In the ever - develop gravid language model ( LLMs ) landscape , two front - runners endure out from the eternal sleep of the race : Anthropic’sClaude 3.5 Sonnetand OpenAI’sGPT-4o(the “ o ” stands for “ Omni ” ) . Both AIs boast telling capabilities , but which reign sovereign ? This guide dives deep into Claude 3.5 Sonnet and GPT-4o , analyse their forcefulness and weaknesses across various tasks .

We ’ll not only explore their comparative accuracy and reception speed , but also take out their pricing social system and service tier up . By the end of this article , you ’ll be arm with the knowledge to make an informed decision about which LLM in force suit your specific motive and budget .

Pricing and tiers

ChatGPT user can reach the GPT-4o exemplar for destitute , but the number of queries you ’re allow to inquire it within a three - 60 minutes straddle is special . Exceeding that limit will move you down to using GPT-3.5 , OpenAI ’s older , less able model , until the cooldown timer has readjust .

User ’s will need to compensate for the $ 20 - per - monthChatGPT Plus subscription , a $ 30 - per - month Teams subscription , or a grocery store rate Enterprise U-boat if they require to apply GPT-4o to their spirit ’ content . Paying for approach not only drastically increases the rate detonating gadget for using the AI model , but it also grants access code to additional feature article like Dall - Es paradigm generation .

Access to Claude is position up in much the same way . At the barren tier , users can converse with the chatbot on either the web or through the iOS app . They cal also upload images and documents to the Anthropic server and query the AI about their substance , as well as enjoy limited use of the new Claude 3.5 Sonnet framework .

Paying $ 20 a month for a Pro account gets you everything from the spare tier , as well as higher use limits , access to both Claude 3 Opus and Haiku , priority bandwidth and availability , and the ability to create labor with the AI center on a set of documents or files . Paying $ 30 a calendar month per somebody ( minimum 5 citizenry ) for a Teams write up nets you even high employment limits and the power to share chats between mate .

Advantages of using Claude

While Claude may not enjoy the same arcdegree of name recognition as GPT-4o , Anthropic ’s latest AI model oblige a routine of advantage over its contender — andnot just in performance benchmarks . For one , Claude sport a importantly larger context window than ChatGPT , ( 200,000 fiber versus 128,000 ) .

This mean it can remember and psychoanalyze a much larger chunk of previous conversation or text , leading to more nuanced and relevant answer , especially across longer interactions . Claude is also trained to prioritize accuracy over creativity in its response .

It may skin a routine in generating mirthful haiku , but Claude excels at summarizing enquiry papers and generating reports based on uploaded data . Claude also has a more recent knowledge shortcut date than GPT-4o : April 2024 versus October 2023 .

In addition , Anthropic trained its Claude models as constitutive AIs . This means that the system implements a circle of principles to make judgment about its generated outputs , effectively execute the model toward the kind of ethical behaviour dictated by the constitution . That include avoid toxic or discriminatory output signal , as well as avoiding describing or advocate for illegal or unethical activities .

Advantages of using GPT-4o

GPT-4o , on the other hand , offers drug user a wider potpourri of subprogram , a larger cognition base , and more flexible logarithm - in options than Claude .

Unlike Anthropic ’s offer , ChatGPT can generate images ( thoughthat feature article requires a subscription ) and get at the web to come up the most up - to - day of the month information available , not just the data it was trained on . And contrary to the sign - up physical process with Claude , GPT-4o exploiter wo n’t have to provide their speech sound number for verification , or really any credentials at all , in purchase order to originate using the manikin And , thanks to its large knowledge base , GPT-4o is more skillful at generating creative contentedness across images , text , and audio than Claude .

The ChatGPT website also tend to overload with user requests at a far lower rate than Claude ’s .

