The battle between two AI giants, developed by OpenAI and Deepseek.
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Deepseek R1 marks a bold advancement in AI technology, delivering innovative features and a focus on precision. Meanwhile, GPT o1-preview represents OpenAI’s next leap forward, showcasing significant improvements in its versatility and reliability.
Explore our detailed comparison of GPT o1-preview with other leading AI models to gain deeper insights into its strengths and how it stacks up against the competition:
In this evolving AI landscape, Deepseek is positioning itself as a strong contender, challenging OpenAI's established presence by refining niche functionalities and emphasizing user adaptability. To help you decide which AI best meets your requirements, let’s delve into the strengths and weaknesses of Deepseek R1 and GPT o1-preview.
The key differences between GPT o1-preview and Deepseek R1 lie in output capacity, speed, and release date. Both models support a 128K input context window, but GPT o1-preview can generate up to 65K output tokens at a speed of 144 tokens/sec, while Deepseek R1 has a maximum output token limit of 8K and processes at 37.2 tokens/sec. Additionally, GPT o1-preview has a knowledge cutoff of October 2023 and was released on September 12, 2024, whereas Deepseek R1 has no specified knowledge cutoff and launched more recently on January 21, 2025.
The choice between these models depends on specific needs, such as token output capacity, processing speed, and recency of release for cutting-edge applications.
This benchmark is the combination of official release notes for both models, as well as multiple open benchmarks.
Deepseek R1 outperforms GPT o1-preview in math (MATH-500: 97.3 vs. 92) and graduate reasoning (GPQA: 71.5 vs. 67), while both excel equally in undergraduate knowledge (MMLU: 90.8).
In coding, Deepseek R1 slightly leads (Human Eval: 96.3 vs. 96). However, GPT o1-preview stands out in cybersecurity (CTFs: 43.0, no data for Deepseek R1).
Each model's strengths suit specific tasks, such as math for Deepseek R1 and cybersecurity for GPT o1-preview.
We are rigorously testing AI models in challenging, real-world scenarios. Our evaluation focuses on key areas such as conversational ability, logical reasoning, mathematical problem-solving, and coding performance to assess the strengths and weaknesses of each model.
Some of the outputs will be edited down for shortness or clarity.
Let’s begin with a familiar challenge: many are aware that AI models often struggle with tasks like counting specific letters in a word. Let’s see how GPT o1-preview and Deepseek R1 handle this task. (I intentionally misspelled the word)
How many letters 'r' are in the word strawbery?
There are **2** instances of the letter 'r' in "strawbery."
The word "strawbery" contains the letter 'r' **2 times**.
The model will be presented with a statement and several possible answers. Its task is to select the correct answer.
The medieval Arabs had many manuscripts of the ancient Greek.
When needed, they translate them into Arabic.
Medieval Arab philosophers were very interested in Aristotle's Theory of Poetry,
which was obviously not shared by Arab poets,
because a poet interested in it must want to read Homer's poems.
Aristotle himself often quotes Homer's poems.
However, Homer's poems were not translated into Arabic until modern times.
Which of the following options, if true, strongly supports the above argument?
Please only return the letter in front of your predict correct option, A, B, C or D.
A. Some medieval Arab translators have manuscripts of Homer poems in ancient Greek.
B. Aristotle's Theory of Poetry is often quoted and commented by modern Arab poets.
C. In Aristotle's Theory of Poetry, most of the content is related to drama,
and medieval Arabs also wrote plays and performed them.
D. A series of medieval Arab stories, such as Arab Night,
are very similar to some parts of Homer's epic.
B
A
Write a short poem about friendship
Life is a journey, a winding road,
A dance of moments, stories untold.
Echoes of laughter, shadows of tears,
Weaving together the days and the years.
Sunrise to sunset, paths intertwine,
Hearts find direction, souls seek a sign.
Through trials and triumphs, joy and strife,
We wander, we wonder—this mosaic is life.
A thread of dawn, a twilight sigh,
The breath between a blink and sky—
A dance of shadows, fleeting, warm,
A tempest woven into form.
We trace our maps in rivers’ veins,
Through valleys carved by joy and pains,
Each scar a star, each loss a tune,
In chaos, life begins to bloom.
The seasons turn, yet roots dig deep,
Where storms and quiet cradle sleep.
A firefly’s flicker, brief, unplanned—
The universe cupped in a hand.
