COR Brief
Conversational AI

Llama 4

The Future of Open-Source AI

Updated Dec 16, 2025Open Sourcev4 Scout

Native Multimodality: Built from the ground up to understand text, images, and video.

10M Token Context: Process entire codebases, books, or video libraries.

Mixture of Experts: Efficient architecture activating only relevant parameters.

Truly Open Source: Weights available for download, fine-tuning, and commercial use.

Pricing
Free
Category
Conversational AI
Company
Meta
Nano Banana SlidesOpen Fullscreen ↗
01
Understand and reason across text, images, and video natively.
02
Industry-leading context window for massive document processing.
03
Efficient Mixture of Experts design for optimal performance.
04
Available in Scout (17B), Maverick (128B), and Behemoth (400B+).
05
Download and run locally or fine-tune for your use case.
06
Free for commercial use with permissive licensing.

Enterprise Deployment

Companies need AI capabilities without cloud dependencies.

Research & Development

Researchers need to fine-tune models for specific domains.

Video Understanding

Media companies need to analyze and index video content.

Code Generation

Development teams need AI-assisted coding.

1
Choose Your Path
Use Meta AI for quick access or download weights for self-hosting.
2
Select Model Size
Choose Scout (17B), Maverick (128B), or Behemoth (400B+) based on needs.
3
Deploy
Use cloud providers or set up local infrastructure.
4
Fine-tune
Customize the model for your specific use case.
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Strategic Context for Llama 4

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Pricing
Model: Open Source
Open Source
Free
  • Full model weights
  • Commercial license
  • Community support
Meta AI
Free
  • Hosted access via meta.ai
  • No setup required
  • Basic features
Cloud Providers
Varies
  • AWS, Azure, GCP hosting
  • Managed infrastructure
  • Enterprise support
Assessment
Strengths
  • Truly open source with commercial license
  • Industry-leading 10M context window
  • Native multimodal capabilities
  • Multiple size options
  • Self-hosting possible
Limitations
  • Requires significant compute for larger models
  • Setup complexity for self-hosting
  • Community support only for open source
  • Some features still in development