Meta Llama 4 Complete Guide 2026: Scout, Maverick, and Behemoth
Meta's Llama 4 is the most powerful open-source AI model family ever released. With 10 million token context, 128 mixture-of-experts, and native multimodal support, it's changing how founders build AI products. Here's everything you need to know.
What Is Llama 4?
Llama 4 is Meta's fourth generation of Large Language Models, released on April 5, 2025. Unlike GPT-5 and Claude 5, Llama 4 is open-source - meaning you can download the weights, run it on your own hardware, and fine-tune it for your specific use case.
The "Llama 4 herd" includes three models designed for different use cases:
- Llama 4 Scout: 17B active parameters, 10M context window - for massive data analysis
- Llama 4 Maverick: 17B active parameters, 128 experts, 400B total params - the generalist workhorse
- Llama 4 Behemoth: 288B active parameters, 2T total params - the flagship model
Why Open Source Matters for Founders
Open-source means no API costs, no rate limits, full data privacy, and the ability to fine-tune for your specific domain. You own the model and can run it wherever you want.
The Three Llama 4 Models
Llama 4 Scout
Best for: Processing massive documents, entire codebases, long-form analysis, or any task requiring understanding of huge context. Fits on a single H100 GPU with Int4 quantization.
Llama 4 Maverick
Best for: General-purpose AI tasks - coding, chatbots, technical assistants, content generation. The workhorse model that balances capability with efficiency. Was co-distilled from Behemoth.
Llama 4 Behemoth
Best for: Advanced research, STEM tasks, model distillation. Outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks like MATH-500 and GPQA Diamond. Still in preview as of release.
Key Features of Llama 4
Native Multimodality
Built from the ground up to understand text, images, and video together - not bolted on as an afterthought. Seamless cross-modal reasoning.
Mixture of Experts (MoE)
Only activates relevant parts of the model for each task. Massive total parameters but efficient inference - lower costs at higher performance.
200 Language Support
Trained on 200 languages from all parts of the globe. Build truly global AI products without separate models for each region.
Reduced Bias
Significantly better than Llama 3 on bias reduction. Refuses less on debated topics (7% to <2%) and more balanced on political/social content.
Agentic Capabilities
Llama 4 can plan, execute tasks, understand context over time, and take action autonomously. Browse web, execute code, use APIs.
Open Weights
Download from Hugging Face. Run locally, fine-tune, deploy anywhere. Full control over your AI infrastructure.
Llama 4 vs GPT-5 vs Claude 5 vs Gemini 3
How does Meta's open-source offering compare to the closed alternatives?
| Feature | Llama 4 Maverick | GPT-5 | Claude 5 Sonnet | Gemini 3 |
|---|---|---|---|---|
| Open Source | Yes | No | No | No |
| Context Window | 1M (Scout: 10M) | 128K | 1M | 2M |
| Multimodal | Text+Image+Video | Text+Image | Text+Image | Text+Image+Video+Audio |
| Self-Host | Yes, free | No | No | No |
| Fine-Tuning | Full access | Limited | Limited | Limited |
| API Cost | Free (self-host) | $5/1M input | $3/1M input | $3.50/1M input |
| Data Privacy | Full (on-prem) | Via API | Via API | Via API |
| STEM Benchmarks | Behemoth leads | Strong | Strong | Strong |
When to Choose Llama 4
Choose Llama 4 when: you need data privacy, want to avoid API costs at scale, need to fine-tune for a specific domain, or want to run AI on-premise. Choose GPT-5/Claude when: you want the easiest integration and don't mind API costs.
