Llama 4 Scout
Meta's efficient multimodal model with industry-leading 10M token context
Llama 4 Scout
Meta • April 2025
Training Data
Up to August 2024
Llama 4 Scout
April 2025
Parameters
109 billion (17B active)
Training Method
Mixture of Experts
Context Window
10,000,000 tokens
Knowledge Cutoff
August 2024
Key Features
Open Source • 10M Token Context • 16 Experts MoE
Capabilities
Context: Outstanding
Efficiency: Outstanding
Multimodal: Very Good
What's New in This Version
Industry-leading 10M token context window with efficient single-GPU deployment
Meta's efficient multimodal model with industry-leading 10M token context
What's New in This Version
Industry-leading 10M token context window with efficient single-GPU deployment
Technical Specifications
Key Features
Capabilities
Other Meta Models
Explore more models from Meta
Muse Spark Open
Meta's open-weight frontier model — an 816B mixture-of-experts with 42B active, natively multimodal, built for coding and long-horizon agents, and able to run on two Mac Studios
Muse Wonder
Meta's first frontier-class model, closed-weight and built for personal and professional agents, with Active Memory across conversations and Active Focus on long goals; its larger sibling, Muse Wonder Full, is reserved for paid Muse Code subscribers
Muse Spark 1.3
Meta's most capable Muse model, built for long-horizon agentic work — it collaborates, asks before consequential actions, and holds complex instructions across multi-step tasks