Meta - Amazon Bedrock
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Meta

The following Meta models are available in Amazon Bedrock:

Model Description
Llama 4 Maverick 17B InstructLlama 4 Maverick is Meta's 17-billion active parameter mixture-of-experts model with 128 experts, optimized for multimodal chat and instruction following.
Llama 4 Scout 17B InstructLlama 4 Scout is Meta's 17-billion active parameter mixture-of-experts model with 16 experts and a 10M token context window for long-document tasks.
Llama 3.3 70B InstructLlama 3.3 70B Instruct is Meta's 70-billion parameter model with improved efficiency, delivering strong reasoning and coding performance with a 128K context window.
Llama 3.2 11B InstructLlama 3.2 11B Instruct is Meta's 11-billion parameter multimodal model that processes both text and images with a 128K context window.
Llama 3.2 1B InstructLlama 3.2 1B Instruct is Meta's ultra-lightweight 1-billion parameter model optimized for on-device and edge deployment.
Llama 3.2 3B InstructLlama 3.2 3B Instruct is Meta's 3-billion parameter lightweight model designed for on-device tasks like summarization and instruction following.
Llama 3.2 90B InstructLlama 3.2 90B Instruct is Meta's 90-billion parameter multimodal model that processes text and images for visual reasoning and document understanding.
Llama 3.1 405B InstructLlama 3.1 405B Instruct is Meta's largest open model with 405 billion parameters and a 128K context window, supporting tool use and multilingual tasks.
Llama 3.1 70B InstructLlama 3.1 70B Instruct is Meta's 70-billion parameter model with an extended 128K context window and support for tool use and code generation.
Llama 3.1 8B InstructLlama 3.1 8B Instruct is Meta's compact 8-billion parameter model with a 128K context window, suitable for edge deployment and fine-tuning.
Llama 3 70B InstructLlama 3 70B Instruct is Meta's 70-billion parameter instruction-tuned model with an 8K context window, optimized for dialogue and assistant-like tasks.
Llama 3 8B InstructLlama 3 8B Instruct is Meta's 8-billion parameter instruction-tuned model with an 8K context window, designed for efficient deployment on smaller infrastructure.