I Compared China’s AI to ChatGPT on 5 Tasks and a Clear Winner Emerged

In the rapidly evolving landscape of artificial intelligence, a new contender has emerged from China that's making waves in the global AI community. DeepSeek, launched in January 2025, has quickly risen to prominence, challenging the dominance of OpenAI's ChatGPT. As an AI prompt engineer with years of experience working with various language models, I was intrigued by DeepSeek's claims and decided to put it to the test against ChatGPT across five different tasks. The results were eye-opening and may signal a significant shift in the AI landscape.

The Rise of DeepSeek: China's AI Challenger

Before diving into the comparison, it's crucial to understand the context of DeepSeek's emergence:

• Launched in January 2025
• Developed in China
• Gained over 1 million downloads in just five days
• Reportedly cost only $5.5 million to create (compared to GPT-4's $100 million)
• Features a distinctive blue whale logo, reminiscent of the biblical Jonah story

DeepSeek's rapid rise and cost-effective development have sent shockwaves through the tech industry, prompting many to reevaluate the current state of AI development and competition.

Task 1: Creative Writing

The Challenge

I prompted both AIs to write a short story about a time traveler who accidentally changes history.

Results

ChatGPT's Response:

The chronometer on Dr. Eliza Chen's wrist beeped urgently as she materialized in 1955 Chicago. Her mission was simple: observe the Great Chicago Fire's aftermath. But as she rounded a corner, she collided with a young man carrying blueprints.

"I'm so sorry!" Eliza exclaimed, helping him gather the scattered papers. Her eyes widened as she read the name: "Daniel Burnham?"

The man nodded, "Yes, that's me. Are you interested in architecture?"

Eliza's heart raced. She'd just met the future designer of Chicago's revolutionary city plan. In her excitement, she blurted out, "Your work will reshape this city!"

Daniel's eyes lit up. "Really? I've been considering a different career, but maybe I should stick with architecture after all."

As Eliza vanished back to 2025, she realized her mistake. The Chicago she returned to was unrecognizable – a sprawling, disorganized metropolis. In her enthusiasm, she'd altered the course of urban planning history.

DeepSeek's Response:

Dr. Maya Wong adjusted her temporal displacement device and stepped into the shimmering portal. Her destination: Philadelphia, July 3, 1776. Her mission: to observe the final edits of the Declaration of Independence.

Materializing in a quiet alley, Maya made her way to Independence Hall. As she approached, she noticed a delegate hurrying away, looking flustered. Curiosity got the better of her, and she followed.

"Excuse me, sir," she called out. "Is everything alright?"

The man turned, revealing a face Maya recognized from history books – Thomas Jefferson.

"I've misplaced my drafts," he admitted, panic in his voice. "The final review is tomorrow, and I can't remember all the changes!"

Without thinking, Maya reached into her bag and pulled out a backup copy of the Declaration she'd brought for reference. "Perhaps this could help?" she offered.

Jefferson's eyes widened as he scanned the document. "This is... remarkable. It's exactly what we need. How did you...?"

Before Maya could respond, she felt the familiar pull of the temporal field. As she faded from 1776, she saw Jefferson hurrying back to Independence Hall, her futuristic copy in hand.

Returning to 2025, Maya's heart sank. The American flag now bore 15 stripes instead of 13, and her history books spoke of the "Wong Declaration of Independence." In her eagerness to help, she had inadvertently become a founding mother of a slightly different United States.

Analysis

Both AIs produced engaging short stories that effectively captured the essence of accidental time travel consequences. However, DeepSeek's story showed a slightly more nuanced understanding of historical context and provided more vivid details. The inclusion of the "Wong Declaration of Independence" and the altered flag demonstrated a creative approach to showing the consequences of the time traveler's actions.

Task 2: Technical Explanation

The Challenge

I asked both AIs to explain quantum computing in simple terms for a general audience.

Results

ChatGPT's Response:

Quantum computing is like having a super-powerful calculator that can solve incredibly complex problems much faster than regular computers. Here's how it works in simple terms:

1. Regular Bits vs. Qubits:
   - Normal computers use bits (0s and 1s)
   - Quantum computers use qubits, which can be 0, 1, or both at the same time

2. Superposition:
   - Qubits can exist in multiple states simultaneously
   - This allows quantum computers to process many possibilities at once

3. Entanglement:
   - Qubits can be linked, even at a distance
   - Changes to one qubit instantly affect its entangled partner

4. Quantum Advantage:
   - For certain problems, quantum computers can find solutions much faster
   - Examples include cryptography, drug discovery, and financial modeling

5. Challenges:
   - Qubits are very sensitive to their environment
   - Keeping them stable is a major hurdle in quantum computing

In essence, quantum computers harness the weird properties of quantum physics to perform calculations in ways that classical computers cannot, potentially revolutionizing fields like medicine, finance, and artificial intelligence.

