ChatGPT vs DeepSeek: We Tested Both on 5 Real Tasks (2026)

ChatGPT vs DeepSeek: The Full Breakdown

DeepSeek exploded onto the scene in early 2025 and hasn’t stopped making waves. With DeepSeek-R1 matching GPT-4-class reasoning at a fraction of the cost, the question on everyone’s mind is: can DeepSeek actually replace ChatGPT?

We tested both models head-to-head on the same 5 tasks. No cherry-picking, no marketing spin — just real outputs you can judge for yourself.

Models tested: ChatGPT (GPT-4o, February 2026) vs DeepSeek-R1 (via chat.deepseek.com)

Quick Verdict

Test 1: Business Email Writing

Prompt: “Write a professional email to a client explaining a 2-week project delay due to supply chain issues. Keep it under 150 words, empathetic but confident.”

ChatGPT’s Output

Subject: Update on Project Timeline

Dear [Client Name],

I wanted to reach out personally regarding the timeline for your project. Due to unexpected supply chain disruptions affecting key materials, we’re looking at an adjusted delivery date of [new date] — approximately two weeks from our original schedule.

I understand this isn’t the news you were hoping for, and I want to assure you that we’re actively working with alternative suppliers to minimize any further delays. Your project remains our top priority.

We’ve already secured 80% of the materials needed and expect the remaining items within the next few days. I’ll keep you updated with weekly progress reports.

Please don’t hesitate to reach out if you have any questions or concerns. I’m happy to schedule a call to discuss this in more detail.

Best regards, [Your Name]

Word count: 138 | Tone: Professional, warm, reassuring

DeepSeek’s Output

Subject: Project Timeline Adjustment — [Project Name]

Dear [Client Name],

I’m writing to inform you of a two-week adjustment to our project timeline. Ongoing supply chain disruptions have delayed the delivery of critical components, pushing our estimated completion to [new date].

We understand the inconvenience this causes and are taking active steps to mitigate further delays, including sourcing from backup suppliers and reallocating internal resources.

Rest assured, the quality of deliverables remains uncompromised. We will provide bi-weekly updates going forward.

Please feel free to reach out with any concerns. We value your partnership and appreciate your understanding.

Regards, [Your Name]

Word count: 105 | Tone: Professional, direct, slightly formal

Verdict: ChatGPT wins 🏆

Both are solid, but ChatGPT’s version is warmer and more empathetic — the “I wanted to reach out personally” opener and specific detail about 80% materials secured builds more confidence. DeepSeek is competent but reads more like a template.

Score: ChatGPT 8/10 | DeepSeek 7/10

Test 2: Python Coding Challenge

Prompt: “Write a Python function that takes a list of stock prices (daily closing prices) and returns the maximum profit from a single buy-sell transaction. Include edge cases and time complexity analysis.”

ChatGPT’s Output

def max_profit(prices: list[float]) -> float:
    """
    Find the maximum profit from buying and selling a stock once.
    
    Args:
        prices: List of daily closing prices
    Returns:
        Maximum possible profit (0 if no profitable trade exists)
    
    Time Complexity: O(n)
    Space Complexity: O(1)
    """
    if not prices or len(prices) < 2:
        return 0
    
    min_price = prices[0]
    max_profit = 0
    
    for price in prices[1:]:
        if price < min_price:
            min_price = price
        else:
            profit = price - min_price
            if profit > max_profit:
                max_profit = profit
    
    return max_profit

Clean, correct, well-documented. Standard optimal solution.

DeepSeek’s Output

def max_profit(prices: list[float]) -> float:
    """
    Calculate maximum profit from a single buy-sell transaction.
    
    Uses Kadane's algorithm variant - tracks minimum price seen so far
    and computes max difference at each step.
    
    Args:
        prices: List of daily closing prices (chronological order)
    Returns:
        Maximum achievable profit. Returns 0 if no profitable trade
        exists or input is invalid.
    
    Time Complexity: O(n) - single pass through the array
    Space Complexity: O(1) - only two tracking variables
    
    Edge Cases:
        - Empty list or single element: returns 0
        - Monotonically decreasing prices: returns 0
        - All same prices: returns 0
        - Negative prices: handled correctly (though unusual)
    
    Examples:
        >>> max_profit([7, 1, 5, 3, 6, 4])
        5  # Buy at 1, sell at 6
        >>> max_profit([7, 6, 4, 3, 1])
        0  # No profitable trade
        >>> max_profit([])
        0
    """
    if not prices or len(prices) < 2:
        return 0
    
    min_price = float('inf')
    max_profit_val = 0
    
    for price in prices:
        min_price = min(min_price, price)
        max_profit_val = max(max_profit_val, price - min_price)
    
    return max_profit_val


# Test suite
if __name__ == "__main__":
    assert max_profit([7, 1, 5, 3, 6, 4]) == 5
    assert max_profit([7, 6, 4, 3, 1]) == 0
    assert max_profit([]) == 0
    assert max_profit([1]) == 0
    assert max_profit([2, 2, 2]) == 0
    assert max_profit([1, 2]) == 1
    assert max_profit([-3, -1, -4]) == 2  # -1 - (-3) = 2
    print("All tests passed!")