How to Get Started with Llama 4
Option 1: Download and Run Locally
# Install required libraries
pip install transformers accelerate torch
# Download Llama 4 Maverick
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "meta-llama/Llama-4-Maverick"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype="auto"
)
# Generate text
inputs = tokenizer("Write a Python function to", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
Option 2: Use via Cloud Providers
If you don't have the hardware to run Llama 4 locally, use it via:
- Amazon Bedrock: Fully managed, serverless Llama 4
- Amazon SageMaker: Deploy on your own AWS instances
- Groq: Ultra-fast inference at competitive prices
- Together AI: Simple API access to Llama models
- Replicate: Pay-per-use Llama 4 inference
# Example: Using Llama 4 via Together AI
import together
together.api_key = "your-api-key"
response = together.Complete.create(
model="meta-llama/Llama-4-Maverick",
prompt="Explain quantum computing in simple terms:",
max_tokens=500
)
print(response["output"]["choices"][0]["text"])
Option 3: Fine-Tune for Your Domain
The real power of open-source AI is fine-tuning. You can create a specialized model for your industry:
# Fine-tune Llama 4 with your data
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir="./llama4-my-domain",
per_device_train_batch_size=4,
num_train_epochs=3,
learning_rate=2e-5,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=your_dataset,
)
trainer.train()
Use Cases for Founders
1. Build AI Products Without API Costs
At scale, API costs for GPT-5 or Claude can reach tens of thousands per month. With Llama 4, your only cost is compute. For high-volume applications, this changes the economics entirely.
2. Data-Sensitive Industries
Healthcare, finance, legal - industries where data can't leave your infrastructure. Llama 4 runs entirely on-premise, so sensitive data never touches external servers.
3. Domain-Specific AI Assistants
Fine-tune Llama 4 on your company's documentation, codebase, or industry data. Create an AI that knows your domain better than any general-purpose model.
4. Embedded AI in Products
Ship Llama 4 as part of your product. No API dependencies, no ongoing costs to providers, no risk of model deprecation or pricing changes.
5. Research and Experimentation
Full model weights mean full control. Understand how the model works, experiment with architectures, contribute to open-source AI research.
Hardware Requirements
What do you need to run Llama 4?
| Model | Min GPU RAM | Recommended | Quantized Option |
|---|---|---|---|
| Scout (17B active) | 24GB | 1x H100 80GB | Single H100 with Int4 |
| Maverick (400B total) | 80GB | 2-4x H100 | 1-2x H100 with Int4 |
| Behemoth (~2T) | 320GB+ | 8x H100 cluster | Not recommended |
Cost-Effective Inference
Don't have H100s? Use cloud providers like Lambda Labs (~$2.50/hr for H100), Vast.ai (marketplace pricing), or the hosted APIs mentioned above. Quantized versions (Int4/Int8) dramatically reduce requirements.
Llama 4 Partners and Ecosystem
Meta has built a massive ecosystem around Llama 4:
- NVIDIA: Optimized inference with TensorRT-LLM
- AWS: Bedrock and SageMaker integration
- Databricks: Enterprise deployment tools
- Groq: Custom LPU inference hardware
- Dell: On-premise hardware solutions
- Snowflake: Data platform integration
- 25+ additional partners
Limitations to Know
- Hardware requirements: Running locally requires significant GPU resources
- Not as refined as closed models: GPT-5 and Claude 5 often have better instruction following for certain tasks
- Behemoth still in preview: The flagship model isn't fully released yet
- Less polish: Closed models have more RLHF refinement and safety tuning
- No built-in moderation: You're responsible for implementing safety guardrails
The Future: Meta's AI Strategy
Mark Zuckerberg's vision is clear: "Our goal is to build the world's leading AI, open source it, and make it universally accessible so that everyone in the world benefits."
However, Meta has signaled that future "superintelligence" models may not be open-sourced. The company is balancing open-source leadership with competitive pressures and safety considerations.
Bottom Line for Founders
Llama 4 is a game-changer for founders who want to:
- Own their AI stack: No vendor lock-in, no API dependencies
- Control costs: Eliminate per-token pricing at scale
- Protect data: Keep everything on-premise
- Differentiate: Fine-tune for your specific domain
- Build moats: Create proprietary AI capabilities competitors can't easily replicate
Whether you use Llama 4 directly or through a cloud provider, having this option changes the competitive dynamics of AI. You're no longer entirely dependent on OpenAI or Anthropic's pricing and product decisions.
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