Llama 4 Maverick
Active Parameters
400B
Context Length
1M
Modality
Multimodal
Architecture
Mixture of Experts (MoE)
License
Llama 4 Community License Agreement
Release Date
5 Apr 2025
Knowledge Cutoff
Aug 2024
API Pricing (per 1M)
Input: $0.26 · Output: $0.91
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
47x RTX 4090
24GB VRAM
Datacenter
13x NVIDIA A100
80GB VRAM
Apple Silicon
10x Apple M3 Max
128GB VRAM
1,000,000 tokens
Consumer
83x RTX 4090
24GB VRAM
Datacenter
21x NVIDIA A100
80GB VRAM
Apple Silicon
17x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#144
| Benchmark | Score | Rank |
|---|---|---|
Professional Knowledge | 0.805 | 29 |
StackUnseen | 0.319 | 32 |
Software Engineering | 0.21 | 37 |
Graduate-Level QA | 0.698 | 85 |
Agentic Index | 0.6 | 126 |
General Text | 1327 | 132 |
Coding Index | 0.16 | 136 |
Intelligence Index | 0.09 | 198 |
StackEval Archived | 0.923 | 7 |
QA Assistant Archived | 0.949 | 10 |
General Knowledge Reference | 0.855 | 11 |
Coding Archived | 0.16 | 20 |
Summarization Archived | 0.72 | 20 |
Rankings
Overall Rank
#144
Coding Rank
#119
About Llama 4 Maverick
Llama 4 Maverick is Meta's natively multimodal Mixture-of-Experts model with 400B total parameters activating 17B per token for production inference. It provides low-latency image and text understanding with early fusion across long contexts.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
96
Key-Value Heads
8
Attention Head Dimension
128
Position Embedding
ROPE
RoPE Theta
500,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
Swish
Dimensions
Hidden Dimension Size
12,288
Number of Layers
120
FFN Intermediate Size (Dense)
8,192
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
202,048
Mixture of Experts
Total Expert Parameters
17.0B
Number of Experts
128
Active Experts
2
Shared Experts
-
FFN Intermediate Size (per Expert)
-
Dense Layers Before MoE
-
About Llama 4
Meta's Llama 4 model family implements a Mixture-of-Experts (MoE) architecture for efficient scaling. It features native multimodality through early fusion of text, images, and video. This iteration also supports significantly extended context lengths, with models capable of processing up to 10 million tokens.