Llama 3.1 405B Instruct
Optimized for multilingual dialogue, outperforming open-source and closed chat models on industry benchmarks.
Meta 
Amazon Bedrock 405B multilingual instruction-tuned text generation
Llama 3.1 405B Instruct is a 405-billion-parameter large language model developed by Meta, part of the Llama 3.1 collection that also includes 8B and 70B variants. It is an instruction-tuned model designed for text-in, text-out tasks, with a 128,000-token context window and support for multiple languages. The model was released as part of Meta's open-weight model series, making the weights publicly accessible for research and commercial use under Meta's Llama license.
This model is optimized specifically for multilingual dialogue use cases, making it well-suited for conversational AI applications, content generation, summarization, and complex instruction-following tasks across languages. At 405 billion parameters, it is the largest model in the Llama 3.1 family and is available through cloud providers including AWS Bedrock. Developers working on applications that require long-context understanding or multilingual support will find the 128K context window and instruction-tuning particularly relevant.
What Llama 3.1 405B Instruct supports
Multilingual Dialogue
Handles conversational tasks across multiple languages, optimized through instruction tuning for multilingual dialogue use cases.
Long Context Window
Supports up to 128,000 tokens of context, enabling processing of long documents, extended conversations, or large code files in a single pass.
Instruction Following
Fine-tuned to follow complex, multi-step instructions accurately, making it suitable for task-oriented applications and structured workflows.
Text Summarization
Condenses long-form content into concise summaries, leveraging the 128K context window to handle lengthy source material without truncation.
Code Generation
Generates and explains code across common programming languages as a text-generation task, supported by the model's large parameter count.
Reasoning Tasks
Applies multi-step reasoning to answer complex questions, analyze arguments, or work through logical problems in natural language.
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Get Started FreeCommon questions about Llama 3.1 405B Instruct
What is the context window for Llama 3.1 405B Instruct?
The model supports a context window of 128,000 tokens, allowing it to process long documents, extended conversations, or large inputs in a single request.
What type of inputs and outputs does this model support?
Llama 3.1 405B Instruct is a text-in, text-out model. It accepts text prompts and returns text responses; it does not natively process images, audio, or other modalities.
Is there a knowledge cutoff date for this model?
A specific training cutoff date is not listed in the available metadata for this model. Meta has not publicly specified an exact cutoff in the model card metadata provided here.
How is this model made available, and can I access the weights?
Llama 3.1 405B Instruct is available through cloud providers such as AWS Bedrock. Meta also releases the model weights publicly under the Llama 3.1 community license, allowing download and self-hosting subject to Meta's license terms.
What languages does this model support?
The model is described as multilingual and is optimized for multilingual dialogue use cases. Specific supported languages are detailed in Meta's official model documentation.
What people think about Llama 3.1 405B Instruct
Community discussions referencing Llama 3.1 405B appear primarily in the LocalLLaMA subreddit, where users discuss open-weight models in the context of comparing generations and evaluating multilingual performance. Threads show interest in how the Llama 3.1 family holds up as newer models like Llama 4 are anticipated or released.
Some community threads raise concerns about benchmark reliability across open-source models generally, with users noting that benchmark scores do not always reflect real-world performance. Multilingual evaluation, particularly for non-English languages, is a recurring topic of interest among LocalLLaMA users.
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