late , OpenAI released its new o1 model(also known as Strawberry ) , which focuses on reasoning and logic . In some field , such as math , skill , and put one over , it surmount the GPT-4o by a large margin . However , GPT-4o still has its advantage and strengths compared to the new o1 model . Both ChatGPT models requireChatGPT plus subscriptionto access . This clause place ChatGPT 4o and o1 models through different prompt across various problem to determine which example is better suited for specific labor . So have ’s set out .
Table of content
1. Coding
get ’s kick - start our tests with tantalise . As an model , I provided a Python script with several errors , inefficient methods for solving the chore , and issues keep it from generating an output . I gave this code to both the o1 Strawberry and GPT-4o using the next prompting .
The consequence were quite surprising . The code generated by GPT 4o could n’t produce an output , but it contend to fix 90 % of the errors . In contrast , the o1 model generated a dead working solvent . Additionally , the computer code from the o1 manikin was more concise , make use of leaning comprehensions and augmented grant .
Notably , it also automatically added a main occasion which the GPT 4o version did not . However , an interesting point is that while GPT 4o imported only the necessary constituent , the o1 example imported the entireheapqmodule . Although this approach is still efficient , it is less elegant .
o1 Model
This could be because we initially tested the AI model with a simple shopping cart program . To further evaluate their capabilities , we test them again with more complex code that includes multi - threading , machine learning , and complex information structures like graphs and trees . This code had even more errors and was extremely inefficient .
This is where the o1 model truly shined . While GPT 4o managed to fix around 40 - 50 % of the computer error , the o1 theoretical account again resolved all of them . to boot , GPT 4o did not better efficiency in any way ; the generate code still used inefficient threading technique , bank on a basic model likeMLPClassifierfor fraud detection , and did n’t tune any simple machine learning simulation . In contrast , the o1 model follow out all of these aspects absolutely .
We have some coolChatGPT tips for programmersthat will serve you get more out of AI command prompt .
GPT 4o Model
2. Generating Emails, Assignments, Articles, etc.
In the 2d examination phase , we focalize on generating various texts , grade from simple emails to 2,000 - word articles . In this causa , both models produced exchangeable outputs , have it difficult to rank one over the other . The reason is straightforward : the o1 role model excels at tasks that require high - tier logical thinking , whereas generating emails and assignments can be expeditiously wield by standard nomenclature models . For illustration , you could see the exam solution in the screenshot below .
While the production was similar , GPT 4o generate the text three time quicker than the o1 modelling . The o1 manakin may have conducted a chain of thought internally , spending more sentence on thinking and analyzing , but for tasks like generating text , GPT 4o is the honorable choice in full term of upper . Additionally , with only 30 message per week useable on the o1 framework , it is more practical to allow it for more modern undertaking rather than routine text genesis .
3. Generating Script, Social Media Posts and Ideas
While get plain e-mail and articles may not require heavy reasoning , one might get into that originative substance would benefit from it . However , that ’s not necessarily the case . For instance , when generate a random script or a societal culture medium post , the o1 manikin does not show any significant advantage , aside from being dim . However , if your requirements are precise and involve a protracted list of didactics , the o1 model performs marginally better .
For model , I provided a 2,000 - word article to both simulation and need them to make a Twitter yarn . I also ask it to follow the fictitious character limit , use Twitter unretentive form , and adopt a colloquial and friendly tone to generate more clicks on the link . There were several other venial instructions too .
As you’re able to see , the GPT 4o role model whole ignored the Twitter theatrical role limit point . I also define not to admit any hashtags , but the GPT 4o model did n’t surveil this educational activity either . Also , o1 translation added necessitate image tag to keep the audience engaging . While these might not seem like reasoning - related take , the o1 model takes time to carry a chemical chain of thought in the background , move over more weight to all your teaching in its response .
GPT 4o Model
When you brush up its chain of thoughts , you may see that it deliberate how to save in a way that could bring forth more clicks . So , even if you ’re generating text edition , but have a long lean of instructions that the GPT 4o translation is n’t fully follow , the o1 simulation can unquestionably come to the rescue .
4. Documents, PDFs, Images and Other Files
GPT 4o can key out objects and elements in images , sum documents and PDFs , and handle various types of file uploads with comfort . However , the o1 model presently lacks the capacity to upload file . As soon as you throw to the o1 model , the option to upload Indian file disappears . This limitation means that tasks involving visual credit or document analytic thinking ca n’t be performed directly with the o1 model . In this aspect , GPT 4o is the clear-cut winner .
5. Solve Math Problems
I tested both models with some basic math questions , and GPT 4o got a few of them wrong . GPT 4o seems to be more focused on regain data from its training data . Whenever I model a complicated question that was n’t immediately available on the internet , there was at least a 30 % chance ( limited sample size ) that it would make a mistake .
The o1 model also made an error in a graphical record - related interrogative . But overall , I asked both model around 12 math questions , and o1 ’s math - solving skills were telling — a significant upgrade over the 4o model . In amath Olympiad trial , the o1 role model score around 83 % , while the 4o fashion model only scored 13 % .
6. Complicated Finance Split
If the o1 model excels at math , it ’s potential to execute well in finance - related tasks too . To test this , I presented a scenario where my two friends and I were renting a new room and had drop money unevenly on various expense like onward motion payment , tear , brokerage firm fees , and other purchase .
I provided all the detail to both models and inquire them to count on how much each person would need to bear to ensure a honest split of all the money drop . In this berth , the manikin needed to understand both the mathematical calculations and the context to offer an accurate answer .
Both GPT 4o and o1 models supply the correct answer as the maths was childlike enough . Both the AI framework have the same level of understanding of the context and o1 model ’s reasoning is n’t at a huge reward here . However , we like the o1 poser ’s response as it explains the solution better with a table . But you’re able to easily get them in the 4o model with a prompt . So in this round , it ’s a tie .
o1 Model
OpenAI’s GPT 4o vs o1 Model
We liken both models across various exam , such as scheduling a timetable , creating a fiscal program for a business , and solving riddles . the o1 model excel , especially in task that required reasoning . However , for job that do not require much logical thinking — like generating text or researching entropy — both models render standardised final result , with the main difference of opinion being that o1 was much dim .
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GPT 4o Model
o1 Model
GPT 4o Model
o1 Model
