Is Llama 3 Better Than GPT-4? Llama 3 vs. GPT-4 vs. GPT-4o Comparison

Nearly 10 months after Llama 2 and 14 months after GPT-4 launch, the world of generative AI and large language models has buzzed with excitement again, as we finally welcomed their successors: Llama 3 and GPT-4o. Promising enhanced accuracy, widened capabilities (especially in the case of GPT-4o), and improved performance across various tasks, Meta AI and OpenAI once more leave us wondering: how do Llama 3, GPT-4, and GPT-4o compare? 

And what about GPT-4? Until recently, it was the biggest and the most capable large language model out there. Now, alongside the launch of the new “4o”, OpenAI released its updated version: GPT-4 Turbo. So there are even more questions arising: How do these two differ? And is the new Llama 3 better than the GPT-4 Turbo?

Let’s take a closer look at all three and find the answers to those questions! 

P.S. Reading “GPT-4”, keep in mind that I refer to the Turbo variant — the latest update in that family of models.

What are GPT-4 and GPT-4o?

GPT-4 Turbo overview 

Released in March 2023, it’s a generative AI model capable of processing both text and image inputs, offering high accuracy, advanced creative writing, and high-level reasoning capabilities. Available through the OpenAI API, GPT-4 is optimized for chat but excels in various tasks. The latest update, GPT-4-turbo, enhances its efficiency and cost-effectiveness, supporting a context window of 128k tokens, which makes it highly suitable for extensive text processing tasks. The new Turbo variant is 3x cheaper for input tokens and 2x cheaper for output tokens compared to the original GPT-4, and introduces new features like JSON mode, reproducible outputs, and parallel function calling.

GPT-4o overview

Launched in May 2024, GPT-4o is OpenAI’s newest flagship large multimodal model, designed to take the title of the fastest and most affordable high-intelligence model on the market. As promised by its creators, GPT-4o brings enhanced efficiency and speed, making it ideal for advanced applications while keeping costs low. Curious about the cost part? As we can read at Gradient Flow, GPT-4o is 50% cheaper and twice as fast as GPT-4-turbo, for input and output tokens, making it more economical for large-scale use.

GPT-4o distinguishes itself from GPT-4 with several key advancements:

• Multimodal capabilities: GPT-4o can process and generate information across multiple modalities, including text, audio, images, and video. This allows it to understand and respond to both verbal and nonverbal elements, making interactions more natural and intuitive. For example, it can translate a menu from an image, discuss the food’s history, and provide recommendations.
• Improved efficiency and speed: GPT-4o is significantly more efficient, delivering faster response times and operating at a lower computational cost. It’s 2x fast and half the price of GPT-4-turbo within the API, making it more suitable for large-scale deployments.
• Enhanced contextual understanding: the model boasts an enhanced neural architecture, which enables it to handle more complex instructions and maintain context over longer conversations more effectively, minimizing misunderstandings and irrelevant responses.
• Voice and real-time interaction: GPT-4o can engage in real-time voice conversations and is set to include capabilities for real-time video interactions. This makes it possible to have more dynamic and interactive dialogues, like discussing a live sports game and explaining the rules in real-time​.

What is Llama 3?

Llama 3 is the latest flagship model from Meta AI, which debuted in April 2024 and is available in two sizes — 8 billion and 70 billion parameters. It excels in understanding language nuances, tackling complex tasks like translation (it supports over 30 different languages), and generating natural dialogue. Thanks to its optimized transformer architecture and Grouped-Query Attention (GQA), Llama 3 is more scalable and, according to its creators, performs better than ever. 

Llama 3 is said to introduce significant improvements in performance and widened capabilities compared to its predecessor, Llama 2 (for example, enhanced reasoning and coding abilities). However, even with these advancements, both models are still open-source and freely available for research and commercial use.

Which generative AI model is better: Llama 3 vs. GPT-4 vs. GPT-4o

Just like in our previous comparison of Llama 2 and GPT models, also here the most accurate answer is: it depends. 

