AI语言模型的演变
已设定了新的标准，尤其是在编码和编程环境中。领导电荷为> deepSeek-v3，gpt-4o 和
结论>
>
模型体系结构和设计> deepSeek-v3
deepSeek -v3是具有6710亿参数的外源外源混合物（MOE）模型，每个令牌激活了370亿个参数。它利用了14.8万亿代币训练的最先进的负载平衡和多token预测方法。该模型在多个基准测试中实现顶级性能，维持培训效率，成本仅为278.8万h800 gpu小时。 DeepSeek-v3 deepseek-r1 lite中的推理能力，并提供了128K上下文窗口。此外，它可以处理多种输入类型，包括文本，结构化数据和复杂的多模式输入，使其用于多种用例。 也请阅读：使用DeepSeek-V3 构建AI应用程序 > gpt-4o绿色3.3 70B
METAllama3.3 70 B多语言大语言模型（LLM）是一种开源，预先培训的，指令调节的生成模型，具有700亿个参数。它旨在优化效率和可扩展性。它采用尖端技术来处理各种各样的任务，对超过15万亿代币进行了培训。 Llama 3.3 70B是一种使用优化的变压器体系结构的自动回归语言模型。该模型在几个基准上实现了出色的性能，并通过优化的资源分配保持培训成本最低。
llama 3.3 70b支持宽阔的上下文窗口，并包含了高级推理功能，以实现细微和精确的任务处理。它旨在处理基于文本的输入，但也可以处理结构化数据，在各种应用程序中提供灵活性。> DeepSeek-V3 vs GPT-4O vs Llama 3.3 70b：模型评估
1。模型概述
| Benchmark | Description | DeepSeek-V3 | GPT-4o | Llama 3.3 70B |
| MMLU | Massive Multitask Language Understanding- Test knowledge across 57 subjects including maths, history, law and more | 88.5% | 88.7% | 88.5% |
| MMLU-Pro | A more robust MMLU benchmark with more complex reasoning focused questions and reduced prompt sensitivity | 75.9% | 74.68% | 75.9% |
| MMMU | Massive Multitask Multimodal Understanding: Text understanding across text, audio,images and videos | Not available | 69.1% | Not available |
| HellaSwag | A challenging sentence completion benchmark | 88.9% | Not available | Not available |
| HumanEval | Evaluates code generation and problem solving capabilities | 82.6% | 90.2% | 88.4% |
| MATH | Tests Mathematical problem solving abilities across various difficulty levels | 61.6% | 75.9% | 77% |
| GPQA | Test PhD-level knowledge in physics, chemistry and biology that require domain expertise | 59.1% | 53.6% | 50.5% |
| IFEval | Test model’s ability to accurately follow explicit formatting instructions, generate appropriate outputs and maintain consistent instructions | 86.1% | Not available | 92.1% |
>您可以在此处找到其单独的基准测试的结果：
谈到定价，与DeepSeek-v3相比，GPT-4O的输入和输出令牌贵大约30倍。同时，与DeepSeek-V3有关输入和输出令牌的Llama 3.3 70B指令大约贵1.5倍。 在需要结构化任务完成的基准中，
DeepSeek-V3在基准中擅长，例如MMLU（大量的多任务语言理解）和HumaneVal（代码生成）。但是，它在数学（数学解决问题）等基准中面临挑战，在数学（数学解决问题）中，其表现不那么竞争。它也导致GPQA（广义段落问题回答），表现优于该域中的其他模型。> 在HumaneVal和MMLU中，
gpt-4O表现特别出色，在那里它以其在各种主题上产生高质量响应的能力而脱颖而出。尽管它在基于推理的任务中闪耀，但它在其他模型等专业领域（例如Math和GPQA）中挣扎。 Llama 3.3 70b在数学和IFEVAL等基准（评估后的指令）等基准中显示出其优势。它比其他模型更有效地展示了出色的问题解决能力，并更有效地遵循复杂的指示。它在HumaneVal和GPQA等其他基准测试中的性能不如DeepSeek-V3和GPT-4O。 > deepSeek-v3 vs gpt-4o vs llama 3.3 70b：编码功能
现在，我们将比较DeepSeek-V3，GPT-4O和Llama 3.3 70B的代码编写功能。为此，我们将在所有三种模型上给出相同的提示，并在以下指标上评估他们的回答：
可读性
q。写python代码以查找大量的阶乘。
GPT-4O响应
使用数组（手动处理乘法和携带操作）来计算大数的阶乘，这是一个python实现：
生成代码的输出
的响应骆驼3.3 70B
>
def multiply(arr, x):
"""
Multiplies an array representation of a number with an integer x.
: param arr: List of integers representing the digits of the number.
: param x: Integer to multiply with.
:return: Updated array with the result.
"""
carry = 0 # Initialize carry
for i in range(len(arr)):
prod = arr[i] * x + carry
arr[i] = prod % 10 # Store last digit of the product in the array
carry = prod // 10 # Update the carry
# Process carry, add digits to the array
while carry > 0:
arr.append(carry % 10)
carry //= 10
return arr
def factorial_large_number_array(n):
"""
Calculate the factorial of a large number using array representation.
: param n: Integer for which the factorial is to be computed.
:return: Factorial of n as a string.
