Meta recently launched the Llama 3.3 70B-instruct model – and while it’s not an earth-shattering release – it does have its perks among other LLM models (cue: pricing). The model succeeds the Llama 3.2 that launched in November. The previous Llama models were lauded for their efficiency, and the new 3.3 doesn’t disappoint. But what about its coding performance? This blog covers the Llama 3.3 70B release, looking at its features and pricing and comparing it to the GPT-4o for its textual and coding capabilities.
Is Llama 3.3 any Good?
Llama 3.3-70b-instruct builds on its predecessors, using 70 billion parameters to deliver exceptional performance for instruction-based tasks. While smaller than Meta’s Llama 3.1 405B model, Llama 3.3 is optimized for efficiency in text-only applications. It also uses advanced techniques such as Grouped-Query Attention (GQA), which enhances its scalability and efficiency during inference.
Besides, Llama 3.3 is trained on a diverse dataset comprising over 15 trillion tokens from publicly available sources, ensuring its broad knowledge base while maintaining a knowledge cut-off date of December 2023.
Here are some metrics to give you a technical outlook of Llama 3.3:
Key Features of Llama 3.3:
- Instruction-tuned architecture: Specifically designed to excel in complex task execution.
- Backward compatibility: Works seamlessly with prompts from earlier Llama models.
- Multilingual capabilities: Supports diverse languages for a wide range of global applications.
- Open-source availability: Its open architecture enables cost-effective integration into diverse projects, democratizing access to advanced AI.
A notable advantage of Llama 3.3 is its ability to run on consumer-grade hardware, such as laptops with sufficient RAM, making it highly accessible for small-scale developers and businesses.
Here’s a detailed benchmark table comparing Llama 3.3-7B against its siblings:
Llama 3.3 in Comparison with GPT-4o
GPT-4o, along with Claude 3.5 Sonnet, is a benchmark model known for its 128K token context window, which allows for extensive text input and generation while maintaining coherence. With a knowledge cutoff in October 2023, it delivers relevant responses across various domains. Key specifications include a generation speed of 77.4 tokens per second and a cost structure of $2.50 per million tokens for input and $10 per million tokens for output, making it ideal for applications where performance is crucial. Additionally, it supports up to 16.4K tokens per generation, excelling in complex, context-heavy tasks.
In contrast, Llama 3.3 features 70 billion parameters and is optimized for efficiency in text-only applications. Although smaller than GPT-4o, Llama 3.3 performs well in tasks like translation and dialogue generation, often achieving competitive results. It is also significantly more affordable, with input costs ranging from $0.10 to $0.60 per million tokens (since Llama 3.3 is open-source, the costs can differ based on the provider). It is a cost-effective choice for developers who want high-quality outputs without the higher expenses associated with GPT-4o.
Llama 3.3 vs GPT-4o Performance Benchmarks
Performance benchmarks are crucial for evaluating the practical utility of AI models. Both Llama 3.3 and GPT-4o were assessed across various metrics, including reasoning, coding proficiency, and general knowledge. Here’s a comparison table:
| Benchmark | GPT-4o Performance | PerformanceLlama 3.3 Performance |
| MMLU (5-shot) | 88.70% | 86% (0-shot) |
| MMLU-Pro (Robust MMLU) | 74.68% | 68.9% (5-shot) |
| HumanEval (Code Generation) | 90.2% (0-shot) | 88.4% (pass@1) |
| Multimodal Understanding | 69.10% | Not applicable |
Note: 0-shot describes the testing setup (no examples given), while pass@1 measures success within that setup (correct output on the first try).
While GPT-4o consistently outperforms Llama 3.3 in metrics such as MMLU and multimodal understanding, the latter delivers near-parity in tasks like coding proficiency. This is particularly noteworthy given its smaller size and more efficient design.
Llama 3.3 vs GPT-4o Cost Comparison
When choosing an AI model, cost is often a key consideration, especially for individuals or businesses with limited budgets. In this respect, Llama 3.3 offers a much more affordable option than GPT-4o. The pricing for Llama 3.3 varies depending on the platform, with input costs ranging from $0.10 to $0.60 per million tokens (compared to GPT-4o’s $.25) and output costs between $0.40 and $0.88 per million tokens (compared to GPT-4o’s $10). This flexible pricing structure allows developers to select the best plan based on their specific needs.
