NVIDIA Nemotron Nano 9B V2 (Non-reasoning)
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描述
NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks.
發布日期
2025-08-18
參數規模
8.9B
上下文長度
131K
支援模態
text
能力雷達圖
27
general
59
coding
61
reasoning
33
science
60
agents
0
multimodal
排行榜排名
基準測試分數 (LLM Stats)
(LLM Stats (zeroeval))Biology
GPQANYU + Cohere + Anthropic (2023)
64.0%自報
Code
LiveCodeBench
71.1%自報
General
IFEvalGoogle Research (2023)
90.3%自報
BFCL_v3_MultiTurn
66.9%自報
Math
MATH-500
97.8%自報
AIME 2025
72.1%自報
AA 評測指數
(Artificial Analysis)Math Index(Artificial Analysis)62.3
Intelligence Index(Artificial Analysis)7.2
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))0.7
Livecodebench(UC Berkeley + MIT + Cornell (2024))0.7
Aime 25(MAA (Mathematical Association of America))0.6
Gpqa(NYU + Cohere + Anthropic (2023))0.6
Ifbench(Google Research (2023))0.3
Lcr(Artificial Analysis)0.3
Tau2(Sierra + U Toronto + Vector Institute (2025))0.2
Scicode(UIUC + Argonne National Lab (2024))0.2
Hle(Center for AI Safety + Scale AI (2025))0.0
Terminalbench Hard(Stanford × Laude Institute (2026))0.0
LLM Stats 分類評分
(LLM Stats (zeroeval))Structured Output90
Instruction Following90
Math80
Reasoning80
General80
Code70
Physics60
Biology60
Chemistry60
定價
輸入價格$0.05 / 1M tokens
輸出價格$0.195 / 1M tokens
混合價格(3:1)$0.086 / 1M tokens
速度
Tokens/秒166.7
首Token延遲1.33s
首回答延遲1.33s
供應商價格排行
供應商價格排行
1 個供應商
供應商輸入輸出
1NVIDIA主要
$0.05
$0.195
比較該模型在不同 API 供應商之間的定價。