Deepseek vs OpenAI: Which LLM is Better for Web Scraping?

When choosing between Deepseek and OpenAI for web scraping and data extraction tasks, developers need to consider several factors including cost, performance, accuracy, context window size, and API capabilities. Both LLMs offer powerful natural language processing capabilities, but they excel in different scenarios. This comprehensive guide compares both platforms to help you make an informed decision.

Overview of Deepseek and OpenAI for Web Scraping

Deepseek Models

Deepseek is a Chinese AI company that has released several powerful open-source models, including:

Deepseek-V3: The latest flagship model with 671B parameters and a 128K token context window
Deepseek-R1: A reasoning-focused model designed for complex analytical tasks
Deepseek-Coder: Specialized for code generation and technical tasks

OpenAI Models

OpenAI offers several models through their API:

GPT-4 Turbo: Advanced reasoning with 128K context window
GPT-4o: Optimized for speed and cost
GPT-3.5 Turbo: Fast and economical for simpler tasks

Cost Comparison

One of the most significant differences between Deepseek and OpenAI is pricing. For large-scale web scraping projects, cost efficiency is crucial.

Deepseek Pricing

Deepseek offers highly competitive pricing:

Input tokens: $0.27 per million tokens
Output tokens: $1.10 per million tokens
Cache hits: $0.014 per million tokens (significant savings for repeated content)

OpenAI Pricing

OpenAI's pricing varies by model:

GPT-4 Turbo: - Input tokens: $10.00 per million tokens - Output tokens: $30.00 per million tokens

GPT-4o: - Input tokens: $2.50 per million tokens - Output tokens: $10.00 per million tokens

GPT-3.5 Turbo: - Input tokens: $0.50 per million tokens - Output tokens: $1.50 per million tokens

Cost Winner: Deepseek is significantly cheaper, costing approximately 10-40x less than OpenAI's models. For web scraping at scale, this can translate to thousands of dollars in savings.

Performance and Accuracy

Data Extraction Accuracy

Both platforms excel at structured data extraction from HTML, but with different strengths:

Deepseek Strengths: - Excellent at technical and code-related content - Strong performance on structured data extraction - Good at following complex instructions - Competitive with GPT-4 on many benchmarks

OpenAI Strengths: - Superior natural language understanding - Better at handling ambiguous or poorly structured content - More robust error handling - Stronger performance on nuanced extraction tasks

Speed and Latency

Deepseek: - Faster response times for most queries - Efficient token processing - Good throughput for batch operations

OpenAI: - GPT-3.5 Turbo: Fastest among OpenAI models - GPT-4o: Optimized balance of speed and quality - GPT-4 Turbo: Slower but most capable

Context Window and Token Limits

Both platforms now offer large context windows, crucial for processing entire web pages:

Deepseek-V3: 128K tokens (~96,000 words)
GPT-4 Turbo: 128K tokens
GPT-4o: 128K tokens
GPT-3.5 Turbo: 16K tokens

This parity means both platforms can handle large HTML documents, though Deepseek's lower cost per token makes it more economical for processing large pages.

Code Examples

Extracting Structured Data with Deepseek (Python)

import requests
import json

def scrape_with_deepseek(html_content, extraction_schema):
    """
    Extract structured data using Deepseek API
    """
    url = "https://api.deepseek.com/v1/chat/completions"
    headers = {
        "Authorization": "Bearer YOUR_DEEPSEEK_API_KEY",
        "Content-Type": "application/json"
    }

    prompt = f"""
    Extract the following information from the HTML:
    {json.dumps(extraction_schema, indent=2)}

    HTML Content:
    {html_content}

    Return the data as valid JSON only.
    """

    payload = {
        "model": "deepseek-chat",
        "messages": [
            {
                "role": "system",
                "content": "You are a web scraping expert. Extract data accurately and return valid JSON."
            },
            {
                "role": "user",
                "content": prompt
            }
        ],
        "temperature": 0.1,
        "response_format": {"type": "json_object"}
    }

    response = requests.post(url, headers=headers, json=payload)
    result = response.json()

    return json.loads(result['choices'][0]['message']['content'])

