Amazon Nova Pro is a cutting-edge multimodal model designed to handle text, image, and video inputs with advanced processing capabilities. Featuring a 300K-token context window, it excels in document analysis, visual question answering, and complex agent-driven workflows. As part of the Amazon Nova foundation models, it supports fine-tuning and distillation, enabling deep customization for various applications.
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 02, 2024 |
Modalities Types of data this model can process | text images video |
API Providers The providers that offer this model. (This is not an exhaustive list.) | Amazon Bedrock |
Knowledge Cut-off Date When the model's knowledge was last updated. | Purposefully not disclosed |
Open Source Whether the model's code is available for public use. | No |
Pricing Input Cost for processing tokens in your prompts | $0.80 per million tokens |
Pricing Output Cost for tokens generated by the model | $3.20 per million tokens |
MMLU Massive Multitask Language Understanding - Tests knowledge across 57 subjects including mathematics, history, law, and more | 85.9% CoT 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 | 89% pass@1 Source |
MATH Tests mathematical problem-solving abilities across various difficulty levels | 76.6% CoT Source |
GPQA Tests PhD-level knowledge in chemistry, biology, and physics through multiple choice questions that require deep domain expertise | 46.9% Main 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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