Activefrontierllm Open Source

Llama 3.1 405B

by Meta· Released July 2024· Cutoff December 2023

Llama 3.1 405B is Meta's largest and most capable open-source language model, featuring 405 billion parameters. It excels in general knowledge, reasoning, and multilingual tasks, and is designed for synthetic data generation and model distillation. As the flagship of the Llama 3.1 family, it offers state-of-the-art performance among open models.

Official Site API Docs 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

128K tokens

Max output

4096 tokens

Modalities

text

Parameters

405B

License

Llama 3.1 Community License

Capabilities

Function CallingCode GenerationMultilingual TranslationReasoningText GenerationSummarizationQuestion AnsweringSynthetic Data Generation

Best For

High-performance open-source applications requiring advanced reasoning, multilingual support, and large-scale text generation.

Strengths

Largest open-source model with 405B parameters
128K token context window
Strong multilingual capabilities across 8 languages
Top-tier performance on reasoning and coding benchmarks

Limitations

Requires significant computational resources for inference
Not multimodal (text-only)
May exhibit biases present in training data
Higher latency compared to smaller models

Use Cases

Synthetic data generation for training smaller models

Complex reasoning and problem-solving tasks

Multilingual customer support chatbots

Code generation and debugging assistance

Content creation and summarization

Academic research and experimentation

Enterprise knowledge management systems

Improvements Over Previous Model

Context window increased from 8K to 128K tokens
Added support for function calling and tool use
Improved multilingual performance across 8 languages
Higher benchmark scores on reasoning, coding, and math tasks
New model distillation and synthetic data generation capabilities
Enhanced safety and alignment through iterative refinement

Back to all models

Improvements Over Previous Model

Context window increased from 8K to 128K tokens

Added support for function calling and tool use

Improved multilingual performance across 8 languages

Higher benchmark scores on reasoning, coding, and math tasks

New model distillation and synthetic data generation capabilities

Enhanced safety and alignment through iterative refinement