# Example usage
schema = {
    "product_name": "string",
    "price": "number",
    "availability": "boolean",
    "reviews_count": "number"
}

html = """
<div class="product">
    <h1>Premium Laptop</h1>
    <span class="price">$1,299.99</span>
    <p class="stock">In Stock</p>
    <div class="reviews">Based on 245 reviews</div>
</div>
"""

extracted_data = scrape_with_deepseek(html, schema)
print(json.dumps(extracted_data, indent=2))

Extracting Structured Data with OpenAI (JavaScript)

const OpenAI = require('openai');

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

async function scrapeWithOpenAI(htmlContent, extractionSchema) {
  const prompt = `
    Extract the following information from the HTML:
    ${JSON.stringify(extractionSchema, null, 2)}

    HTML Content:
    ${htmlContent}

    Return the data as valid JSON only.
  `;

  const completion = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [
      {
        role: "system",
        content: "You are a web scraping expert. Extract data accurately and return valid JSON."
      },
      {
        role: "user",
        content: prompt
      }
    ],
    temperature: 0.1,
    response_format: { type: "json_object" }
  });

  return JSON.parse(completion.choices[0].message.content);
}

// Example usage
const schema = {
  product_name: "string",
  price: "number",
  availability: "boolean",
  reviews_count: "number"
};

const html = `
<div class="product">
    <h1>Premium Laptop</h1>
    <span class="price">$1,299.99</span>
    <p class="stock">In Stock</p>
    <div class="reviews">Based on 245 reviews</div>
</div>
`;

scrapeWithOpenAI(html, schema)
  .then(data => console.log(JSON.stringify(data, null, 2)))
  .catch(error => console.error('Error:', error));

Use Case Recommendations

When to Choose Deepseek

High-volume scraping projects where cost is a primary concern
Technical documentation or code-heavy websites
Structured data extraction from well-formatted HTML
Budget-conscious projects requiring good performance
Batch processing of large numbers of pages

When to Choose OpenAI

Complex, unstructured content requiring nuanced understanding
High-stakes applications where maximum accuracy is critical
Multilingual scraping with complex language variations
Projects with existing OpenAI integrations
Content requiring advanced reasoning and context understanding

API Features Comparison

Function Calling

Both platforms support function calling for structured output:

Deepseek:

# Deepseek supports JSON mode and structured outputs
{
    "response_format": {"type": "json_object"}
}

OpenAI:

// OpenAI offers advanced function calling
{
    "tools": [{
        "type": "function",
        "function": {
            "name": "extract_product_data",
            "parameters": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "price": {"type": "number"}
                }
            }
        }
    }]
}

Rate Limits

Deepseek: - More generous rate limits for the price - Good for burst traffic - Enterprise options available

OpenAI: - Tiered rate limits based on usage history - Rate limits increase with spending - Enterprise plans with higher limits

Integration with Web Scraping Tools

Both LLMs integrate well with popular web scraping frameworks:

Python Integration Example

from selenium import webdriver
from bs4 import BeautifulSoup
import requests

def scrape_dynamic_page(url, llm_provider='deepseek'):
    """
    Scrape a dynamic page and extract data using LLM
    """
    # Use Selenium for JavaScript-rendered content
    driver = webdriver.Chrome()
    driver.get(url)

    # Wait for content to load
    driver.implicitly_wait(5)
    html = driver.page_source
    driver.quit()

    # Clean HTML with BeautifulSoup
    soup = BeautifulSoup(html, 'html.parser')
    cleaned_html = soup.prettify()

    # Extract with chosen LLM
    if llm_provider == 'deepseek':
        return scrape_with_deepseek(cleaned_html, schema)
    else:
        return scrape_with_openai(cleaned_html, schema)

For more complex scenarios involving dynamic content, you might want to explore how to handle AJAX requests using Puppeteer before processing with your chosen LLM.

