What are the main differences between Deepseek and ChatGPT for web scraping?

When choosing an LLM for web scraping tasks, developers often compare Deepseek and ChatGPT (OpenAI's GPT models). Both can extract structured data from HTML, but they differ significantly in pricing, performance, context handling, and API features. This guide explores the key differences to help you choose the right model for your scraping needs.

Overview of Each Model

ChatGPT (GPT-4/GPT-3.5) is OpenAI's flagship language model, widely used for various AI tasks including data extraction from web pages. It offers robust API support, function calling capabilities, and consistent performance across different data extraction scenarios.

Deepseek is a newer LLM provider that has gained attention for its competitive pricing and strong performance on technical tasks. While less established than OpenAI, Deepseek offers an API that can handle web scraping and data extraction tasks efficiently.

Key Differences for Web Scraping

1. Pricing and Cost Efficiency

The most significant difference between these models is cost structure:

ChatGPT Pricing (as of 2025): - GPT-4 Turbo: ~$10 per 1M input tokens, ~$30 per 1M output tokens - GPT-3.5 Turbo: ~$0.50 per 1M input tokens, ~$1.50 per 1M output tokens

Deepseek Pricing: - Deepseek Chat: ~$0.14 per 1M input tokens, ~$0.28 per 1M output tokens - Deepseek Coder: ~$0.14 per 1M input tokens, ~$0.28 per 1M output tokens

For large-scale web scraping projects, Deepseek can reduce API costs by 90-95% compared to GPT-4 and 70-80% compared to GPT-3.5. When scraping thousands of pages daily, this cost difference becomes substantial.

Example Cost Calculation:

# Scraping 1000 pages per day, 5000 tokens per page average

pages_per_day = 1000
tokens_per_page = 5000
total_tokens = pages_per_day * tokens_per_page * 30  # Monthly

# ChatGPT GPT-3.5
chatgpt_cost = (total_tokens / 1_000_000) * 1.50
print(f"ChatGPT monthly cost: ${chatgpt_cost}")  # ~$225

# Deepseek
deepseek_cost = (total_tokens / 1_000_000) * 0.28
print(f"Deepseek monthly cost: ${deepseek_cost}")  # ~$42

print(f"Savings: ${chatgpt_cost - deepseek_cost}")  # ~$183/month

2. Context Window Size

Context window determines how much HTML content you can send in a single request:

• ChatGPT GPT-4 Turbo: 128K tokens (~300-400 pages of text)
• ChatGPT GPT-3.5 Turbo: 16K tokens (~40-50 pages of text)
• Deepseek Chat: 32K tokens (~80-100 pages of text)

For most web scraping scenarios, you'll be sending individual HTML pages (typically 2K-10K tokens), making all three models suitable. However, if you need to process very large pages or multiple pages in a single request, GPT-4 Turbo offers the largest context window.

3. Accuracy and Data Extraction Quality

ChatGPT (GPT-4) generally provides the highest accuracy for complex extraction tasks: - Better at understanding nuanced instructions - More reliable for multi-step extraction logic - Lower hallucination rates on edge cases

ChatGPT (GPT-3.5) offers good accuracy for straightforward extraction: - Reliable for well-structured HTML - Occasional issues with complex nested structures - May struggle with ambiguous content

Deepseek performs competitively on technical content: - Excellent for structured data extraction - Strong performance on code-heavy pages - May have slightly higher error rates on natural language edge cases

Practical Example:

// Using ChatGPT for complex product extraction
const openai = require('openai');

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

async function extractProductData(html) {
  const response = await client.chat.completions.create({
    model: "gpt-4-turbo-preview",
    messages: [
      {
        role: "system",
        content: "Extract product data from HTML into JSON format."
      },
      {
        role: "user",
        content: `Extract title, price, description, and reviews from:\n${html}`
      }
    ],
    response_format: { type: "json_object" }
  });

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

# Using Deepseek for cost-effective extraction
import requests
import json

def extract_with_deepseek(html):
    url = "https://api.deepseek.com/v1/chat/completions"

    payload = {
        "model": "deepseek-chat",
        "messages": [
            {
                "role": "system",
                "content": "Extract product data from HTML into JSON format."
            },
            {
                "role": "user",
                "content": f"Extract title, price, description, and reviews from:\n{html}"
            }
        ],
        "response_format": {"type": "json_object"}
    }

    headers = {
        "Authorization": f"Bearer {DEEPSEEK_API_KEY}",
        "Content-Type": "application/json"
    }

    response = requests.post(url, json=payload, headers=headers)
    return json.loads(response.json()['choices'][0]['message']['content'])

4. Speed and Latency

Response times vary based on model complexity:

• GPT-4: 2-8 seconds per request (slower but more accurate)
• GPT-3.5: 0.5-2 seconds per request (fast and efficient)
• Deepseek: 1-3 seconds per request (competitive speed)

For real-time scraping or high-volume operations, GPT-3.5 typically offers the fastest response times. Deepseek provides good speed at a lower cost point.