"""
if n < 0:
return "Factorial is not defined for negative numbers."
if n == 0 or n == 1:
return "1"
arr = [1] # Initialize the array with the first factorial value (1)
for x in range(2, n + 1):
arr = multiply(arr, x)
# The result is stored in reverse order in the array
return ''.join(map(str, reversed(arr)))
# Input a large number
try:
number = int(input("Enter a large number to calculate its factorial: "))
result = factorial_large_number_array(number)
print(f"The factorial of {number} is:\n{result}")
except ValueError:
print("Invalid input! Please enter a valid integer.")def multiply(arr, x):
"""
Multiplies an array representation of a number with an integer x.
: param arr: List of integers representing the digits of the number.
: param x: Integer to multiply with.
:return: Updated array with the result.
"""
carry = 0 # Initialize carry
for i in range(len(arr)):
prod = arr[i] * x + carry
arr[i] = prod % 10 # Store last digit of the product in the array
carry = prod // 10 # Update the carry
# Process carry, add digits to the array
while carry > 0:
arr.append(carry % 10)
carry //= 10
return arr
def factorial_large_number_array(n):
"""
Calculate the factorial of a large number using array representation.
: param n: Integer for which the factorial is to be computed.
:return: Factorial of n as a string.
"""
if n < 0:
return "Factorial is not defined for negative numbers."
if n == 0 or n == 1:
return "1"
arr = [1] # Initialize the array with the first factorial value (1)
for x in range(2, n + 1):
arr = multiply(arr, x)
# The result is stored in reverse order in the array
return ''.join(map(str, reversed(arr)))
# Input a large number
try:
number = int(input("Enter a large number to calculate its factorial: "))
result = factorial_large_number_array(number)
print(f"The factorial of {number} is:\n{result}")
except ValueError:
print("Invalid input! Please enter a valid integer.") 
DeepSeek-V3
要使用Python中的数组计算大数量的阶乘，我们可以将结果存储在数组（或列表）中，以处理超过标准数据类型限制的非常大的数字。您可以实现以下方式：
生成代码的输出def multiply(x, res, res_size):
"""Multiplies x with the number represented by res[]"""
carry = 0
for i in range(res_size):
prod = res[i] * x + carry
res[i] = prod % 10
carry = prod // 10
while carry:
res.append(carry % 10)
carry = carry // 10
def factorial(n):
""" Calculates the factorial of a number n"""
res = [1]
res_size = 1
for i in range(2, n + 1):
multiply(i, res, res_size)
res_size = len(res)
return res
def print_factorial(n):
""" Prints the factorial of a number n"""
res = factorial(n)
print("Factorial of", n, "is:")
for digit in reversed(res):
print(digit, end="")
# Example usage:
n = 55
print_factorial(n)
比较结果
>建议：
用于实际用途的
>：
生成代码的输出
>这是一个python解决方案，该解决方案最多可以在删除一个字符后检查字符串是否可以是文安。 生成代码的输出
def factorial(n):
# Initialize the result as a list with 1
result = [1]
# Multiply the result list with numbers from 2 to n
for x in range(2, n + 1):
carry = 0
# Multiply each digit in the result list with x
for i in range(len(result)):
product = result[i] * x + carry
result[i] = product % 10 # Store the last digit
carry = product // 10 # Carry over the remaining digits
# Add the remaining carry to the result list
while carry > 0:
result.append(carry % 10)
carry = carry // 10
# Reverse the result list to get the correct order
result.reverse()
return result
# Input: Number to find factorial of
n = int(input("Enter a number: "))
# Compute factorial
factorial_result = factorial(n)
# Convert the list of digits to a string for display
factorial_str = ''.join(map(str, factorial_result))
# Output the result
print(f"Factorial of {n} is: {factorial_str}")> 生成代码的输出
def multiply(arr, x):
"""
Multiplies an array representation of a number with an integer x.
: param arr: List of integers representing the digits of the number.
: param x: Integer to multiply with.
:return: Updated array with the result.
"""
carry = 0 # Initialize carry
for i in range(len(arr)):
prod = arr[i] * x + carry
arr[i] = prod % 10 # Store last digit of the product in the array
carry = prod // 10 # Update the carry
# Process carry, add digits to the array
while carry > 0:
arr.append(carry % 10)
carry //= 10
return arr
def factorial_large_number_array(n):
"""
Calculate the factorial of a large number using array representation.
: param n: Integer for which the factorial is to be computed.
:return: Factorial of n as a string.
"""
if n < 0:
return "Factorial is not defined for negative numbers."
if n == 0 or n == 1:
return "1"
arr = [1] # Initialize the array with the first factorial value (1)
for x in range(2, n + 1):
arr = multiply(arr, x)
# The result is stored in reverse order in the array
return ''.join(map(str, reversed(arr)))
# Input a large number
try:
number = int(input("Enter a large number to calculate its factorial: "))
result = factorial_large_number_array(number)
print(f"The factorial of {number} is:\n{result}")
except ValueError:
print("Invalid input! Please enter a valid integer.")比较见解
GPT-4O的响应是最完整且有据可查的响应。它以清晰度处理核心功能，使未来的开发人员可以轻松修改或扩展代码。它的效率和清晰文档的结合使其非常适合生产环境。
用于实际用途的
>
：GPT-4O响应是最好的，因为其详尽的文档，清晰的结构和可读性。 出于教育目的，
>以上是DeepSeek-V3与GPT-4O vs Llama 3.3 70b：找到最佳的AI模型的详细内容。更多信息请关注PHP中文网其他相关文章！
