Llama 3.1 405B

Generates coherent multi-turn conversations and long-form content up to 128K tokens using a transformer architecture trained on 15+ trillion tokens. Implements standard causal language modeling with attention mechanisms optimized for extended context, enabling document-length reasoning and synthesis without context truncation. The 128K window allows processing of entire codebases, research papers, or conversation histories in a single inference pass.

Generates fluent text in 8 supported languages using a unified transformer trained on multilingual corpora. The model learns language-agnostic representations during training, allowing it to switch between languages and handle code-switching within single responses. Supports conversational agents, translation-adjacent tasks, and localized content generation without language-specific fine-tuning.

Detects and flags prompt injection attacks using Prompt Guard, a security tool released alongside 405B. Prompt Guard classifies prompts to identify attempts to manipulate model behavior through adversarial inputs, enabling security-aware applications to reject or handle suspicious prompts. The tool operates as a separate classification model that scores prompt safety before inference.

Llama 3.1 405B is accessible to end users through WhatsApp (US only) and meta.ai web interface, enabling non-technical users to interact with the model without API integration or infrastructure setup. These consumer deployments abstract away inference complexity and provide familiar interfaces for conversational AI. The model powers Meta's consumer AI products, demonstrating production-grade reliability and safety.

Llama 3.1 405B is distributed as open-weight model files through Hugging Face Model Hub and llama.meta.com, enabling developers to download and deploy the model locally or on custom infrastructure. The model is released under an open license (specific license terms not enumerated in documentation) that allows commercial use and modification. Distribution includes model weights in standard formats compatible with popular inference frameworks.

Meta provides reference implementations and system prompts for building custom agents, conversational systems, and applications using Llama 3.1 405B. The reference system includes best practices for prompt engineering, tool integration, safety filtering, and multi-turn conversation management. Developers can use these references as starting points for building domain-specific applications without starting from scratch.

Enables distillation of 405B knowledge into smaller, faster models through synthetic data generation and fine-tuning. The model can generate training data for smaller models, and its outputs can be used as targets for knowledge distillation. This capability is explicitly called out as 'never achieved at this scale in open source,' enabling organizations to create specialized, efficient models that inherit 405B's capabilities.

Generates syntactically correct and functionally sound code across multiple programming languages using transformer-based code understanding trained on code-heavy portions of the 15+ trillion token dataset. Achieves 89% pass rate on HumanEval benchmark, indicating strong capability for function-level code generation, completion, and bug fixing. Works through standard next-token prediction with learned patterns from diverse codebases.

Solves grade-school math word problems and multi-step mathematical reasoning tasks with 96.8% accuracy on the GSM8K benchmark. Implements chain-of-thought reasoning patterns learned during training on mathematical problem-solving data within the 15+ trillion token corpus. The model breaks down problems into intermediate steps and performs arithmetic reasoning without external calculators.

Executes tool calls and function invocations through learned patterns in the transformer, enabling the model to decide when to invoke external APIs, databases, or code execution environments. Implements tool use as a learned behavior during training rather than through constrained decoding, allowing flexible tool composition and multi-step tool orchestration. The model generates structured tool calls that downstream systems parse and execute.

Generates high-quality synthetic training data that can be used to train smaller models through distillation, leveraging the 405B model's reasoning and knowledge to create diverse, labeled datasets. The model produces varied outputs across different prompts and temperature settings, enabling creation of large synthetic datasets without manual annotation. This capability enables open-source model distillation at scale, previously unavailable in the open-source ecosystem.

Answers factual questions and performs reasoning across diverse knowledge domains (science, history, law, medicine, etc.) with 88.6% accuracy on the MMLU benchmark. Implements knowledge retrieval through learned patterns in the 405B transformer trained on 15+ trillion tokens, enabling broad-domain question-answering without external knowledge bases. The model reasons through multiple-choice questions and open-ended queries using learned world knowledge.

Follows complex, multi-part instructions and adapts behavior based on system prompts, in-context examples, and user directives through learned instruction-following patterns in the transformer. The model interprets nuanced requests, respects tone and style preferences, and maintains consistency with specified constraints throughout long conversations. Steerability is achieved through training on diverse instruction-following examples within the 15+ trillion token dataset.

Executes inference across multiple GPUs using distributed tensor parallelism and pipeline parallelism, coordinated through inference frameworks and cloud platforms. The 405B model is available through 25+ ecosystem partners (AWS, Azure, Google Cloud, NVIDIA, Groq, Databricks, etc.) on day one, each providing optimized inference infrastructure and APIs. Inference is not available as single-GPU deployment; all inference requires multi-GPU coordination.

Filters unsafe content and detects policy violations using Llama Guard 3, a companion safety model released alongside 405B. Llama Guard 3 classifies inputs and outputs against safety categories (violence, sexual content, illegal activity, etc.), enabling content moderation in both user inputs and model outputs. The safety model is integrated into the ecosystem but operates as a separate inference pass, not built into 405B itself.