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MODELS

Llama 4 Maverick

by Meta

multimodalllamameta
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Overview

Meta Llama 4 Maverick is an open-weight, natively multimodal Mixture-of-Experts model for text and image understanding.

Details

Llama 4 Maverick is part of Meta’s Llama 4 family. Meta describes it as a natively multimodal open-weight MoE model with 17B active parameters, 128 experts, text-and-image understanding, and a 1M-token context window. The official model card identifies Llama 4 Maverick as a 17B-active-parameter, 400B-total-parameter multimodal MoE model and documents intended use, benchmarks, safeguards, red teaming, and system protections.

When to Use

Use when you need an open-weight Llama 4 model with both text and image understanding. Use when evaluating a large-context multimodal model; Meta’s launch materials describe a 1M-token context window. Use via managed platforms when appropriate: AWS Bedrock documents Llama 4 Maverick 17B Instruct with Converse and Invoke API access and Google Cloud lists token pricing for Llama 4 Maverick.

Getting Started

  1. Read the Llama 4 model page and the Llama 4 model-card and prompt-format documentation on llama.com.
  2. Review the official GitHub model card for intended use
  3. benchmarks
  4. safeguards
  5. red teaming
  6. and system protections.
  7. Check Meta’s Llama get-started page for downloads and partner access options.
  8. If using a cloud provider
  9. review the AWS Bedrock model card or Google Cloud pricing page for platform-specific access and costs.
  10. Review the Llama 4 Acceptable Use Policy before deploying applications built on the model.

Key Features

  • •Natively multimodal model for image and text understanding.
  • •Mixture-of-Experts architecture with 17B active parameters and 128 experts
  • •according to Meta’s launch materials.
  • •Official model card describes 400B total parameters for Llama 4 Maverick.
  • •Meta’s launch materials describe a 1M-token context window.
  • •Official documentation and GitHub resources include model card
  • •prompt-format information
  • •safeguards
  • •red teaming
  • •and system protections.

Capabilities

  • •text generation
  • •image understanding
  • •multimodal understanding
  • •long-context processing
  • •managed API access via supported cloud platforms

Last updated Jul 31, 2026