# Llama 4 Review 2026: Meta’s Most Advanced Open-Source AI Family
Meta launched **Llama 4** in early April 2026, introducing a new generation of Mixture of Experts (MoE) architecture that delivers frontier-class performance while maintaining efficiency through selective expert activation.
## The MoE Revolution Continues
Llama 4 ships in two primary variants, both leveraging MoE architecture to achieve massive total parameter counts while keeping active parameters manageable:
| Model | Total Params | Active Params | Architecture | Context Window |
|——-|————-|—————|————–|—————-|
| **Llama 4 Scout** | 109B | 17B | MoE (16 experts) | **10M tokens** |
| **Llama 4 Maverick** | 400B | 17B | MoE (128 experts) | 1M tokens |
The headline feature of Llama 4 Scout is its **10 million token context window**—a capability previously unheard of in production models. This enables processing of massive documents, entire codebases, or extended video content in a single pass.
## Benchmark Performance
| Model | MMLU-Pro Score | Category Ranking |
|——-|—————|——————|
| **Qwen 3 MoE 235B** | 81.5% | Best overall |
| Qwen 3 72B | 79.8% | Best dense |
| **Llama 4 Maverick** | 78.2% | Best multilingual MoE |
| Llama 4 Scout | 73.1% | Best context |
Llama 4 Maverick specifically excels in **multilingual tasks**, making it ideal for global applications requiring high-quality translation and cross-lingual understanding.
## Hardware Requirements
Despite massive total parameter counts, MoE architecture keeps VRAM requirements surprisingly reasonable:
– **Llama 4 Scout**: ~34 GB (FP16) with 17B active params
– **Llama 4 Maverick**: ~34 GB (FP16) with 17B active params
– **Quantized (Q4_K_M)**: Feasible on consumer GPUs with 24-48 GB VRAM
## Deployment Ecosystem
Meta has invested heavily in deployment tooling:
“`bash
# Official Llama Stack
pip install llama-stack
llama stack build –template llama4-scout
# Hugging Face GGUF (community)
# Llama-4-Scout-GGUF: 180K+ downloads in first week
“`
The rapid community response includes quantization packs and fine-tuning integrations, with GGUF variants making local inference accessible.
## Licensing Considerations
Llama 4 uses the **Llama 4 Community License**—a permissive license allowing:
– Commercial use
– Fine-tuning and adaptation
– Local deployment
– Redistribution of modified versions
Review the specific terms at Meta’s official repository for detailed compliance requirements.
## Our Verdict
Llama 4 Scout’s 10M token context window is a game-changer for applications requiring extensive document processing or code analysis. Combined with Maverick’s multilingual excellence, the Llama 4 family offers unmatched flexibility for both research and production use cases.
The MoE architecture democratizes access to massive model capabilities on reasonable hardware, continuing Meta’s commitment to open-source AI accessibility.
**Rating: 4.7/5**
—
*For most developers, Llama 4 Scout’s extended context makes it the default choice for document-heavy applications.*