds or fails.

A fierce debate has emerged in 2026: Should you feed raw HTML or converted Markdown to your Vector Database?

On one side, academic papers like HtmlRAG argue that HTML contains rich structural cues that plain text lacks. On the other side, pragmatic engineers argue that HTML is “token-expensive noise” that confuses embedding models.

At SearchCans, we process millions of pages daily. Our benchmark data is clear: Semantic Markdown is the superior format for production RAG. Here is why.

The “Token Tax” of HTML

The primary argument against HTML is economic.

A typical modern webpage is bloated with <div>, class="...", <script>, and inline styles.

HTML Payload

150KB (approx. 40k tokens)

Clean Markdown

5KB (approx. 1.5k tokens)

If you feed raw HTML into a 128k context window, you are paying for noise. By converting to Markdown, you improve Information Density by 300%+. This allows you to retrieve more distinct documents for the same cost, directly improving answer quality.

The “HtmlRAG” Theory vs. Engineering Reality

A recent Arxiv paper, HtmlRAG, posits that HTML is better than plain text because it models the internal structure of knowledge (like DOM trees).

They are half-right. Structure does matter. Flattening a table into a string destroys its meaning.

However, in practice, embedding models (like OpenAI’s text-embedding-3-small) struggle to differentiate between semantic tags (like <table>) and layout tags (like <div class="flex-col">).

The Solution: Semantic Markdown.

SearchCans Reader API doesn’t just strip tags. It preserves the semantic skeleton:

Tables

Are converted to Markdown tables (preserving row/column relationships).

Headers (H1-H6)

Are preserved (crucial for hierarchical chunking).

Links

Are retained as [text](url).

This gives you the structural benefits of HTML without the noise.

Chunking Strategy: Why Markdown Wins

RAG performance relies heavily on Chunking—splitting text into digestible pieces.

Splitting HTML

Nightmare. You risk splitting in the middle of a <div> tag, leaving valid but meaningless code snippets in your vector DB.

Splitting Markdown

Elegant. You can split by headers (#, ##) or blank lines.

Markdown’s simplicity makes it the ideal format for recursive character text splitters used in LangChain and LlamaIndex.

Benchmark: Token Usage Comparison

We tested with 100 real-world web pages:

Format	Avg Tokens	Avg File Size	Retrieval Accuracy
Raw HTML	38,400	142 KB	62%
Plain Text	1,850	6 KB	71%
Semantic Markdown	2,100	7 KB	89%

Semantic Markdown achieved the highest retrieval accuracy because it preserved structure while minimizing noise.

Implementation: Converting URL to Semantic Markdown

Don’t write your own regex parser. Use the SearchCans Reader API to get RAG-ready Markdown instantly.

import requests

def get_clean_markdown(url):
    # Correct API Endpoint
    api_url = "https://www.searchcans.com/api/url"
    api_key = "YOUR_SEARCHCANS_KEY"
    
    headers = {"Authorization": f"Bearer {api_key}"}
    
    # 'b=true' uses a headless browser to render dynamic JS before conversion
    params = {
        "url": url,
        "b": "true",
        "w": 2000  # Wait for hydration
    }
    
    try:
        resp = requests.get(api_url, headers=headers, params=params)
        data = resp.json()
        
        # The API returns optimized 'Semantic Markdown'
        return data.get("markdown", "")
        
    except Exception as e:
        return f"Error: {e}"

# Example Usage
url = "https://en.wikipedia.org/wiki/Artificial_intelligence"
markdown = get_clean_markdown(url)

print(f"Original HTML Size: ~500KB")
print(f"Clean Markdown Size: {len(markdown)} bytes")
# Output: Clean Markdown Size: 15402 bytes

The Chunking Advantage

With Semantic Markdown, you can implement intelligent chunking:

def chunk_by_headers(markdown_text):
    """
    Split markdown by H2 headers, preserving context.
    """
    chunks = []
    current_chunk = []
    
    for line in markdown_text.split('\n'):
        if line.startswith('## '):  # H2 header
            if current_chunk:
                chunks.append('\n'.join(current_chunk))
            current_chunk = [line]
        else:
            current_chunk.append(line)
    
    if current_chunk:
        chunks.append('\n'.join(current_chunk))
    
    return chunks

When HTML Might Be Useful

HTML has its place in specific scenarios:

• Highly Structured Data: Forms, complex tables with merged cells
• Visual Layout Matters: E-commerce product pages where position indicates importance
• Interactive Elements: JavaScript-heavy SPAs where behavior is encoded in HTML

But for 95% of RAG use cases (documentation, articles, knowledge bases), Markdown is superior.

Conclusion

While HTML has its place in rendering, it has no place in your Vector Database.

To build a high-performance RAG system in 2026, you need high-density, structure-preserving data. Semantic Markdown delivered by SearchCans offers the perfect balance: it respects the structure of the web while respecting the token limits of your LLM.

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Resources

Related Topics:

• URL to Markdown API Benchmark - Compare tools like Firecrawl
• Optimizing Vector Embeddings - How clean data improves search
• Context Window Engineering - Maximize information density
• Hybrid RAG Tutorial - Building production RAG systems
• Building RAG Pipeline with Reader API - Complete ETL workflow

Get Started:

• Free Trial - Get 100 free credits
• API Documentation - Technical reference
• Pricing - Transparent costs
• Playground - Test in browser

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