Which Llama 3 Model is Right for You? A Comparison Guide

Meta’s Llama series of large language models (LLMs) has rapidly evolved, with each iteration bringing new capabilities and improvements. This article provides a technical comparison of three notable models from the Llama family: Llama 3.3 70B, Llama 3.2 90B, and Llama 3.1 405B. The comparison aims to assist developers in making informed choices based on their specific needs and resource constraints, focusing on architecture, performance, and practical applications.

Basic Introduction of Model

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

Llama 3.3 70b

• Release Date: December 6, 2024
• Model Scale:
  • meta-llama/llama-3.3-70b-instruct
• Key Features:
  • Instruction-tuned,text-only model
  • Utilizes Grouped-Query Attention (GQA) for improved efficiency
  • Supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai

Llama 3.2 90b

• Release Date: September 25, 2024
• Other Llama 3.2 Models:
  • meta-llama/llama-3.2-1B
  • meta-llama/llama-3.2-3B
  • meta-llama/llama-3.2-11B
  • meta-llama/llama-3.2-90B
• Key Features:
  • Multimodal model, supports both text and image inputs
  • Supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai

Llama 3.1 405b

• Release Date: July 23, 2024
• Other Llama 3.1 Models:
  • meta-llama/llama-3.1-8b-instruct
  • meta-llama/llama-3.1-70b-instruct
• Key Features:
  • Supports 8 languages
  • 128K token context window

Model Comparison

Overall, these three versions of the Llama model differ in model size, architectural design, and quantification accuracy, but all maintain the same context size. Llama 3.1 405B has the largest parameter count, while Llama 3.3 70B is optimized in terms of architecture and quantization for greater efficiency.

Speed Comparison

If you want to test it yourself, you can start a free trial on the Novita AI website.

Speed Comparison

Cost Comparison

Taken together, Llama 3.2 90B (Vision) performs best in total response time and latency, while Llama 3.3 70B performs best in output speed. The Llama 3.1 405B performs poorly on all three metrics. This suggests that when selecting a model, these metrics need to be weighed based on specific application scenarios and requirements. And from the price point of view, llama 3.3 70b is more cost-effective.

Benchmark 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.

Summarize:

• Llama 3.3 70B: Best Maths and Q&A skills
• Llama 3.2 90B (Vision): supports multi-modal vision, suitable for visual tasks
• Llama 3.1 405B: Best multi-tasking understanding and code generation capabilities

When selecting a model, these indicators and capabilities need to be weighed based on specific application scenarios and requirements. 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.

If you want to see more comparisons between llama 3.3 and other models, you can check out these articles:

• Qwen 2.5 72b vs Llama 3.3 70b: Which Model Suits Your Needs?
• Llama 3.1 70b vs. Llama 3.3 70b: Better Performance, Higher Price
• Discover the Power of Llama 3 Models

Applications and Use Cases

Llama 3.3 70B:

• Multilingual chatbots and assistants
• Coding assistance and code generation
• Synthetic data generation
• Multilingual content creation and localization
• Knowledge-based applications like question answering

Llama 3.2 90B:

• Image understanding and reasoning
• Document-level understanding including charts and graphs
• Image captioning
• Visual grounding tasks
• Real-time language translation with visual inputs

Llama 3.1 405B:

• Large-scale synthetic data generation.
• Model distillation to improve smaller models.
• Advanced research and experimentation.
• Industry-specific solutions requiring high performance on complex tasks.

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

The Llama series offers a range of models tailored to different needs:

• Llama 3.3 (70B) balances performance with accessibility for diverse applications.
• Llama 3.2 (90B) introduces powerful multimodal capabilities for processing both image and text data.
• Llama 3.1 (405B) excels in complex tasks but demands significant resources.

Choosing the right model depends on specific project needs, computational resources, and whether multimodal capabilities are required.

Frequently Asked Questions

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Recommend Reading

• How to Access Llama 3.3 70b Locally or via API: A Complete Guide
• Llama 3.2 VS Claude 3.5: Which AI Model Suits Your Project?
• Llama 3.3 70B: Features, Access Guide & Model Comparison