Nova Pro

AmazonAmazonProprietary

描述

Amazon Nova Pro is a highly-capable multimodal model with state-of-the-art performance across text, image, and video understanding. It excels at core capabilities like language understanding, mathematical reasoning, and multimodal tasks while offering industry-leading speed and cost efficiency.

發布日期

2024-12-03

參數規模

—

上下文長度

300K

支援模態

image, pdf, text, video

能力雷達圖

general

coding

reasoning

science

agents

multimodal

排行榜排名

領域	#排名	分數	來源
程式碼能力榜	520	19.0	AA
通用能力榜	501	28.0	AA
多模態榜	128	35.0	LS
科學能力	527	25.0	AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

Chat

IFEvalGoogle Research (2023)

92.1%自報

Finance

FinQA

77.2%自報

General

MMLU

85.9%自報

BFCL

68.4%自報

Language

Translation en→Set1 COMET22

89.1%自報

Translation Set1→en COMET22

89.0%自報

Translation Set1→en spBleu

44.4%自報

Translation en→Set1 spBleu

43.4%自報

Math

GSM8k

94.8%自報

MATH

76.6%自報

Multimodal

GroundUI-1K

81.4%自報

VATEX

77.8%自報

MM-Mind2Web

63.7%自報

MMMU

61.7%自報

Reasoning

ARC-C

94.8%自報

ChartQAMasry et al. (2022)

89.2%自報

HumanEvalOpenAI (2021)

89.0%自報

BBH

86.9%自報

DROP

85.4%自報

CRAG

50.3%自報

GPQANYU + Cohere + Anthropic (2023)

46.9%自報

Summarization

SQuALITY

19.8%自報

Vision

DocVQADocVQA (2020)

93.5%自報

TextVQA

81.5%自報

VisualWebBench

79.7%自報

EgoSchema

72.1%自報

LVBench

41.6%自報

AA 評測指數

(Artificial Analysis)

Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))

78.6

Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))

69.1

Gpqa(NYU + Cohere + Anthropic (2023))

49.9

Ifbench(Google Research (2023))

38.1

Livecodebench(UC Berkeley + MIT + Cornell (2024))

23.3

Lcr(Artificial Analysis)

21.0

Tau2(Sierra + U Toronto + Vector Institute (2025))

14.0

Aime(MAA (Mathematical Association of America))

10.7

Aime 25(MAA (Mathematical Association of America))

7.0

Intelligence Index(Artificial Analysis)

7.0

Math Index(Artificial Analysis)

7.0

Terminalbench Hard(Stanford × Laude Institute (2026))

6.1

Hle(Center for AI Safety + Scale AI (2025))

3.2

LLM Stats 分類評分

(LLM Stats (zeroeval))

Chat

Image To Text

Instruction Following

Legal

Structured Output

Code

Math

Reasoning

Language

Multimodal

Finance

General

Healthcare

Tool Calling

Vision

Frontend Development

Agents

Economics

Physics

Biology

Chemistry

Long Context

Summarization

定價

輸入價格$0.8 / 1M tokens

輸出價格$3.2 / 1M tokens

混合價格(3:1)$1.4 / 1M tokens

快取讀取價格$0.2 / 1M tokens

快取寫入價格$0.8 / 1M tokens

速度

Tokens/秒0.0

首Token延遲0.00s

首回答延遲0.00s

供應商價格排行

2 個供應商

最便宜: Amazon最貴: Amazon Bedrock

供應商輸入輸出

1Amazon主要

$0.8

$3.2

2Amazon Bedrock

$0.8

$3.2

比較該模型在不同 API 供應商之間的定價。

外部連結

LLM Stats Artificial Analysis