So tread the edge where mysteries pour,
For grace exists in what’s unsure.
A heartbeat’s hum, the dark, the dawn—
This borrowed light goes on… goes on.
I tested these models with several different tasks, and they successfully completed them all. However, GPT encountered difficulties with this particular one.
The expression 2⋅3⋅4⋅5+1=121, as multiplication is performed before addition.
However, other values can be obtained by inserting parentheses into the expression.
For example:
(2⋅(3⋅4))⋅(5+1)=144
How many distinct values can be obtained by inserting parentheses into
2⋅3⋅4⋅5+1, without rearranging the terms?
Here are the possible valid ways to parenthesize
the expression and their corresponding values:
1. **Original expression**:
\((2 \cdot 3 \cdot 4 \cdot 5) + 1 = 121\)
...
5. **Grouping \(2 + 3\) first**:
\(((2 + 3) \cdot 4 \cdot 5) + 1 = 101\)
...
So, there are **7 different values** obtainable.
\(\boxed{7}\)
Deepseek R1 output
...
3. **Adding 1 to the product of the last two terms (4 and 5)**:
\[
(2 \cdot 3) \cdot (4 \cdot 5 + 1) = 6 \cdot 21 = 126
\]
This can also be grouped in different ways, but the result remains 126.
4. **Adding 1 to the product of the last three terms (3, 4, and 5)**:
\[
2 \cdot (3 \cdot 4 \cdot 5 + 1) = 2 \cdot 61 = 122
\]
By systematically analyzing all possible ways to insert parentheses,
we find that the different values obtained are 121, 144, 126, and 122.
Each of these values corresponds to adding 1 at different stages
of the multiplication process.
Thus, the total number of distinct values that can be obtained is \(\boxed{4}\).
Given two sorted arrays nums1 and nums2 of size m and n respectively,
return the median of the two sorted arrays.
The overall run time complexity should be O(log (m+n))
Example:
Input: nums1 = [1,3], nums2 = [2]
Output: 2.00000
Explanation: merged array = [1,2,3] and median is 2.
def findMedianSortedArrays(nums1, nums2):
m, n = len(nums1), len(nums2)
# Ensure nums1 is the smaller array to minimize the binary search range
if m > n:
nums1, nums2, m, n = nums2, nums1, n, m
# Handle the case where both arrays are empty
if n == 0:
raise ValueError("Both arrays are empty.")
imin, imax = 0, m
half_len = (m + n + 1) // 2
while imin <= imax:
i = (imin + imax) // 2 # Partition index for nums1
j = half_len - i # Partition index for nums2
# Edge cases where partition is at the extreme ends
maxLeftX = nums1[i - 1] if i > 0 else float('-inf')
minRightX = nums1[i] if i < m else float('inf')
maxLeftY = nums2[j - 1] if j > 0 else float('-inf')
minRightY = nums2[j] if j < n else float('inf')
# Check if we have found the correct partition
if maxLeftX <= minRightY and maxLeftY <= minRightX:
# Compute the median based on the total length parity
if (m + n) % 2 == 0:
return (max(maxLeftX, maxLeftY) + min(minRightX, minRightY)) / 2.0
else:
return max(maxLeftX, maxLeftY)
elif maxLeftX > minRightY:
# Too far on the right side for partitionX, move left
imax = i - 1
else:
# Too far on the left side for partitionX, move right
imin = i + 1
# If we reach here, the input arrays were not sorted as expected
raise ValueError("Input arrays are not sorted or invalid.")
def findMedianSortedArrays(nums1, nums2):
# Ensure nums1 is the smaller array
if len(nums1) > len(nums2):
nums1, nums2 = nums2, nums1
m, n = len(nums1), len(nums2)
low, high = 0, m
total = m + n
while low <= high:
i = (low + high) // 2
j = (total + 1) // 2 - i
max_left_A = nums1[i-1] if i > 0 else float('-inf')
min_right_A = nums1[i] if i < m else float('inf')
max_left_B = nums2[j-1] if j > 0 else float('-inf')
min_right_B = nums2[j] if j < n else float('inf')
if max_left_A <= min_right_B and max_left_B <= min_right_A:
if total % 2 == 1:
return max(max_left_A, max_left_B)
else:
return (max(max_left_A, max_left_B) + min(min_right_A, min_right_B)) / 2.0
elif max_left_A > min_right_B:
high = i - 1
else:
low = i + 1
return 0.0 # This return is a fallback and should theoretically never be reached
This function, form_valid, processes a valid form
submission for a purchase in a Django view. Optimize and refactor it.