Speed and accuracy comparison

Both models are equal to of generating a wide variety of text production , from refining exploiter - write prose to make computer code and spreadsheet algorithms . When asked to render a “ patch of Python code that will measure the country of a circle and generate a graphical record of the function , ” both returned likewise structured snippets . GPT-4o produced the following :

importation numpy as npimport matplotlib.pyplot as plt

Function to count on the area of a circledef calculate_area(radius):return np.pi * radius * * 2

Generate a compass of radius valuesradii = np.linspace(0 , 10 , 100 ) # radii from 0 to 10 with 100 peak

Calculate the corresponding area valuesareas = calculate_area(radii )

Plotting the functionplt.figure(figsize=(10 , 6))plt.plot(radii , area , label=’Area = πr²’)plt.title(‘Area of a Circle as a Function of Radius’)plt.xlabel(‘Radius’)plt.ylabel(‘Area’)plt.legend()plt.grid(True)plt.show ( )

Meanwhile , Claude ’s response was a bit more fleshed out :

import mathimport numpy as npimport matplotlib.pyplot as plt

def circle_area(radius):return math.pi * radius**2

Generate radii from 0 to 10 with 100 pointsradii = np.linspace(0 , 10 , 100 )

Calculate areas for each radiusareas = [ circle_area(r ) for roentgen in r ]

Create the plotplt.figure(figsize=(10 , 6))plt.plot(radii , area , ‘ b- ‘ , linewidth=2)plt.title(‘Area of a Circle as a Function of Radius’)plt.xlabel(‘Radius’)plt.ylabel(‘Area’)plt.grid(True )

tote up some interesting pointsplt.plot(0 , circle_area(0 ) , ‘ ro ’ , label=’r = 0′)plt.plot(1 , circle_area(1 ) , ‘ ro ’ , label=’r = 1′)plt.plot(5 , circle_area(5 ) , ‘ ro ’ , label=’r = 5′)plt.plot(10 , circle_area(10 ) , ‘ ro ’ , label=’r = 10′ )

plt.legend()plt.show ( )

In terms of image identification , the two models are close matched . When asked to distinguish the various aspects of the conform to range of a function , both GPT-4o and Claude 3.5 were easily able to find and describe each of the 21 items present .

GPT-4o has the vantage of delivering information about late events , yield its ability to look the web for news show that ’s more late than its training information . Asking Claude “ what happen in Luxembourg yesterday ” only gets you a note reading : “ I apologize , but I do n’t have access to real - time news show or information about specific upshot that pass yesterday in Luxembourg . My knowledge crosscut is in April 2024 , and I do n’t have info about events after that escort . ” The AI does declare oneself a number of helpful good word as to where you might be able-bodied to find the information you request .

I did find comparing the two scheme to be a bit of a challenge , mostly because I was only able to make a half - twelve requests of Claude before the system locked me out for three hours . Even on the free tier of ChatGPT , I wo n’t be frozen out of the organization wholly — I ’ll just have to converse with a slightly inferior mannequin for a while .

Here’s how Claude 3.5 Sonnet and GPT-4o stack up in a direct comparison

Pricing and tiers

Advantages of using Claude

Advantages of using GPT-4o

Speed and accuracy comparison

Function to count on the area of a circledef calculate_area(radius):return np.pi * radius * * 2

Generate a compass of radius valuesradii = np.linspace(0 , 10 , 100 ) # radii from 0 to 10 with 100 peak

Calculate the corresponding area valuesareas = calculate_area(radii )

Plotting the functionplt.figure(figsize=(10 , 6))plt.plot(radii , area , label=’Area = πr²’)plt.title(‘Area of a Circle as a Function of Radius’)plt.xlabel(‘Radius’)plt.ylabel(‘Area’)plt.legend()plt.grid(True)plt.show ( )

Generate radii from 0 to 10 with 100 pointsradii = np.linspace(0 , 10 , 100 )

Calculate areas for each radiusareas = [ circle_area(r ) for roentgen in r ]

