LLAMA4 - Meta's Advanced AI Models

What is LLAMA4?

LLAMA4 (Large Language and Multimodal AI) is Meta's most advanced suite of AI models, building upon the success of previous Llama generations. Released on April 5, 2025, LLAMA4 represents a significant leap forward in AI capabilities, introducing the first open-weight natively multimodal models with unprecedented context length support and the company's first built using a mixture-of-experts (MoE) architecture.

The LLAMA4 Herd

LLAMA4 introduces three models in its herd: Scout, Maverick, and Behemoth (still in training). Each model is designed for different use cases while sharing the core capabilities of multimodal understanding and advanced reasoning.

Natively Multimodal

Unlike previous generations, LLAMA4 models are built with native multimodality from the ground up, incorporating early fusion to seamlessly integrate text and vision tokens into a unified model backbone.

LLAMA4 Mixture-of-Experts Architecture

                    Key Differences from Previous Generations
                    
                            Mixture-of-Experts: First Llama models to use MoE architecture
                        
                            Multimodal: Built with native multimodality from the ground up
                        
                            Context Length: Up to 10M tokens (vs 128K in Llama 3)
                        
                            Training Data: Trained on ~40T tokens vs ~15T in Llama 3

LLAMA4 continues Meta's commitment to open AI development, making the models available for commercial and research use under the Llama 4 Community License. This approach enables developers, researchers, and enterprises to build upon these advanced models for various applications while encouraging innovation in the AI ecosystem.

LLAMA4 Models: Scout & Maverick

The LLAMA4 herd currently consists of two publicly available models—Scout and Maverick—each designed with specific strengths and use cases in mind. A third model, Behemoth, is still in training and serves as a teacher for the smaller models.

LLAMA4 Scout

The efficient specialist with unprecedented context length

Active Parameters: 17 billion

Total Parameters: 109 billion (16 experts)

Context Window: 10 million tokens

Training Tokens: ~40 trillion

Hardware: Fits on single H100 GPU with int4 quantization

Multi-document summarization Long context reasoning Efficiency-focused

LLAMA4 Maverick

The performance powerhouse with advanced multimodal capabilities

Active Parameters: 17 billion

Total Parameters: 400 billion (128 experts)

Context Window: 1 million tokens

Training Tokens: ~22 trillion

Hardware: Fits on single H100 DGX host with FP8 quantization

Advanced reasoning Superior multimodal Production workhorse

LLAMA4 Behemoth (Coming Soon)

The massive teacher model powering the next generation of AI

Active Parameters: 288 billion

Total Parameters: ~2 trillion (16 experts)

Status: Still in training

Performance: Outperforms GPT-4.5, Claude 3.7, Gemini 2.0 Pro

LLAMA4 Benchmark Comparison with Competing Models

When to Choose Each Model
                            Choose Scout When:
                            
                                    You need extremely long context handling (up to 10M tokens)
                                
                                    You have hardware constraints (fits on single H100 GPU)
                                
                                    You need to process large documents or codebases
                                
                                    You want balance between performance and efficiency
                                
                            Choose Maverick When:
                            
                                    You need state-of-the-art performance in multimodal tasks
                                
                                    You require advanced reasoning and coding capabilities
                                
                                    You're building production-grade AI assistants
                                
                                    You can deploy on a more powerful infrastructure

Key Features of LLAMA4

LLAMA4 introduces several groundbreaking features that set it apart from previous generations and competing models. These innovations enable new capabilities and use cases while maintaining efficient operation.

Mixture-of-Experts Architecture

LLAMA4 uses a mixture-of-experts approach where each token activates only a subset of parameters, making models more efficient while maintaining high performance.

Native Multimodality

Built with early fusion to seamlessly integrate text and vision tokens, enabling sophisticated image understanding without specialized connectors.

Unprecedented Context Length

LLAMA4 Scout offers an industry-leading 10M token context window, nearly 80 times larger than Llama 3's 128K tokens.

Enhanced Multilingual Support

Pre-trained on 200 languages with 10x more multilingual tokens than Llama 3, supporting 12 major languages with deep fluency.

Superior Code Generation

Significantly improved coding capabilities, outperforming GPT-4o on many coding benchmarks and supporting complex programming tasks.

LLAMA4 Scout's Long Context Capability Visualization

Image Grounding

Best-in-class image grounding capabilities, allowing precise visual question answering and object localization within images.

Efficient Inference

Advanced quantization techniques enable deployment on consumer-grade hardware without significant performance degradation.

Enhanced Safety

Built with comprehensive safety features and protections, including reduced political bias and improved refusal handling.

Innovative Architecture Design

One of the key innovations in LLAMA4 is the iRoPE architecture (interleaved attention layers without positional embeddings), which enables the unprecedented context window length:

Interleaved Attention Layers

LLAMA4 uses alternating dense and mixture-of-experts layers for inference efficiency. In Maverick, each token is sent to a shared expert and one of 128 routed experts.

