NVIDIA's Llama 3.1 Nemotron 70B is a powerful language model optimized for delivering accurate and informative responses. Built on the Llama 3.1 70B architecture and enhanced with Reinforcement Learning from Human Feedback (RLHF),it achieves top performance in automatic alignment benchmarks. Designed for applications demanding high precision in response generation and helpfulness, this model is well-suited for a wide range of user queries across multiple domains.
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. | 2 years ago Oct 15, 2023 |
Modalities Types of data this model can process | text |
API Providers The providers that offer this model. (This is not an exhaustive list.) | OpenRouter |
Knowledge Cut-off Date When the model's knowledge was last updated. | - |
Open Source Whether the model's code is available for public use. | Yes |
Pricing Input Cost for processing tokens in your prompts | $0.35 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 | 85% 5-shot Source |
MMLU-Pro A more robust MMLU benchmark with harder, reasoning-focused questions, a larger choice set, and reduced prompt sensitivity | Not available |
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 | 75% Source |
MATH Tests mathematical problem-solving abilities across various difficulty levels | 71% Source |
GPQA Tests PhD-level knowledge in chemistry, biology, and physics through multiple choice questions that require deep domain expertise | Not available |
IFEval Tests model's ability to accurately follow explicit formatting instructions, generate appropriate outputs, and maintain consistent instruction adherence across different tasks | Not available |
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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