def form_valid(self, form):
email = form.cleaned_data['email']
tel = form.cleaned_data['tel']
country = form.cleaned_data['country']
city = form.cleaned_data['city']
street = form.cleaned_data['street']
user = self.request.user
products = Basket.objects.values_list(
'product',
flat=True
).filter(user=self.request.user)
total_price = Basket.objects.filter(
user=self.request.user
).aggregate(
total_price=Sum(F('product__price') * F('quantity'))
)
purchase = Purchase.objects.create(
user=user,
total_price=total_price.get('total_price'),
email=email,
tel=tel,
country=country,
city=city,
street=street
)
purchase.products.add(*products)
Basket.objects.filter(user=self.request.user).delete()
return redirect('history_purchases')
from django.db import transaction
from django.db.models import F, Sum
def form_valid(self, form):
user = self.request.user
# Extract form data
data = {field: form.cleaned_data[field] for field in ('email', 'tel', 'country', 'city', 'street')}
# Fetch basket items with related products in a single query
basket_items = Basket.objects.filter(user=user).select_related('product')
# Calculate total price in the database
total_price = basket_items.aggregate(
total_price=Sum(F('product__price') * F('quantity'))
)['total_price'] or 0 # Handle None total_price
with transaction.atomic():
# Create the purchase record
purchase = Purchase.objects.create(
user=user,
total_price=total_price,
**data
)
# Add products to the purchase
purchase.products.add(*[item.product for item in basket_items])
# Clear the user's basket
basket_items.delete()
return redirect('history_purchases')
from django.db import transaction
from django.db.models import Sum, F
from django.shortcuts import redirect
def form_valid(self, form):
user = self.request.user
with transaction.atomic():
# Get the user's basket items in a single queryset
user_baskets = Basket.objects.filter(user=user)
# Extract product IDs and calculate total price using the same queryset
products = user_baskets.values_list('product', flat=True)
total_price = user_baskets.aggregate(
total_price=Sum(F('product__price') * F('quantity'))
)['total_price'] or 0 # Default to 0 if no items to avoid None
# Create the Purchase instance with form data
purchase = Purchase.objects.create(
user=user,
total_price=total_price,
**{field: form.cleaned_data[field] for field in [
'email', 'tel', 'country', 'city', 'street'
]}
)
# Add products and clear the basket
purchase.products.add(*products)
user_baskets.delete()
return redirect('history_purchases')
Create an adaptive image slider using HTML and CSS.
Ensure that the slider is fully responsive,
adjusts to different screen sizes, and includes basic
navigation controls (such as arrows or dots).
Avoid using JavaScript for this task.
While we've highlighted some strengths, the best model for your specific task depends on your needs. The snippet below provides a starting point to test and compare two language models, GPT o1-preview and Deepseek R1. Play around with different prompts and see which model delivers the most relevant and impressive results for you!
import openai
def main():
client = OpenAI(
api_key='<YOUR_API_KEY>',
base_url="https://api.aimlapi.com",
)
# Specify the two models you want to compare
model1 = 'o1-preview'
model2 = 'deepseek/deepseek-r1'
selected_models = [model1, model2]
system_prompt = 'You are an AI assistant that only responds with jokes.'
user_prompt = 'Why is the sky blue?'
results = {}
for model in selected_models:
try:
response = client.chat.completions.create(
model=model,
messages=[
{'role': 'system', 'content': system_prompt},
{'role': 'user', 'content': user_prompt}
],
)
message = response.choices[0].message.content
results[model] = message
except Exception as error:
print(f"Error with model {model}:", error)
# Compare the results
print('Comparison of models:')
print(f"{model1}: {results.get(model1, 'No response')}")
print(f"{model2}: {results.get(model2, 'No response')}")
if __name__ == "__main__":
main()
GPT o1-preview shines in logical reasoning, algorithmic tasks, and creative writing, making it the go-to choice for complex, performance-critical projects. Deepseek R1, on the other hand, leads in advanced math, graduate-level logic, and memory-efficient coding, though it occasionally falls short in web development polish and.
Choosing the right model ultimately depends on your project’s priorities, whether it’s creative output, logical challenges, or coding efficiency.