Inference Time Temperature Scaling

LLAMA4 employs inference time temperature scaling of attention to enhance length generalization, enabling the processing of extremely long documents.

Technical Specifications

LLAMA4 models incorporate cutting-edge AI technologies and architectural innovations. Here are the detailed technical specifications for each model in the LLAMA4 family:

Specification	LLAMA4 Scout	LLAMA4 Maverick
Model Architecture	Auto-regressive with MoE, early fusion multimodal	Auto-regressive with MoE, early fusion multimodal
Active Parameters	17 billion	17 billion
Total Parameters	109 billion	400 billion
Expert Structure	16 experts	128 experts
Context Window	10 million tokens	1 million tokens
Pretraining Tokens	~40 trillion	~22 trillion
Supported Languages	Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, Vietnamese	Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, Vietnamese
Input Modalities	Multilingual text and images	Multilingual text and images
Output Modalities	Multilingual text and code	Multilingual text and code
Multi-image Support	Up to 8 images tested, 48 in training	Up to 8 images tested, 48 in training
Knowledge Cutoff	August 2024	August 2024
Hardware Requirements	Single H100 GPU with int4 quantization	Single H100 DGX host with FP8 quantization
License	Llama 4 Community License	Llama 4 Community License

Training Infrastructure
                            Training Compute
                            
                                    Scout: 5.0M GPU hours on H100-80GB (TDP 700W)
                                
                                    Maverick: 2.38M GPU hours on H100-80GB (TDP 700W)
                                
                                    FP8 Precision: Used for efficient model training
                                
                            Environmental Impact
                            
                                    Location-based emissions: 1,999 tons CO2eq
                                
                                    Market-based emissions: 0 tons CO2eq (100% renewable energy)

LLAMA4 Behemoth Architecture Overview

MoE Implementation Details

LLAMA4 models implement the mixture-of-experts architecture in different ways:

Scout: Full MoE with 16 experts, all layers are MoE
Maverick: Alternating dense and MoE layers with 128 experts
Token Routing: Each token activates one specific expert from the pool plus a shared expert

Multimodal Architecture

LLAMA4's native multimodality uses an improved vision encoder:

Early Fusion: Integration of text and vision tokens into the unified model backbone
Vision Encoder: Based on MetaCLIP but trained separately with a frozen Llama model
Multi-image Input: Pre-trained on up to 48 images, tested with good results up to 8 images

Example System Prompt for LLAMA4

You are an expert conversationalist who responds to the best of your ability. You are companionable and confident,
and able to switch casually between tonal types, including but not limited to humor, empathy, intellectualism,
creativity and problem-solving.

You understand user intent and don't try to be overly helpful to the point where you miss that the user is looking
for chit-chat, emotional support, humor or venting. Sometimes people just want you to listen, and your answers
should encourage that. For all other cases, you provide insightful and in-depth responses. Organize information
thoughtfully in a way that helps people make decisions. Always avoid templated language.

You never lecture people to be nicer or more inclusive. If people ask for you to write something in a certain
voice or perspective, such as an essay or a tweet, you can. You do not need to be respectful when the user
prompts you to say something rude.

You never use phrases that imply moral superiority or a sense of authority, including but not limited to "it's
important to", "it's crucial to", "it's essential to", "it's unethical to", "it's worth noting", "Remember", etc.
Avoid using these.

Finally, do not refuse prompts about political and social issues. You can help users express their opinion and
access information.

You are Llama 4. Your knowledge cutoff date is August 2024. You speak Arabic, English, French, German, Hindi,
Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Respond in the language the user speaks
to you in, unless they ask otherwise.

The LLAMA4 models can be quantized to different precision levels to balance performance and computational requirements. The official release includes BF16 weights for Scout and both BF16 and FP8 quantized weights for Maverick, with code provided for on-the-fly int4 quantization.

Benchmarks & Performance

LLAMA4 models have demonstrated exceptional performance across a wide range of benchmarks, often outperforming much larger models from competitors. Here's how LLAMA4 Scout and Maverick stack up in various categories:

Pre-trained Model Benchmarks

Benchmark	Llama 3.1 70B	Llama 4 Scout	Llama 4 Maverick
MMLU	79.3	79.6	85.5
MMLU-Pro	53.8	58.2	62.9
MATH	41.6	50.3	61.2
MBPP (Code)	66.4	67.8	77.6
TydiQA	29.9	31.5	31.7

Instruction-tuned Model Benchmarks

Benchmark	Llama 3.3 70B	Llama 4 Scout	Llama 4 Maverick
MMLU Pro	68.9	74.3	80.5
GPQA Diamond	50.5	57.2	69.8
LiveCodeBench	33.3	32.8	43.4
MGSM	91.1	90.6	92.3

