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Qwen3 Next 80B A3B Instruct

AlibabaQwen開源權重Apache 2.0 · 商用許可

描述

Qwen3-Next-80B-A3B-Instruct is the first in the Qwen3-Next series, featuring groundbreaking architectural innovations. It uses Hybrid Attention combining Gated DeltaNet and Gated Attention for efficient ultra-long context modeling, High-Sparsity MoE with 512 experts (10 activated + 1 shared) achieving extreme low activation ratio, and Multi-Token Prediction for improved performance and faster inference. With 80B total parameters and only 3B activated, it outperforms Qwen3-32B-Base with 10% training cost and 10x throughput for 32K+ contexts. The model performs on par with Qwen3-235B-A22B-Instruct-2507 while excelling at ultra-long-context tasks up to 256K tokens (extensible to 1M with YaRN). Architecture: 48 layers, 15T training tokens, hybrid layout of 12*(3*(Gated DeltaNet->MoE)->(Gated Attention->MoE)).

發布日期
2025-09-11
參數規模
80.0B
上下文長度
131K
支援模態
text

能力雷達圖

33
general
60
coding
68
reasoning
45
science
50
agents
0
multimodal

排行榜排名

領域#排名分數來源
智慧體能力模型榜66
48.0
LS
程式碼能力榜236
49.0
AA
通用能力榜328
39.0
AA
科學能力285
46.0
AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

Agents

BFCL-v370.3%自報

Biology

GPQANYU + Cohere + Anthropic (2023)72.9%自報

Chemistry

SuperGPQA58.8%自報

Code

Aider-Polyglot49.8%自報

Communication

WritingBench87.3%自報
Multi-IF75.8%自報
TAU-bench Retail60.9%自報
Tau2 Retail57.3%自報
Tau2 Airline45.5%自報
TAU-bench Airline44.0%自報
Tau2 Telecom13.2%自報

Creativity

Creative Writing v385.3%自報
Arena-Hard v282.7%自報

Finance

MMLU-Pro80.6%自報
MMLU-ProX76.7%自報

General

MMLU-Redux90.9%自報
MultiPL-E87.8%自報
IFEvalGoogle Research (2023)87.6%自報
Include78.9%自報
LiveBench 2024112575.8%自報
LiveCodeBench v656.6%自報

Math

AIME 202569.5%自報
HMMT2554.1%自報
PolyMATH45.9%自報

AA 評測指數

(Artificial Analysis)
Math Index(Artificial Analysis)
66.3
Intelligence Index(Artificial Analysis)
13.8
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.8
Gpqa(NYU + Cohere + Anthropic (2023))
0.7
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.7
Aime 25(MAA (Mathematical Association of America))
0.7
Lcr(Artificial Analysis)
0.5
Ifbench(Google Research (2023))
0.4
Scicode(UIUC + Argonne National Lab (2024))
0.3
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.2
Hle(Center for AI Safety + Scale AI (2025))
0.1
Terminalbench Hard(Stanford × Laude Institute (2026))
0.1

LLM Stats 分類評分

(LLM Stats (zeroeval))
Creativity
90
Writing
90
Legal
80
Structured Output
80
Instruction Following
80
Language
80
Math
70
Physics
70
Finance
70
General
70
Healthcare
70
Agents
70
Biology
70
Chemistry
70
Reasoning
60
Economics
60
Multimodal
50
Spatial Reasoning
50
Code
50
Communication
50
Tool Calling
50
Vision
50

定價

輸入價格$0.5 / 1M tokens
輸出價格$2 / 1M tokens
混合價格(3:1)$0.875 / 1M tokens

速度

Tokens/秒186.2
首Token延遲1.05s
首回答延遲1.05s

供應商價格排行

供應商價格排行

11 個供應商

最便宜: Kilo Gateway最貴: Alibaba
供應商輸入輸出
1Kilo Gateway最便宜
$0.0975
$0.78
2OpenRouter
$0.1
$1.1
3Charm Hyper
$0.1175
$1.136
4Helicone
$0.14
$1.4
5Alibaba (China)
$0.144
$0.574
6Merge Gateway
$0.144
$0.574
7Jiekou.AI
$0.15
$1.5
8NovitaAI
$0.15
$1.5
9LLM Gateway
$0.15
$1.2
10Neon
$0.15
$1.2
11Alibaba主要
$0.5
$2

比較該模型在不同 API 供應商之間的定價。

外部連結