Llama 3.1 405B Instruct
Optimized for multilingual dialogue, outperforming open-source and closed chat models on industry benchmarks.
Meta 
DeepInfra Multilingual dialogue with 405B parameter scale
Llama 3.1 405B Instruct is a 405-billion-parameter instruction-tuned large language model developed by Meta, part of the Llama 3.1 collection that also includes 8B and 70B variants. It accepts text input and produces text output, and is instruction-tuned specifically for multilingual dialogue use cases across a range of languages. The model was released in July 2024 and supports a 128,000-token context window, making it suitable for tasks involving long documents or extended conversations.
At 405 billion parameters, this is the largest model in the Llama 3.1 family and is designed for use cases that benefit from greater model capacity, such as complex reasoning, nuanced instruction following, and multilingual text generation. Because it is released under Meta's Llama 3.1 community license, it can be used and fine-tuned by developers and researchers who agree to the license terms. It is best suited for applications requiring multilingual support, long-context comprehension, and detailed generative responses.
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 Processing
Supports a 128,000-token context window, enabling processing of long documents, extended conversations, or large code files in a single pass.
Instruction Following
Fine-tuned on instruction-following data to respond accurately to user prompts, system instructions, and multi-turn dialogue.
Text Generation
Generates coherent long-form text including summaries, essays, reports, and creative writing based on natural language prompts.
Complex Reasoning
Applies multi-step reasoning to answer questions, analyze arguments, and work through logical or analytical problems.
Code Generation
Generates and explains code across common programming languages, supported by the model's broad pretraining corpus.
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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 or extended multi-turn conversations in a single request.
What languages does this model support?
Llama 3.1 405B Instruct is optimized for multilingual dialogue. Meta's documentation for the Llama 3.1 family lists support for English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, among others.
What is the difference between this model and the 8B or 70B variants?
All three variants (8B, 70B, 405B) are instruction-tuned for multilingual dialogue, but the 405B model has significantly more parameters, which generally allows it to handle more complex tasks and nuanced instructions.
Is Llama 3.1 405B Instruct open source?
The model is released under Meta's Llama 3.1 community license, which permits use and fine-tuning subject to the license terms. It is not fully open source under a standard OSI-approved license, but weights are publicly available.
What is the training data cutoff for this model?
The metadata provided does not specify a training data cutoff date. Meta has not publicly disclosed a precise knowledge cutoff date for Llama 3.1 405B Instruct in the available documentation.
What people think about Llama 3.1 405B Instruct
Community discussions mentioning Llama 3.1 405B tend to appear in the context of comparing open-source models, with users in r/LocalLLaMA noting its place as a reference point for large open-weight models. Threads from early 2025 reflect ongoing interest in the Llama family as users anticipated the release of Llama 4.
Some community members raise concerns about benchmark reliability across large language models generally, a topic that surfaces in several of the threads found. The Llama 3.1 405B model is frequently referenced as a baseline for evaluating newer releases rather than as a primary deployment target.
"That feeling when the AIs are sharing one brain cell but you can't prove it" - BUT now researchers have
GPT -OSS is heavily trained on benchmark. scored rank 34 on simplebench worse than grok 2
While Waiting for Llama 4
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