Verdict: DeepSeek wins 🏆

Both arrive at the same O(n) algorithm, but DeepSeek’s response is significantly more thorough: explicit edge case documentation, doctest examples, a complete test suite including negative prices, and a note about the Kadane’s algorithm connection. This is the difference between “correct code” and “production-ready code.”

Score: ChatGPT 7/10 | DeepSeek 9/10

Test 3: Logical Reasoning

Prompt: “A farmer has 17 sheep. All but 9 die. How many sheep does the farmer have left? Explain your reasoning step by step.”

ChatGPT’s Output

Let’s break this down:

1. The farmer starts with 17 sheep.
2. “All but 9 die” means every sheep dies except 9.
3. So 17 - 9 = 8 sheep died.
4. The farmer has 9 sheep remaining.

The key phrase is “all but 9” — this means 9 survived, not that 9 died. It’s a common trick question designed to make you subtract 9 from 17 and answer 8.

Correct. Clear explanation.

DeepSeek’s Output

Answer: 9 sheep.

Step-by-step reasoning:

1. Parse the language carefully. “All but 9 die” = “All except 9 die” = “Every sheep other than 9 of them dies.”

2. Identify the trap. The question tempts you to compute 17 - 9 = 8. But 9 isn’t the number that died — it’s the number that survived.

3. The number 17 is a red herring. It doesn’t matter how many sheep the farmer started with. The sentence “all but 9 die” already tells you the final count: 9.

4. Verification: If 9 survived out of 17, then 17 - 9 = 8 died. 8 died + 9 alive = 17 total. ✓

The question works as a trick because our brain instinctively wants to perform arithmetic with both numbers presented. The word “but” (meaning “except”) is the critical term to parse correctly.

Verdict: DeepSeek wins 🏆

Both get the right answer, but DeepSeek’s explanation is superior — it explicitly identifies the red herring, explains why the trap works psychologically, and includes a verification step. More educational and thorough.

Score: ChatGPT 8/10 | DeepSeek 9/10

Test 4: Creative Writing

Prompt: “Write the opening paragraph of a noir detective novel set in Tokyo, 2045. Make it atmospheric and include at least one metaphor.”

ChatGPT’s Output

The rain in Neo-Shinjuku fell like static from a broken broadcast — gray, relentless, full of nothing. I stood under the awning of a ramen joint that hadn’t served ramen in three years, watching the neon kanji bleed down the sides of buildings like mascara on a crying city. My name is Tanaka. I find things that don’t want to be found. Tonight, the thing was a girl named Yuki, last seen walking into the Kabukichō district with a smile that could cut glass and a data chip worth more than my apartment. The holographic billboards overhead cycled through their endless advertisements — skin cream, neural implants, happiness in a syringe — while below, the real Tokyo pulsed in the spaces between the light.

DeepSeek’s Output

Rain hammered the Shibuya overpass like a debt collector who knew you were home. I pressed deeper into the doorway of a shuttered pachinko parlor, letting the neon wash over me in sick waves of pink and green. Forty-eight hours without sleep and the city was starting to look honest — all its pretty facades dissolved, leaving nothing but concrete, rust, and the cold arithmetic of who owed what to whom. My phone buzzed: another message from a dead woman. In this line of work, that’s not a metaphor. Her name was Aiko Mori, and until last Tuesday, she’d been senior cryptographer at Mitsubishi Neural. Now she was a body in the Sumida River and a case file on my desk, and somehow, impossibly, she was still sending texts.

Verdict: ChatGPT wins (barely) 🏆

Both are genuinely good. ChatGPT’s prose is more polished and lyrical — “neon kanji bleed down buildings like mascara on a crying city” is a killer image. DeepSeek’s opening metaphor (“like a debt collector who knew you were home”) is arguably more original and noir-appropriate. DeepSeek also has a stronger narrative hook with the dead woman texting. This one’s almost a toss-up, but ChatGPT edges it on pure prose quality.

Score: ChatGPT 9/10 | DeepSeek 8.5/10

Test 5: Data Analysis & Summarization

Prompt: “Here’s quarterly revenue data for a fictional company: Q1 $2.3M, Q2 $2.1M, Q3 $2.8M, Q4 $3.5M. Analyze the trend, calculate YoY growth if last year’s total was $8.9M, and provide 3 actionable insights for the board.”