Handling Large-Scale Scraping

Batch Processing with Deepseek

import asyncio
import aiohttp

async def batch_scrape_deepseek(urls, schema, batch_size=10):
    """
    Process multiple URLs concurrently with Deepseek
    """
    async def process_url(session, url):
        # Fetch HTML (simplified)
        async with session.get(url) as response:
            html = await response.text()

        # Extract with Deepseek
        return await scrape_with_deepseek_async(html, schema)

    async with aiohttp.ClientSession() as session:
        tasks = [process_url(session, url) for url in urls]
        results = await asyncio.gather(*tasks)

    return results

# Process 1000 pages
urls = [f"https://example.com/product/{i}" for i in range(1000)]
results = asyncio.run(batch_scrape_deepseek(urls, schema))

Cost Comparison for 1000 Pages

Assuming each page uses approximately 10K input tokens and generates 1K output tokens:

Deepseek: - Input: 10,000,000 tokens × $0.27/1M = $2.70 - Output: 1,000,000 tokens × $1.10/1M = $1.10 - Total: $3.80

OpenAI GPT-4o: - Input: 10,000,000 tokens × $2.50/1M = $25.00 - Output: 1,000,000 tokens × $10.00/1M = $10.00 - Total: $35.00

Savings with Deepseek: ~89%

Error Handling and Reliability

Both platforms require robust error handling:

import time
from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
def extract_with_retry(html, schema, provider='deepseek'):
    """
    Extract data with automatic retry logic
    """
    try:
        if provider == 'deepseek':
            return scrape_with_deepseek(html, schema)
        else:
            return scrape_with_openai(html, schema)
    except Exception as e:
        print(f"Error during extraction: {e}")
        raise

# Usage with fallback
def extract_with_fallback(html, schema):
    """
    Try Deepseek first, fall back to OpenAI if needed
    """
    try:
        return extract_with_retry(html, schema, 'deepseek')
    except Exception as e:
        print(f"Deepseek failed, trying OpenAI: {e}")
        return extract_with_retry(html, schema, 'openai')

Best Practices for Both Platforms

Minimize token usage: Clean HTML before sending to the LLM by removing scripts, styles, and unnecessary tags
Use caching: Both platforms offer caching mechanisms to reduce costs
Batch requests: Process multiple extractions in a single request when possible
Validate outputs: Always validate LLM-generated data against expected schemas
Monitor costs: Track API usage to avoid unexpected bills

HTML Cleaning Example

from bs4 import BeautifulSoup

def clean_html_for_llm(html):
    """
    Remove unnecessary elements to reduce token count
    """
    soup = BeautifulSoup(html, 'html.parser')

    # Remove scripts, styles, and comments
    for element in soup(['script', 'style', 'meta', 'link']):
        element.decompose()

    # Remove empty elements
    for element in soup.find_all():
        if not element.get_text(strip=True):
            element.decompose()

    # Get text with minimal formatting
    return soup.prettify()

# Reduce tokens by 50-70%
cleaned = clean_html_for_llm(raw_html)

Conclusion

Choose Deepseek if: - Cost efficiency is a priority - You're scraping technical or structured content - You need to process high volumes of pages - Performance is acceptable for your use case

Choose OpenAI if: - Maximum accuracy is critical - You're working with complex, unstructured content - You need the most advanced reasoning capabilities - Budget is less of a constraint

For many web scraping applications, Deepseek offers the best value proposition, delivering competitive accuracy at a fraction of the cost. However, OpenAI's GPT-4 models remain superior for complex extraction tasks requiring nuanced understanding.

The ideal approach for large-scale projects might be a hybrid strategy: use Deepseek for the majority of straightforward extractions, and reserve OpenAI for challenging edge cases or validation. When dealing with dynamic content, consider integrating your LLM workflow with tools that can handle browser sessions to ensure you're extracting from fully-rendered pages.

Ultimately, the choice between Deepseek and OpenAI should be based on your specific requirements, budget, and the complexity of your web scraping tasks. Both platforms continue to evolve, so staying informed about new releases and pricing changes is essential for optimizing your web scraping infrastructure.

Table of contents