5. API Features and Integration

ChatGPT API Features: - Function calling: Define structured schemas for extraction - JSON mode: Guaranteed JSON responses - Vision API: Can process screenshots alongside HTML - Streaming responses: Get partial results as they're generated - Fine-tuning: Custom model training available

Deepseek API Features: - JSON mode: Structured output support - OpenAI-compatible API: Easy migration from ChatGPT - Function calling: Available in recent versions - Standard REST API: Straightforward integration

Both APIs follow similar patterns, making it relatively easy to switch between them.

6. Function Calling for Structured Extraction

Both models support function calling, which is crucial for reliable web scraping:

# ChatGPT with function calling
import openai

client = openai.OpenAI()

tools = [
    {
        "type": "function",
        "function": {
            "name": "extract_article",
            "description": "Extract article data from HTML",
            "parameters": {
                "type": "object",
                "properties": {
                    "title": {"type": "string"},
                    "author": {"type": "string"},
                    "publish_date": {"type": "string"},
                    "content": {"type": "string"},
                    "tags": {"type": "array", "items": {"type": "string"}}
                },
                "required": ["title", "content"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": f"Extract article from: {html}"}],
    tools=tools,
    tool_choice={"type": "function", "function": {"name": "extract_article"}}
)

Function calling ensures you receive structured data in the exact format you need, reducing post-processing.

7. Use Case Recommendations

Choose ChatGPT GPT-4 when: - Accuracy is critical (financial data, medical information) - Working with highly unstructured or ambiguous content - Processing very large HTML documents (>50K tokens) - Budget allows for premium pricing

Choose ChatGPT GPT-3.5 when: - Need fast response times for real-time applications - Working with well-structured HTML - Moderate budget constraints - Processing standard e-commerce or news sites

Choose Deepseek when: - Cost efficiency is a top priority - Scraping large volumes of pages daily - Working with technical or code-heavy content - Testing and prototyping scraping workflows

8. Handling Dynamic Content

When scraping JavaScript-rendered websites, you'll typically use tools like Puppeteer or Selenium to render the page first, then pass the HTML to the LLM. Both Deepseek and ChatGPT can process the rendered HTML effectively:

const puppeteer = require('puppeteer');

async function scrapeWithLLM(url, llmExtractor) {
  const browser = await puppeteer.launch();
  const page = await browser.newPage();

  await page.goto(url, { waitUntil: 'networkidle0' });
  const html = await page.content();

  await browser.close();

  // Pass to your chosen LLM (ChatGPT or Deepseek)
  return await llmExtractor(html);
}

For complex JavaScript applications, you might need to handle AJAX requests using Puppeteer to ensure all dynamic content is loaded before extraction.

9. Error Handling and Reliability

ChatGPT has more mature infrastructure: - Higher uptime (99.9%+) - Better rate limit handling - More detailed error messages

Deepseek is improving but less established: - Occasional API instability - Standard error responses - Growing infrastructure

For production systems, implement robust retry logic regardless of provider:

import time
from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10))
def extract_with_retry(html, api_function):
    try:
        return api_function(html)
    except Exception as e:
        print(f"Extraction failed: {e}")
        raise

10. Hybrid Approaches

Many developers use both models strategically:

1. Deepseek for bulk extraction (90% of pages)
2. ChatGPT GPT-4 for complex cases (10% requiring high accuracy)
3. GPT-3.5 for real-time processing (user-facing features)

def smart_extract(html, complexity_score):
    if complexity_score > 0.8:
        # Use GPT-4 for complex pages
        return extract_with_chatgpt_4(html)
    elif complexity_score > 0.5:
        # Use GPT-3.5 for moderate complexity
        return extract_with_chatgpt_35(html)
    else:
        # Use Deepseek for simple pages
        return extract_with_deepseek(html)

Alternative: Specialized Web Scraping APIs

While LLMs are powerful for data extraction, specialized APIs like WebScraping.AI can offer advantages:

• Pre-optimized for web scraping (proxy rotation, JavaScript rendering)
• Predictable pricing (no token counting required)
• Built-in anti-bot bypassing and CAPTCHA handling
• Combined LLM and traditional parsing for best results

For production web scraping workflows, consider combining traditional scraping tools with LLM-based extraction for optimal cost and performance.

Conclusion

Deepseek and ChatGPT both excel at web scraping tasks, but serve different needs:

• Deepseek: Best for high-volume, cost-sensitive projects with structured data
• GPT-3.5: Balanced speed and accuracy for general-purpose scraping
• GPT-4: Premium accuracy for complex or mission-critical extraction

Most developers find success using a hybrid approach, leveraging Deepseek's cost efficiency for bulk processing while reserving ChatGPT for complex edge cases. Test both with your specific HTML structures to determine which performs best for your use case.

For maximum flexibility, design your scraping pipeline with provider abstraction so you can switch between models based on cost, performance, and accuracy requirements as your project evolves.