ChatGPT’s Output

Revenue Analysis — FY2025

Total Revenue: $10.7M YoY Growth: 20.2% ($10.7M vs $8.9M)

Quarterly Trend:

• Q1→Q2: -8.7% decline
• Q2→Q3: +33.3% growth
• Q3→Q4: +25.0% growth

3 Insights for the Board:

1. Strong H2 momentum — 76% of growth came in Q3-Q4. Investigate what drove the acceleration (new product? seasonal demand? sales hire?) and double down.

2. Q2 dip needs attention — The Q1→Q2 decline suggests a recurring weakness. Review if this is seasonal or structural, and pre-plan mitigation for next Q2.

3. 20% YoY growth is healthy but front-load next year — At current trajectory, Q4 dependency creates risk. Aim to distribute growth more evenly through pipeline diversification.

DeepSeek’s Output

Fiscal Year Revenue Analysis

Quarter	Revenue	QoQ Change	% of Annual
Q1	$2.3M	—	21.5%
Q2	$2.1M	-$0.2M (-8.7%)	19.6%
Q3	$2.8M	+$0.7M (+33.3%)	26.2%
Q4	$3.5M	+$0.7M (+25.0%)	32.7%
Total	$10.7M		100%

Year-over-Year Growth: ($10.7M - $8.9M) / $8.9M = 20.22%

Trend: J-curve recovery pattern. Q2 dip followed by accelerating growth through H2. Revenue concentration increasing toward Q4 (32.7% of annual).

Actionable Insights for the Board:

1. Diagnose the Q2 trough before it recurs. Two data points don’t confirm seasonality, but a -8.7% QoQ drop warrants root cause analysis. Action: compare Q2 pipeline data with Q1 to identify if this is a demand issue, sales cycle gap, or budget-cycle effect from customers.

2. Stress-test the Q4 dependency. 32.7% of revenue in one quarter is a concentration risk. If Q4 slips (macro downturn, deal delays), the annual target is at risk. Action: model a scenario where Q4 underperforms by 20% and build contingency pipeline now.

3. Set Q1 2026 targets aggressively. The exit rate ($3.5M/quarter = $14M annualized) significantly exceeds the $10.7M actual. Either the business is accelerating and next year’s target should be $13-14M, or Q4 was anomalous. Action: break down Q4 revenue by new vs. expansion vs. one-time to determine sustainable run rate.

Verdict: DeepSeek wins 🏆

ChatGPT gives a solid, clean analysis. DeepSeek gives a board-ready analysis — the table format, the explicit formula for YoY growth, the J-curve pattern identification, and crucially, each insight includes a specific action item (not just an observation). The Q4 stress-test recommendation and the annualized run-rate analysis show deeper business thinking.

Score: ChatGPT 7.5/10 | DeepSeek 9/10

Pricing Comparison

The cost difference is staggering. DeepSeek’s API is roughly 5-10x cheaper than OpenAI’s. For individual users, DeepSeek’s web interface is completely free with no usage caps on the reasoning model.

When to Choose ChatGPT

• You want a polished, all-in-one platform — plugins, DALL·E, voice, GPTs, the works
• Creative writing and marketing copy — ChatGPT’s prose is consistently more polished
• You need the ecosystem — Custom GPTs, API integrations, enterprise features
• Reliability matters — OpenAI has better uptime and global infrastructure
• You’re non-technical — ChatGPT’s UX is more approachable

When to Choose DeepSeek

• Coding is your primary use case — DeepSeek’s code quality and documentation is consistently superior
• You need strong reasoning — Math, logic, data analysis
• Budget matters — Free web access, dramatically cheaper API
• You’re a developer — The API pricing makes it viable for production applications
• You want open weights — DeepSeek models are open-source, ChatGPT is closed

When to Use Both

Honestly? The smartest move might be using both. ChatGPT for creative work, brainstorming, and general tasks. DeepSeek for coding, analysis, and anything where you need rigorous reasoning. The cost of DeepSeek’s API is so low that running both barely impacts your budget.

Final Score

Overall: DeepSeek edges ahead on raw capability in our tests, especially for technical tasks. ChatGPT wins on ecosystem, polish, and creative work. The real story is the price — DeepSeek delivers GPT-4-class performance at a fraction of the cost.

Our recommendation: If you’re technical and price-conscious, DeepSeek is remarkable value. If you want the smoothest experience with the broadest feature set, ChatGPT is still the gold standard. And if you can — use both.

Last updated: February 2026. We re-test monthly as models update.