Llama 3.3 70B vs Llama 3.2 90B: Text Mastery or Visual Power

The Llama series of large language models from Meta has seen rapid evolution, with frequent updates and new releases. Two notable models are Llama 3.3 70B and Llama 3.2 90B. This article delves into the technical aspects, performance, and practical applications of each model, offering a comparison to help developers choose the right tool for their tasks.

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
• Model Scale:
  • meta-llama/llama-3.1-1B
  • meta-llama/llama-3.1-3B
  • meta-llama/llama-3.1-11B
  • meta-llama/llama-3.1-90B
• Key Features:
  • Multimodal model, supports both text and image inputs
  • Supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai

Model Comparison

In summary, Llama 3.2 90B has a larger model size and supports multimodal capabilities, making it more versatile for tasks involving vision. Llama 3.3 70B, while smaller, offers specific quantization precision options. Both models maintain the same context window size, ensuring consistent performance in handling large text inputs.

Speed Comparison

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

Speed Comparison

Cost Comparison

Llama 3.3 70B has a clear advantage in output speed, making it suitable for tasks that require rapid generation of large amounts of text. Llama 3.2 90B (Vision) performs slightly better in terms of total response time and latency, making it suitable for applications that require fast response and low latency. Although Llama 3.2 90B (Vision) has stronger multimodal capabilities, it is not as fast as Llama 3.3 70B in pure text processing speed. 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.

Llama 3.3 70B outperforms Llama 3.2 90B in all tests, particularly excelling in the HumanEval and MATH tests. Despite having more parameters and multimodal capabilities, Llama 3.2 90B does not perform as well as Llama 3.3 70B in pure text tasks.

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
• Is Llama 3.3 70B Really Comparable to Llama 3.1 405B?

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

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.

Both Llama 3.3 70B and Llama 3.2 90B offer unique advantages tailored to different use cases. Llama 3.3 excels in text-based tasks requiring strong multilingual capabilities and instruction following with an emphasis on efficiency, while Llama 3.2 shines in multimodal applications involving image understanding.

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