When you factor in both input and output costs, Llama 3.3 comes out to about 19.8 times cheaper than GPT-4o. This dramatic cost difference makes Llama 3.3 a highly attractive choice for developers looking for powerful AI solutions that won’t break the bank. Its affordability is especially beneficial for startups and smaller businesses that want to take advantage of cutting-edge AI technology without overspending.
Llama 3.3 vs GPT-4o Practical Use Cases
Beyond raw numbers, the value of an AI model lies in its applicability across real-world scenarios. Here’s a more nuanced look at how Llama 3.3 and GPT-4o stack up:
Coding and Development
Both models perform well on HumanEval, with GPT-4o scoring slightly higher. However, Llama 3.3 offers exceptional cost-efficiency, making it a preferred choice for smaller development teams. GPT-4o’s larger context window, on the other hand, is better suited for complex projects requiring extensive documentation or code generation.
Multilingual and Domain-Specific Applications
Llama 3.3’s instruction-tuned architecture enables it to excel in multilingual tasks, catering to businesses operating in diverse linguistic markets. GPT-4o, while also capable of handling multiple languages, shines in domain-specific applications where maximum accuracy is essential.
Accessibility and Deployment
Llama 3.3’s ability to run on local hardware makes it ideal for organizations with limited access to cloud infrastructure. In contrast, GPT-4o requires robust cloud resources, which can drive up costs but provide unmatched scalability for enterprise-level operations.
Token Context Management
With its 128K token context window, GPT-4o is unbeatable for applications like research, legal analysis, or long-form content creation. Llama 3.3, with a smaller context window, is better suited for concise tasks where ultra-high token capacity is unnecessary.
Llama 3.3 vs GPT-4o Coding Examples
Here are some prompts you can try to test each of these models. For Llama 3.3, you can head over to Meta AI and enter the prompt. For testing GPT-4o, you can go here.
1. Python: “Write a Python function that takes a list of integers and returns the largest prime number in the list. If no prime numbers are present return -1.” (Tests basic algorithm implementation and handling edge cases)
2. JavaScript: “Create a JavaScript function that uses Promises to fetch data from this URL: ‘[INSERT YOUR URL]’ and then logs the ‘title’ property to the console.” (Tests asynchronous programming and API interaction)
3. Java: “Implement a Java class representing a ‘BankAccount’ with methods for deposit, withdrawal, and getting the current balance. Ensure that withdrawals cannot result in a negative balance by throwing an exception.” (Tests object-oriented programming principles and exception handling)
4. C#: “Write a C# LINQ query that filters a list of strings to only include those that contain the substring ‘abc’ and then orders them alphabetically.” (Tests LINQ usage and string manipulation)
5. Go: “Develop a Go function that takes a string as input and returns a map where the keys are the unique words in the string and the values are their respective counts.” (Tests data structures, string processing, and Go’s map functionality)
The Bottom Line
Both Llama 3.3 and GPT-4o represent significant advancements in AI, but their distinct features cater to different user needs:
- Llama 3.3 is ideal for budget-conscious developers seeking powerful yet accessible AI solutions. Its affordability and compatibility with local hardware make it a game-changer for small and medium-sized enterprises.
- GPT-4o excels in high-performance tasks where accuracy, context handling, and scalability are non-negotiable. Despite its higher costs, it remains the go-to choice for enterprise-level applications requiring cutting-edge AI capabilities.
As we continue to see more and more LLMs emerge, models like Llama 3.3 highlight a growing trend towards democratizing technology, ensuring that even small-scale developers can harness the power of artificial intelligence. Meanwhile, GPT-4o sets a benchmark for premium, large-scale applications, ensuring its relevance in the most demanding scenarios. To try GPT-4o, Claude 3.5 Sonnet, and similar cutting-edge AI models, head over to Bind AI copilot.