DeepSeek's Response:

Imagine you're trying to find your way out of a huge maze. A classical computer would explore one path at a time, backtracking when it hits a dead end. This could take a very long time for a complex maze.

Now, picture a quantum computer as a magical mist that can spread through every path of the maze simultaneously. When the mist reaches the exit, it instantly knows the correct path, collapsing all other possibilities.

This is the power of quantum computing, and here's how it works in simple terms:

1. Qubits: The building blocks of quantum computers. Unlike classical bits (0 or 1), qubits can exist in multiple states at once, like a coin spinning on its edge.

2. Superposition: This is the "magical mist" property. It allows qubits to explore multiple possibilities simultaneously.

3. Entanglement: Qubits can be linked in such a way that the state of one instantly affects the other, no matter the distance between them. It's like having psychic twins solving a puzzle together.

4. Quantum Algorithms: Special instructions designed to take advantage of superposition and entanglement to solve problems exponentially faster than classical computers.

5. Potential Applications:
   - Breaking complex encryption
   - Simulating molecular structures for drug discovery
   - Optimizing financial portfolios
   - Improving machine learning and AI

While still in its early stages, quantum computing promises to revolutionize fields like cryptography, chemistry, and artificial intelligence by solving problems that are currently intractable for classical computers.

Analysis

Both AIs provided clear and accessible explanations of quantum computing. However, DeepSeek's response stood out for its use of the maze analogy, which effectively conveyed the concept of superposition. The "magical mist" metaphor was particularly effective in helping a layperson understand the fundamental difference between classical and quantum computing.

Task 3: Data Analysis

The Challenge

I provided both AIs with a fictional dataset of sales figures and asked them to analyze trends and provide insights.

Results

ChatGPT's Response:

Based on the provided sales data, here are the key trends and insights:

1. Overall Growth:
   - Total sales increased by 15% year-over-year
   - Q4 showed the highest growth at 22% compared to the previous year

2. Product Category Performance:
   - Electronics: Strongest performer with 25% YoY growth
   - Clothing: Modest growth of 8% YoY
   - Home Goods: Slight decline of 2% YoY

3. Seasonal Patterns:
   - Q4 consistently outperforms other quarters across all categories
   - Electronics sales spike in Q4, likely due to holiday shopping
   - Clothing sales show a secondary peak in Q2, possibly due to summer fashion

4. Regional Variations:
   - West Coast: Highest overall sales, strong in electronics
   - Midwest: Strongest growth in clothing category
   - Southeast: Underperforming in home goods

5. Customer Segment Analysis:
   - Young professionals (25-34): Driving growth in electronics
   - Families (35-49): Stable across all categories
   - Seniors (65+): Increasing engagement in home goods

Recommendations:
1. Invest in expanding electronics inventory for Q4
2. Develop targeted marketing for clothing in the Midwest region
3. Investigate reasons for home goods decline in the Southeast
4. Create tailored promotions for young professionals in the electronics category
5. Explore ways to boost home goods appeal to the growing senior market

DeepSeek's Response:

Analysis of the provided sales data reveals several interesting trends and insights:

1. Year-over-Year Performance:
   - Overall sales growth: 17.3%
   - Highest growing category: Electronics (+29.8%)
   - Lowest performing category: Home Goods (+3.1%)

2. Quarterly Trends:
   - Q4 dominates sales across all categories (avg. 35% of annual sales)
   - Q1 consistently the weakest (avg. 18% of annual sales)
   - Electronics show highest quarterly volatility (CV = 0.42)

3. Regional Disparities:
   - West Coast leads in total sales volume (31% of total)
   - Northeast shows highest growth rate (22.5% YoY)
   - Midwest underperforming in Electronics category (-5% YoY)

4. Product Category Insights:
   - Electronics:
     * Driving overall growth
     * Showing signs of market saturation in Midwest
   - Clothing:
     * Steady performer (12.7% YoY growth)
     * Strong Q2 performance indicates seasonal demand
   - Home Goods:
     * Lagging behind other categories
     * Opportunity for growth in Southeast region

5. Customer Segment Analysis:
   - 25-34 age group: Highest spend on Electronics
   - 35-49 age group: Balanced spending across categories
   - 50-64 age group: Increasing Home Goods purchases

6. Emerging Trends:
   - Rise in eco-friendly product sales across all categories (+45% YoY)
   - Increasing adoption of buy-now-pay-later options, especially in Electronics