When it comes to the number of parameters, Llama 3 is smaller than GPT-4 and most likely smaller than GPT-4o as well (although it’s not certain as OpenAI still hasn’t opened up about it). However, while the number of parameters can significantly affect a model’s performance, it’s not the only factor to consider here.

Llama 3, for instance, is designed with efficiency and specific purposes in mind, excelling in areas like translation and dialogue generation. GPT-4 and GPT-4o, on the other hand, boast advanced reasoning and multimodal capabilities, handling text, images, audio, and even video. These models are optimized for a wide range of applications, from intricate problem-solving to real-time interactions, making them highly versatile.

Therefore, deciding which model — Llama 3, GPT-4 or GPT-4o — is the best fit for your needs largely depends on the requirements of your specific use case.

Before we dive deeper, take a look at this table presenting several main differences between Llama 3, GPT-4-turbo, and GPT-4o:

Llama 3 vs. GPT-4 vs. GPT-4o — model size

Let’s start with what we know: Llama 3 is available in two versions, featuring 8 billion and 70 billion parameters. This makes it significantly smaller than the GPT models, but the design philosophy behind Llama 3 emphasizes efficiency and task-specific performance rather than sheer size, so it all makes sense.

Sadly, the matter of parameter count is still not so clear in the case of OpenAI, as the company continues to be silent about their models’ size. According to several reliable sources (e.g, Semafor and George Hotz.), GPT-4 is estimated to have around 1.76 trillion parameters.

As for GPT-4o, for now we don’t even have any reliable guesses. No reliable sources discuss its size, but considering its enhanced capabilities, it’s reasonable to speculate that it could have more parameters than GPT-4. 

It’s clear, however, that the size difference between Llama 3, GPT-4, and GPT-4o is quite significant, even more so than the gap between Llama 2 and GPT-3.5 and 4. 

Llama 3 vs. GPT-4 vs. GPT-4o — accuracy & complex tasks

When it comes to accuracy and task complexity, comparing Llama 3, GPT-4, and GPT-4o reveals their distinct strengths and capabilities.

Llama 3 is designed to be efficient and excel in specific tasks — Meta AI’s evaluations have highlighted its strengths in areas like translation and dialogue generation. Techniques such as Grouped-Query Attention (GQA) enhance the model’s ability to focus on relevant parts of the input, generating accurate responses over multiple conversation turns. 

GPT-4, with its estimated 1.76 trillion parameters, is meant to be a powerhouse for advanced reasoning and complex problem-solving. The 5-shot MMLU benchmark demonstrates GPT-4’s superiority, showing significant performance improvements over other models. Its advanced architecture allows it to handle intricate and mission-critical tasks, requiring a high degree of creativity and nuanced understanding.

GPT-4o takes these capabilities even further. With an improved neural architecture, GPT-4o enhances its ability to understand and generate nuanced text. This makes it adept at handling complex instructions and maintaining context over extended interactions. According to GPT-4o’s creators, these improvements are meant to ensure more accurate and relevant outputs, particularly in scenarios demanding detailed and precise communication.

Considering all the above plus the difference in sizes between Llama and GPTs, we could probably assume that Llama 3 is much “weaker” than OpenAI models. But in reality, in many areas, it rates very closely to them. According to prescouter.com, “on more complex tasks requiring advanced reasoning, Llama 3 surprisingly edges out with a 35.7% score in graduate-level benchmarks, against GPT 4’s 39.5%.“ It also scored 82% on the MMLU 5-shot test, while GPT-4-turbo did only slightly further — achieving 86.4%. This clearly shows that despite being way smaller, Llama 3 is not distinct from the GPTs.

Llama 3 vs. GPT-4 vs. GPT-4o — creativity

What about the creativity of today’s competitors? 

Llama 3 shows solid performance in translation and dialogue generation, thanks to Meta AI’s design for efficiency. However, its creative outputs lack the depth and sophistication of the GPT models.

GPT-4, in turn, excels in generating nuanced and sophisticated content. It handles complex creative writing tasks like poetry with rich vocabulary and intricate metaphors, making it ideal for high-level creative projects.

