Which Llama 4 Model Fits You Best—Maverick or Scout?

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The AI landscape is dominated by cutting-edge language models like Llama 4 Maverick and Gemma 3 27B, each tailored for distinct use cases. Llama 4 Maverick’s unmatched power, muWhy did Meta release not just one, but two flagship models—Llama 4 Maverick and Llama 4 Scout—on the same day?

Because one size doesn’t fit all.

While both models are built on cutting-edge Mixture-of-Experts architecture and support multimodal input, they serve very different needs. Maverick is a fast, versatile powerhouse for real-time, multimodal tasks. Scout, on the other hand, is a long-context specialist, built to handle deep reasoning across entire books or technical documents.

In this post, we’ll break down the core differences, real-world applications, and performance benchmarks to help you choose the right model for your use case.

Llama 4 Maverick vs Llama 4 Scout: Basic Introduction

Category	Llama 4 Maverick	Llama 4 Scout
Release Date	April 5, 2025	April 5, 2025
Model Size	400B (128 MoE, 17B active/token)	109B (16 MoE, 17B active/token)
Open Source	✅ Yes	✅ Yes
Architecture	MoE (128 Experts)	MoE + iRoPE (16 Experts + Interleaved RoPE)
Context Length	1M tokens	10M tokens
Language Support	200+ languages	200+ languages
Multimodal Input	Text + Image	Text + Image
Output Type	Multilingual Text + Code	Multilingual Text + Code

Llama 4 Maverick vs Llama 4 Scout: Training and Application

Maverick excels in multimodal understanding, making it ideal for use cases like ad recommendation, assistant systems, and scenarios requiring efficient processing of text-image combinations.

Scout, trained on a much larger and more diverse text-focused corpus, is designed for long-context tasks such as technical documentation, legal text analysis, and complex reasoning.

Category	Llama 4 Maverick	Llama 4 Scout
Training Data	~22T tokens (multimodal, including Meta content)	~40T tokens (language-rich, broader coverage)
Pre-Training	MetaP: Adaptive Experts + Mid-Training	Same
Post-Training	SFT → RL → DPO	Same

Llama 4 Maverick vs Llama 4 Scout: Benchmark

Overall, in various benchmark tests, Llama 4 Maverick scored higher than Llama 4 Scout in most cases, demonstrating more outstanding performance in aspects such as reasoning, coding, and long – context processing.

Llama 4 Maverick vs Llama 4 Scout: Speed Comparsion

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

Llama 4 Maverick vs Llama 4 Scout: Hardware Requirements

Maverick vs Scout: Maverick consumes more memory even at the same token length due to larger model size, while Scout is more optimized for ultra-long contexts but at the cost of extreme memory requirements.

Llama 4 Maverick vs Llama 4 Scout: Applications

Aspect	Llama 4 Maverick	Llama 4 Scout
Model Focus	General-purpose, optimized for multimodal and high-efficiency inference	Long-context specialist, designed for extended reasoning and memory-intensive tasks
Ideal Use Cases	AI assistants Ad recommendation Image+text Q&A Chatbots	Legal document analysis Technical manual parsing Full-book summarization
Deployment Target	Enterprises needing real-time, cost-balanced inference	Labs, research, or institutions handling ultra-long documents

How to Access Llama 4 via Novita API?

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 python users.

from openai import OpenAI
  
client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    api_key="<YOUR Novita AI API Key>",
)

model = "meta-llama/llama-4-scout-17b-16e-instruct"
stream = True # or False
max_tokens = 2048
system_content = """Be a helpful assistant"""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }

chat_completion_res = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": system_content,
        },
        {
            "role": "user",
            "content": "Hi there!",
        }
    ],
    stream=stream,
    max_tokens=max_tokens,
    temperature=temperature,
    top_p=top_p,
    presence_penalty=presence_penalty,
    frequency_penalty=frequency_penalty,
    response_format=response_format,
    extra_body={
      "top_k": top_k,
      "repetition_penalty": repetition_penalty,
      "min_p": min_p
    }
  )

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

Both Llama 4 Maverick and Llama 4 Scout serve distinct purposes. If you’re building high-speed, multimodal AI products, Maverick is your go-to. If you’re working with ultra-long documents or need persistent reasoning over tens of thousands of tokens, Scout is the better fit. Developers can try both via Novita AI’s platform, with a free trial and easy API access.

Frequently Asked Questions

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Recommend Reading

• DeepSeek V3: Advancing Open-Source Code Models, Now Available on Novita AI
• Deepseek v3 vs Llama 3.3 70b: Language Tasks vs Code & Math
• Llama 3.2 3B vs DeepSeek V3: Comparing Efficiency and Performance.