Llama 3.3 70B Instruct, created by Meta, is a multilingual large language model specifically fine-tuned for instruction-based tasks and optimized for conversational applications. It is capable of processing and generating text in multiple languages, with a context window supporting up to 128,000 tokens. Launched on December 6, 2024, the model surpasses numerous open-source and proprietary chat models in various industry benchmarks. It utilizes Grouped-Query Attention (GQA) to improve scalability and has been trained on a diverse dataset comprising over 15 trillion tokens from publicly available sources. The model's knowledge is current up to December 2023.
Web Site AI Model Web Page | |
Provider The entity that provides this model. | |
Chat Input a message to start chatting | - |
Release Date When the model was first released. | 1 year ago Dec 06, 2024 |
Modalities Types of data this model can process | text |
API Providers The providers that offer this model. (This is not an exhaustive list.) | Fireworks, Together, DeepInfra, Hyperbolic |
Knowledge Cut-off Date When the model's knowledge was last updated. | 12.2024 |
Open Source Whether the model's code is available for public use. | Yes |
Pricing Input Cost for processing tokens in your prompts | $0.23 per million tokens |
Pricing Output Cost for tokens generated by the model | $0.40 per million tokens |
MMLU Massive Multitask Language Understanding - Tests knowledge across 57 subjects including mathematics, history, law, and more | 86% 0-shot, CoT Source |
MMLU-Pro A more robust MMLU benchmark with harder, reasoning-focused questions, a larger choice set, and reduced prompt sensitivity | 68.9% 5-shot, CoT Source |
MMMU Massive Multitask Multimodal Understanding - Tests understanding across text, images, audio, and video | Not available |
HellaSwag A challenging sentence completion benchmark | Not available |
HumanEval Evaluates code generation and problem-solving capabilities | 88.4% pass@1 Source |
MATH Tests mathematical problem-solving abilities across various difficulty levels | 77% 0-shot, CoT Source |
GPQA Tests PhD-level knowledge in chemistry, biology, and physics through multiple choice questions that require deep domain expertise | 50.5% 0-shot, CoT Source |
IFEval Tests model's ability to accurately follow explicit formatting instructions, generate appropriate outputs, and maintain consistent instruction adherence across different tasks | 92.1% Source |
SimpleQA Assessing the accuracy of simple questions | - |
AIME 2024 | - |
AIME 2025 | - |
Aider Polyglot Multilingual programming benchmark. | - |
LiveCodeBench v5 Benchmark for real-time programming | - |
Global MMLU (Lite) A simplified version of the benchmark for assessing the universality of models at the global level. | - |
MathVista Evaluates the mathematical reasoning abilities of AI models within visual contexts | - |
Mobile Application | - |
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