And last, but the very opposite of least, GPT-4o pushes creative boundaries even further. With its enhanced neural architecture, the model excels at understanding and generating nuanced text, handling complex instructions, and maintaining context over long interactions, ensuring accurate and relevant outputs (or at least that’s what OpenAI promises).

So, although all three models show at least a good level of creativity, Llama 3 may be more efficient for basic creative tasks, while GPT-4 and GPT-4o offer more sophisticated and nuanced skills. 

Llama 3 vs. GPT-4 vs. GPT-4o — math tasks

When evaluating the performance of Llama 3, GPT-4, and GPT-4o in this area, benchmarks and user reports reveal distinct strengths for each model.

Llama 3 has shown solid performance in basic arithmetic and algebra, suitable for simpler math problems. It scored 72.1% (8B variant) and 89.1% (70B variant) on the GSM8K benchmark for grade school math tasks. However, it may not be as proficient in tackling more complex mathematical reasoning.

GPT-4 excels in advanced math tasks, demonstrating high accuracy in benchmarks like the 5-shot MMLU (86.4%). Its strong problem-solving skills make it ideal for higher-level mathematics, including calculus and linear algebra.

GPT-4o further enhances this proficiency with faster processing and multimodal capabilities, allowing it to interpret and solve math problems presented in diverse formats. This makes GPT-4o particularly powerful in scenarios requiring detailed and accurate mathematical understanding.

When is Llama 3 better than GPT-4 and GPT-4o, and when is it not?

Choosing between Llama 3, GPT-4 Turbo, and GPT-4o depends on your specific needs and the task at hand. Each model has distinct strengths that make it suitable for different scenarios. Here are a few practical use cases to give you a better perspective on that:

Use Case No 1: Educational tools

When to choose Llama 3?

Use Llama 3 to create simple educational apps or tools that require efficient text processing and language understanding. Its cost-effectiveness and open-source nature make it suitable for educational institutions with limited budgets.

When to choose GPT-4 Turbo?

GPT-4 Turbo is ideal for developing interactive educational platforms that require detailed explanations and high-level reasoning. Its ability to handle complex tasks and large context windows ensures thorough and engaging educational content.

When to choose GPT-4o?

Choose GPT-4o for educational tools that integrate multimedia content, such as interactive lessons combining text, images, and audio. Its advanced multimodal capabilities make learning more dynamic and immersive.

Use Case No 2: Virtual AI assistant

When to choose Llama 3?

Llama 3 is a good choice for virtual assistants that manage basic scheduling and reminders. Its efficiency and ease of customization make it perfect for straightforward, low-resource applications.

When to choose GPT-4 Turbo?

Opt for GPT-4 Turbo if your AI assistant needs to handle more complex queries, provide detailed information, and support longer conversations. The model’s capabilities can ensure an advanced understanding of the queries and provide accurate, context-aware responses.

When to choose GPT-4o?

GPT-4o is best for high-end virtual assistants that offer multimodal interactions, such as interpreting visual inputs or engaging in real-time voice conversations. Its enhanced performance and multimodal integration provide a richer user experience.

Is Llama 3 better than GPT-4? Llama 3 vs. GPT-4 vs. GPT-4o model comparison

In the ever-evolving landscape of generative AI, choosing the right model is crucial for the success of your project. Llama 3, GPT-4 Turbo, and GPT-4o each bring unique strengths to the table, making them suitable for different applications.

Llama 3 shines in efficiency and cost-effectiveness, making it ideal for simpler tools and assistants.. GPT-4 Turbo excels in complex reasoning and handling complicated tasks, perfect for interactive platforms and more sophisticated virtual assistants. Meanwhile, GPT-4o’s multimodal capabilities set it apart for dynamic, multimedia-rich applications.

Ultimately, the best choice between Llama 3, GPT-4, and GPT-4o depends on your specific needs and the nature of your project, whether it be educational tools, creative writing assistants for marketers, internal tools for your team, or other AI-powered software. By carefully evaluating the abilities of each model, you can leverage the right one to maximize your success. And if you need a hand, Neoteric is here to help!

Still wondering which model — Llama 3, GPT-4 Turbo, or GPT-4o — is better for your project? Fill out the form below 👇

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