Llama 3.1 70b vs. Llama 3.3 70b: Better Performance, Higher Price

Llama 3.3 70b and Llama 3.1 70b, developed by Meta, are large language models with significant differences. Let’s compare their performance, resource efficiency, applications, and how to choose and access them.

Basic Introduction of Models Families

To begin our comparison, we first understand the fundamental characteristics of each model.

Llama 3.1 Model Family Characteristics

• Release Date: Early 2024
• Model Scale:
  • meta-llama/llama-3.1-8b-instruct
  • meta-llama/llama-3.1-70b-instruct
• Key Features:
  • Context Window Expansion to 128k tokens. 
  • Multilingual Capability Enhancement
  • Resource Efficiency

Llama 3.3 Model Family Characteristics

• Release Date: Mid-2024
• Model Scale:
  • meta-llama/llama-3.3-70b-instruct
• Key Innovations:
  • Optimized transformer architecture
  • Trained using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
  • Incorporates 15 trillion tokens of publicly available data in its training.
  • The suggested approach utilizes grouped query attention (GMA) to enhance inference scalability.
  • Supports eight core languages with a focus on quality over quantity.

Performance Comparison

Now that we’ve established the basic characteristics of each model, let’s delve into their performance across various benchmarks. This comparison will help illustrate their strengths in different areas.

Benchmark	Meaning	Llama 3.1 70b	Llama 3.3 70b
MMLU(5-shot）	MMLU (Massive Multitask Language Understanding) evaluates general language understanding across diverse tasks.	66.4	68.9
HumanEval	HumanEval tests a model’s ability to write correct Python code based on given problem descriptions.	80.5	88.4
MATH	MATH assesses mathematical problem-solving capabilities of models.	68	77.0
MBPP	MBPP (Modern Biology Problem Solving) measures AI’s ability to solve problems in biological sciences.	86	87.6

As we can see from this table, Llama 3.3 70b demonstrates particular strengths in all dimensions.

If you would like to know more about the llama3.3 benchmark knowledge. You can view this article as follows: Llama 3.3 Benchmark: Key Advantages and Application Insights.

Resource Efficiency

When evaluating the efficiency of a Large Language Model (LLM), it’s crucial to consider three key categories: the model’s inherent processing capabilities, API performance, and hardware requirements.

If you want to use them, Novita AI provides a $0.5 credit to get you started !

Applications and Use Cases

Both models are suitable for similar applications, including:

• Multilingual chat
• Coding assistance
• Synthetic data generation
• Text summarization
• Content creation
• Localization
• Knowledge-based tasks
• Tool use

Llama 3.3 70b may perform better in these applications, especially in multilingual dialogue scenarios, due to its optimizations

Accessibility and Deployment through Novita AI

Step 1: Log In and Access the Model Library

Log in to your account and click on the Model Library button.

Step 2: Choose Your Model

Browse through the available options and select the model that suits your needs.

Step 3: Start Your Free Trial

Begin your free trial to explore the capabilities of the selected model.

Step 4: Get Your API Key

To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.

Step 5: Install the API

Install API using the package manager specific to your programming language.

After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for pthon users.

 from openai import OpenAI

client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    # Get the Novita AI API Key by referring to: https://novita.ai/docs/get-started/quickstart.html#_2-manage-api-key.
    api_key="<YOUR Novita AI API Key>",
)

model = "meta-llama/llama-3.3-70b-instruct"
stream = True  # or False
max_tokens = 512

chat_completion_res = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": "Act like you are a helpful assistant.",
        },
        {
            "role": "user",
            "content": "Hi there!",
        }
    ],
    stream=stream,
    max_tokens=max_tokens,
)

if stream:
    for chunk in chat_completion_res:
        print(chunk.choices[0].delta.content or "")
else:
    print(chat_completion_res.choices[0].message.content)

Upon registration, Novita AI provides a $0.5 credit to get you started!

If the free credits is used up, you can pay to continue using it.

Conclusion

In conclusion, the choice between Llama 3.1 70B and Llama 3.3 70B depends on the specific requirements of your application and the available hardware resources. Llama 3.1 70B excels in terms of cost and latency, making it well-suited for applications that demand quick responses and cost efficiency. On the other hand, Llama 3.3 70B shines in maximum output and throughput, making it ideal for applications that require the generation of long texts and high throughput, albeit with higher hardware demands. Therefore, it is crucial to weigh these factors carefully to select the model that best fits your needs.

Frequently Asked Questions

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Recommend Reading

• How to Access Llama 3.3 70b Locally or via API: A Complete Guide
• Qwen 2.5 72b vs Llama 3.3 70b: Which Model Suits Your Needs?
• Llama 3.3 70B: Features, Access Guide & Model Comparison