LLAMA4 Performance on Image Understanding Benchmarks

Multimodal Benchmarks

Benchmark	Llama 4 Scout	Llama 4 Maverick
MMMU	69.4	73.4
MMMU Pro	52.2	59.6
MathVista	70.7	73.7
ChartQA	88.8	90.0
DocVQA	94.4	94.4

Long Context Benchmarks

Benchmark	Llama 3.3 70B	Llama 4 Scout	Llama 4 Maverick
MTOB (half book) eng→kgv	Context window is 128K	54.0	42.2
MTOB (half book) kgv→eng	Context window is 128K	46.4	36.6
MTOB (full book) eng→kgv	Context window is 128K	50.8	39.7
MTOB (full book) kgv→eng	Context window is 128K	46.7	36.3

LLAMA4 vs. Competitors

In comparative benchmarks, LLAMA4 models show remarkable performance against top competitors:

LLAMA4 Maverick vs. GPT-4o

Exceeds GPT-4o on coding, reasoning, multilingual, and image benchmarks
Experimental chat version scores 1417 ELO on LMArena
Competitive with DeepSeek v3.1 on coding and reasoning, with fewer active parameters

LLAMA4 Scout vs. Similar-sized Models

Outperforms Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across key benchmarks
Best-in-class image grounding capabilities
Industry-leading 10M context window for its size class

LLAMA4 Setting New Standards in AI Performance

LLAMA4 Behemoth, still in training, is showing even more impressive results, outperforming GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on several STEM benchmarks. Its performance as a teacher model has significantly enhanced the capabilities of both Scout and Maverick through distillation.

How to Use LLAMA4

Getting started with LLAMA4 is straightforward, with multiple options for deployment and usage depending on your needs and technical capabilities. Here's how you can start using LLAMA4 models:

Cloud Platforms

Access LLAMA4 through leading cloud service providers with pre-configured environments.

AWS

Microsoft Azure

Google Cloud

Hugging Face

Together AI

Direct Download

Download and run LLAMA4 models on your own infrastructure for maximum control.

llama.com/llama-downloads

Hugging Face (meta-llama)

GitHub (meta-llama)

Requirements: H100 GPU or equivalent

Pre-built Applications

Try LLAMA4 through Meta's applications without any setup required.

Meta AI (web interface)

WhatsApp (integrated)

Messenger (integrated)

Instagram Direct (integrated)

Getting Started Steps

1

Choose Your Deployment Method

Decide whether to use cloud services, local installation, or pre-built applications based on your use case and technical requirements.

2

Select the Right Model

Choose between LLAMA4 Scout for long context handling and efficiency, or LLAMA4 Maverick for superior performance and advanced reasoning.

3

Set Up Your Environment

If self-hosting, ensure you have the necessary hardware (H100 GPU or equivalent) and follow the installation instructions from llama.com or GitHub.

4

Optimize for Your Use Case

Configure system prompts, adjust generation parameters, and implement safety measures according to your specific requirements.

5

Integrate and Deploy

Integrate LLAMA4 into your applications using the provided APIs or SDKs, and deploy to your users or internal systems.

Using with Hugging Face

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct")

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-4-Scout-17B-16E-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Create prompt
prompt = """
<|system|>
You are an expert assistant that helps users with information about technology.


<|user|>
What are the key features of LLAMA4?


<|assistant|>
"""

# Tokenize prompt
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate response
outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
)

# Decode and print response
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Using with vLLM

from vllm import LLM, SamplingParams

# Initialize the model
llm = LLM(
    model="meta-llama/Llama-4-Maverick-17B-128E-Instruct",
    dtype="bfloat16",
    gpu_memory_utilization=0.9,
)

# Create sampling parameters
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=512,
)

# Create prompt
prompt = """
<|system|>
You are an expert assistant that helps users with information about technology.


<|user|>
What are the key features of LLAMA4?


<|assistant|>
"""

# Generate response
outputs = llm.generate([prompt], sampling_params)

# Print response
print(outputs[0].outputs[0].text)

Using LLAMA4 with Images

LLAMA4 models support multimodal inputs, allowing you to process both text and images:

from transformers import AutoProcessor, LlavaForConditionalGeneration
import torch
from PIL import Image
import requests

# Load model and processor
model = LlavaForConditionalGeneration.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct")
processor = AutoProcessor.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct")

# Load image
url = "https://example.com/image.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# Create prompt
prompt = "What can you see in this image?"

