{"id":5111,"date":"2024-05-24T07:43:43","date_gmt":"2024-05-24T07:43:43","guid":{"rendered":"https:\/\/favtutor.com\/articles\/?p=5111"},"modified":"2024-05-24T08:36:33","modified_gmt":"2024-05-24T08:36:33","slug":"gpt-4o-vs-llama-3","status":"publish","type":"post","link":"https:\/\/favtutor.com\/articles\/gpt-4o-vs-llama-3\/","title":{"rendered":"We Found The Winner between GPT-4o vs Llama 3 After Testing"},"content":{"rendered":"\n

OpenAI’s recently released GPT-4o model<\/a> is creating a buzz across the internet. Various breathtaking use cases of GPT-4o have been shared, building excitement among users. People are saying it is already the best in the league, but is it really?<\/p>\n\n\n\n

Let\u2019s test the GPT-4o model versus Meta\u2019s latest and most powerful model, Llama 3, across a diverse array of tasks including doing maths and coding. Here are the results of these head-to-head tests:<\/p>\n\n\n\n

1) GPT-4o vs Llama 3: Apple Test<\/strong><\/h3>\n\n\n\n

In the Apple test, an LLM is asked to generate 10 sentences that end with the word \u2018apple.\u2019 LLMs often struggle with this task and cannot achieve 100% accuracy. We performed the Apple Test on Llama 3 and GPT-4o.<\/p>\n\n\n\n

Prompt:<\/strong> Give me 10 sentences that end with the word \u2018apple\u2019.<\/p>\n\n\n\n

GPT-4o:<\/strong><\/p>\n\n\n

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\"\"<\/figure>\n<\/div>\n\n\n

Llama 3:<\/strong><\/p>\n\n\n

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\"\"<\/figure>\n<\/div>\n\n\n

Llama 3 achieved 100% accuracy by generating 10 sentences that end with Apple. However, GPT-4o could generate only 8 such sentences thus achieving an accuracy of 80%. So, for the Apple Test, Llama 3 convincingly beats GPT-4o.<\/p>\n\n\n\n

2) Code Explanation<\/strong><\/h3>\n\n\n\n

Prompt:<\/strong> Explain simply what this function does:<\/p>\n\n\n\n

def func(lst):\nif len(lst) == 0:\nreturn []\nif len(lst) == 1:\nreturn [lst]\nl = []\nfor i in range(len(lst)):\nx = lst[i]\nremLst = lst[:i] + lst[i+1:]\nfor p in func(remLst):\nl.append([x] + p)\nreturn l<\/pre><\/div>\n\n\n\n
GPT-4o:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
Llama 3:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
GPT-4o analyzes the code and explains each line in-depth so that a user can understand it better. On the other hand, Llama 3 only provides the overview of the function in a single line.<\/p>\n\n\n\n
For code explanation tasks, GPT-4o beats Llama 3.<\/p>\n\n\n\n
3) Haiku Test<\/strong><\/h3>\n\n\n\n
Prompt:<\/strong> Argue for and against the use of Kubernetes in the style of a haiku.<\/p>\n\n\n\n
GPT-4o:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
Llama 3:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
Here, we asked the models to generate a haiku about the advantages and disadvantages of Kubernetes. As we can see, GPT-4o included all the important terms related to the concept of Kubernetes. <\/p>\n\n\n\n
It also spoke about the steep learning curve and the complexity of the disadvantages. On the other hand, Llama\u2019s haiku was not as appealing as GPT\u2019s at first sight. For haiku generation, GPT-4o beats Llama 3.<\/p>\n\n\n\n
4) Product Description<\/strong><\/h3>\n\n\n\n
Prompt:<\/strong> Create a 200-word product description for a high-tech smartwatch that tracks your fitness goals and receives notifications from your phone.<\/p>\n\n\n\n
GPT-4o:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
Llama 3:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
Here, GPT-4o\u2019s product description is better than Llama\u2019s because it discusses the required features in a detailed manner. Llama\u2019s description only talks about the important features in 1-2 lines. This makes GPT-4o\u2019s description more appealing and attractive to the user and it give the user a sense of confidence in the product.<\/p>\n\n\n\n
For product description tasks, GPT-4o outperforms Llama 3.<\/p>\n\n\n\n
5) Mathematical Operations<\/strong><\/h3>\n\n\n\n
Prompt:<\/strong> Subtract 3(x^2+1)^2 from 6x^3\u22129x^2\u221213x\u22124<\/p>\n\n\n\n
GPT-4o:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
Llama 3:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
As can see in the example above, GPT-4o gives the correct answer unlike Llama 3 which makes a mistake in the final calculation process.<\/p>\n\n\n\n
For mathematical operations, GPT-4o outperforms Llama 3.<\/p>\n\n\n\n
6) Logical Riddles<\/strong><\/h3>\n\n\n\n
Riddle 1:<\/strong> Old Granny Adams left half her money to her granddaughter and half that amount to her grandson. She left a sixth to her brother, and the remainder, $1,000, to the dogs\u2019 home. How much did she leave altogether?<\/p>\n\n\n\n
GPT-4o:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
Llama 3:<\/strong><\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
PT-4o is correct while Llama 3 is wrong. This shows GPT\u2019s logical powers as it is able to accurately understand the riddles and provide correct answers. For logical riddles, GPT-4o outperforms Llama 3.<\/p>\n\n\n\n
For code generation tasks, GPT-4o beats Llama 3.<\/p>\n\n\n\n
Conclusion<\/strong><\/h2>\n\n\n\n
Based on the comprehensive testing and evaluation presented, it is evident that GPT-4o outshines Llama 3 in numerous tasks, including code explanation, product descriptions, and mathematical operations. While both models showcase impressive capabilities, GPT-4o emerges as the superior choice for a wide range of applications.<\/p>\n","protected":false},"excerpt":{"rendered":"
Here is the detailed comparison of GPT-4o vs Llama 3 for code explanation, product descriptions, and mathematical operations.<\/p>\n","protected":false},"author":18,"featured_media":5125,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":null,"jnews_primary_category":{"id":"","hide":""},"footnotes":""},"categories":[57],"tags":[61,258,171,172,81,60],"class_list":["post-5111","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","tag-chatgpt","tag-gpt-4o","tag-llama","tag-llama-3","tag-meta","tag-openai"],"_links":{"self":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/5111","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/users\/18"}],"replies":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/comments?post=5111"}],"version-history":[{"count":2,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/5111\/revisions"}],"predecessor-version":[{"id":5126,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/5111\/revisions\/5126"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/media\/5125"}],"wp:attachment":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/media?parent=5111"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/categories?post=5111"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/tags?post=5111"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}