Create the plotplt.figure(figsize=(10 , 6))plt.plot(radii , area , ‘ b- ‘ , linewidth=2)plt.title(‘Area of a Circle as a Function of Radius’)plt.xlabel(‘Radius’)plt.ylabel(‘Area’)plt.grid(True )

tote up some interesting pointsplt.plot(0 , circle_area(0 ) , ‘ ro ’ , label=’r = 0′)plt.plot(1 , circle_area(1 ) , ‘ ro ’ , label=’r = 1′)plt.plot(5 , circle_area(5 ) , ‘ ro ’ , label=’r = 5′)plt.plot(10 , circle_area(10 ) , ‘ ro ’ , label=’r = 10′ )

Which is better?

Pricing and tiers#

Advantages of using Claude#

Advantages of using GPT-4o#

Speed and accuracy comparison#

Function to count on the area of a circledef calculate_area(radius):return np.pi * radius * * 2#

Generate a compass of radius valuesradii = np.linspace(0 , 10 , 100 ) # radii from 0 to 10 with 100 peak#

Calculate the corresponding area valuesareas = calculate_area(radii )#

Plotting the functionplt.figure(figsize=(10 , 6))plt.plot(radii , area , label=’Area = πr²’)plt.title(‘Area of a Circle as a Function of Radius’)plt.xlabel(‘Radius’)plt.ylabel(‘Area’)plt.legend()plt.grid(True)plt.show ( )#

Generate radii from 0 to 10 with 100 pointsradii = np.linspace(0 , 10 , 100 )#

Calculate areas for each radiusareas = [ circle_area(r ) for roentgen in r ]#

Create the plotplt.figure(figsize=(10 , 6))plt.plot(radii , area , ‘ b- ‘ , linewidth=2)plt.title(‘Area of a Circle as a Function of Radius’)plt.xlabel(‘Radius’)plt.ylabel(‘Area’)plt.grid(True )#

tote up some interesting pointsplt.plot(0 , circle_area(0 ) , ‘ ro ’ , label=’r = 0′)plt.plot(1 , circle_area(1 ) , ‘ ro ’ , label=’r = 1′)plt.plot(5 , circle_area(5 ) , ‘ ro ’ , label=’r = 5′)plt.plot(10 , circle_area(10 ) , ‘ ro ’ , label=’r = 10′ )#

Which is better?#

Pricing and tiers

Advantages of using Claude

Advantages of using GPT-4o

Speed and accuracy comparison

Function to count on the area of a circledef calculate_area(radius):return np.pi * radius * * 2

Generate a compass of radius valuesradii = np.linspace(0 , 10 , 100 ) # radii from 0 to 10 with 100 peak

Calculate the corresponding area valuesareas = calculate_area(radii )

Plotting the functionplt.figure(figsize=(10 , 6))plt.plot(radii , area , label=’Area = πr²’)plt.title(‘Area of a Circle as a Function of Radius’)plt.xlabel(‘Radius’)plt.ylabel(‘Area’)plt.legend()plt.grid(True)plt.show ( )

Generate radii from 0 to 10 with 100 pointsradii = np.linspace(0 , 10 , 100 )

Calculate areas for each radiusareas = [ circle_area(r ) for roentgen in r ]

Create the plotplt.figure(figsize=(10 , 6))plt.plot(radii , area , ‘ b- ‘ , linewidth=2)plt.title(‘Area of a Circle as a Function of Radius’)plt.xlabel(‘Radius’)plt.ylabel(‘Area’)plt.grid(True )

tote up some interesting pointsplt.plot(0 , circle_area(0 ) , ‘ ro ’ , label=’r = 0′)plt.plot(1 , circle_area(1 ) , ‘ ro ’ , label=’r = 1′)plt.plot(5 , circle_area(5 ) , ‘ ro ’ , label=’r = 5′)plt.plot(10 , circle_area(10 ) , ‘ ro ’ , label=’r = 10′ )

Which is better?