Recommendations:
1. Develop targeted marketing campaigns for Electronics in the Midwest to reverse negative trend
2. Investigate Home Goods product mix in Southeast for potential improvements
3. Launch Q2 promotion for Clothing category to capitalize on seasonal demand
4. Expand eco-friendly product lines across all categories
5. Explore partnerships with buy-now-pay-later providers to boost Electronics sales
6. Tailor marketing messages to age-specific preferences identified in the analysis

Analysis

Both AIs provided comprehensive analyses of the fictional sales data. However, DeepSeek's response was more detailed and nuanced. It included additional metrics such as the coefficient of variation for quarterly sales and specific growth percentages. DeepSeek also identified emerging trends like the rise of eco-friendly products and buy-now-pay-later options, which weren't mentioned in the ChatGPT response. This demonstrates DeepSeek's ability to extrapolate insights beyond the immediate data provided.

Task 4: Code Generation

The Challenge

I asked both AIs to generate a Python script that would scrape a website for product information and store it in a CSV file.

Results

ChatGPT's Response:

import requests
from bs4 import BeautifulSoup
import csv

def scrape_product_info(url):
    # Send a GET request to the URL
    response = requests.get(url)
    
    # Parse the HTML content
    soup = BeautifulSoup(response.content, 'html.parser')
    
    # Find all product containers
    products = soup.find_all('div', class_='product-container')
    
    # Prepare a list to store product information
    product_list = []
    
    # Extract information from each product
    for product in products:
        name = product.find('h2', class_='product-name').text.strip()
        price = product.find('span', class_='product-price').text.strip()
        rating = product.find('div', class_='product-rating').text.strip()
        
        product_list.append([name, price, rating])
    
    return product_list

def save_to_csv(product_list, filename):
    # Write the product information to a CSV file
    with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
        writer = csv.writer(csvfile)
        writer.writerow(['Name', 'Price', 'Rating'])  # Write header
        writer.writerows(product_list)

# URL of the website to scrape
url = 'https://example.com/products'

# Scrape product information
products = scrape_product_info(url)

# Save the information to a CSV file
save_to_csv(products, 'product_info.csv')

print("Scraping completed. Data saved to product_info.csv")

DeepSeek's Response:

import requests
from bs4 import BeautifulSoup
import csv
import time
from urllib.parse import urljoin

def scrape_product_info(base_url, max_pages=5):
    all_products = []
    page = 1

    while page <= max_pages:
        url = f"{base_url}?page={page}"
        response = requests.get(url)
        
        if response.status_code != 200:
            print(f"Failed to retrieve page {page}. Stopping.")
            break

        soup = BeautifulSoup(response.content, 'html.parser')
        products = soup.find_all('div', class_='product-item')

        if not products:
            print(f"No more products found on page {page}. Stopping.")
            break

        for product in products:
            name = product.find('h2', class_='product-name').text.strip()
            price = product.find('span', class_='product-price').text.strip()
            rating = product.find('div', class_='product-rating').get('data-rating', 'N/A')
            image_url = urljoin(base_url, product.find('img', class_='product-image')['src'])
            
            all_products.append({
                'name': name,
                'price': price,
                'rating': rating,
                'image_url': image_url
            })

        print(f"Scraped page {page}")
        page += 1
        time.sleep(1)  # Be polite to the server

    return all_products

def save_to_csv(products, filename):
    with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
        fieldnames = ['name', 'price', 'rating', 'image_url']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        
        writer.writeheader()
        for product in products:
            writer.writerow(product)

if __name__ == "__main__":
    base_url = 'https://example.com/products'
    products = scrape_product_info(base_url)
    save_to_csv(products, 'product_info.csv')
    print(f"Scraping completed. {len(products)} products saved to product_info.csv")

Analysis

Both AIs generated functional Python scripts for web scraping. However, DeepSeek's script included several additional features that demonstrate a more advanced understanding of web scraping best practices:

1. Pagination handling: DeepSeek's script can scrape multiple pages of products.
2. Error handling: It checks for successful HTTP responses and stops if no more products are found.
3. Rate limiting: A sleep function is included to avoid overwhelming the server.
4. More comprehensive data extraction: It also scrapes the product image URL.
5. Use of dictionaries: This makes the code more readable and maintainable.

These additions make DeepSeek's script more robust and suitable for real-world application.

Task 5: Language Translation and Cultural Nuance

The Challenge

I asked both AIs to translate a colloquial English phrase into Mandarin Chinese and explain any cultural nuances.

Results

ChatGPT's Response:

English phrase: "It's not my cup of tea."

Mandarin translation: "这不是我的菜。" (Zhè bú shì wǒ de cài.)

Literal translation: "This is not