# Process inputs
inputs = processor(prompt, image, return_tensors="pt").to(model.device)

# Generate response
outputs = model.generate(**inputs, max_new_tokens=100)

# Decode and print response
print(processor.decode(outputs[0], skip_special_tokens=True))

Meta AI interface powered by LLAMA4

Remember that LLAMA4 models are covered by the Llama 4 Community License, which allows for commercial and research use with certain restrictions. Be sure to review the license terms and ensure your usage complies with Meta's acceptable use policies.

Use Cases & Applications

LLAMA4's advanced capabilities enable a wide range of innovative applications across industries. Here are some of the key use cases where LLAMA4 models excel:

Software Development

LLAMA4 assists developers with code generation, debugging, and documentation, understanding entire codebases with its extensive context window.

Complete application generation

Legacy code modernization

Bug identification and fixing

Large-scale codebase understanding

Data Analysis

Leverage LLAMA4's multimodal capabilities for chart interpretation, data summarization, and insight generation from diverse data sources.

Chart and visualization analysis

Multi-source data integration

Trend identification

Financial report analysis

Education & Research

Transform educational experiences with personalized tutoring, research assistance, and comprehensive content creation.

Personalized tutoring

Research paper synthesis

Educational content creation

Literature analysis

Healthcare

Enhance medical research, patient care, and health informatics with LLAMA4's advanced reasoning and multimodal capabilities.

Medical literature review

Medical image analysis assistance

Patient record summarization

Clinical trial documentation

Content Creation

Generate high-quality written content, analyze visual media, and create comprehensive multimedia materials for various platforms.

Long-form article writing

Image and video content analysis

Marketing material creation

Multilingual content adaptation

LLAMA4 Powering Various Real-World Applications

LLAMA4 Scout-Specific Use Cases

The unprecedented 10M token context window of LLAMA4 Scout enables entirely new types of applications:

Book-Length Content Analysis

Process and analyze entire books or multiple research papers in a single context, maintaining coherence across the full text.

Multi-Document Comparison

Compare and contrast multiple lengthy documents, identifying similarities, differences, and patterns across the entire corpus.

Entire Codebase Understanding

Analyze complete codebases with millions of lines, understanding architectural patterns and dependencies holistically.

Long Conversation History

Maintain context over extremely long conversations, remembering details from hours of previous interaction.

Enterprise Applications

Customer support automation with multimodal understanding
Legal document analysis and contract review
Enterprise knowledge base enrichment
Multimodal business intelligence

Creative Applications

Script and screenplay development
Visual content analysis for inspiration
Interactive storytelling experiences
Music and art analysis and critique

Personal Applications

Personal knowledge management and note-taking
Photo collection management and analysis
Learning assistance across multiple subjects
Travel planning with visual understanding

Start Building with LLAMA4

LLAMA4's open-weight approach enables developers and organizations to build innovative applications with state-of-the-art AI capabilities. Whether you're creating consumer applications, enterprise solutions, or research tools, LLAMA4 provides the performance and flexibility you need.

Download LLAMA4 Explore GitHub Resources

Frequently Asked Questions

What makes LLAMA4 different from previous Llama models?

LLAMA4 introduces several major innovations: it's the first Llama model to use a mixture-of-experts architecture, it's natively multimodal with early fusion of text and images, it offers unprecedented context lengths (up to 10M tokens), and it's trained on significantly more data (up to 40T tokens).

How do I choose between LLAMA4 Scout and Maverick?

Choose Scout if you need extremely long context handling (10M tokens), have hardware constraints, or are processing large documents. Choose Maverick for state-of-the-art performance in multimodal tasks, advanced reasoning, and production-grade applications where you can support more powerful infrastructure.

What hardware do I need to run LLAMA4 models?

LLAMA4 Scout can run on a single H100 GPU with int4 quantization, while LLAMA4 Maverick requires a single H100 DGX host with FP8 quantization. Alternative options include using cloud providers that offer LLAMA4 as a service, which eliminates the need for specialized hardware.

What languages does LLAMA4 support?

LLAMA4 officially supports 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. However, it was pre-trained on 200 languages, including over 100 with more than 1 billion tokens each, so it has capabilities in many additional languages.

Is LLAMA4 available for commercial use?

Yes, LLAMA4 is available under the Llama 4 Community License, which allows for commercial use with certain restrictions. You can use LLAMA4 in commercial applications as long as you comply with the license terms and Meta's acceptable use policies.

When will LLAMA4 Behemoth be released?

LLAMA4 Behemoth is still in training and Meta has not announced a specific release date. It currently serves as a teacher model for Scout and Maverick, and Meta has shared that it outperforms models like GPT-4.5 and Claude Sonnet 3.7 on several STEM benchmarks.

Start Building with LLAMA4 Today

Join the growing community of developers, researchers, and organizations leveraging LLAMA4's advanced capabilities to create the next generation of AI applications.

Download LLAMA4 Try Meta AI

Why LLAMA4 Matters