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Qwen3 Next 80B A3B Instruct

AlibabaQwen오픈 웨이트Apache 2.0 · 상업적 사용 가능

설명

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)).

출시일
2025-09-11
파라미터
80.0B
컨텍스트 길이
131K
모달리티
text

능력 레이더

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

랭킹

도메인#순위점수소스
에이전트형 역량64
48.0
LS
코딩 랭킹225
49.0
AA
종합 랭킹316
39.0
AA
과학275
46.0
AA

벤치마크 점수 (LLM Stats)

(LLM Stats (zeroeval))

Agents

BFCL-v370.3%자체 보고

Biology

GPQANYU + Cohere + Anthropic (2023)72.9%자체 보고

Chemistry

SuperGPQA58.8%자체 보고

Code

Aider-Polyglot49.8%자체 보고

Communication

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

Creativity

Creative Writing v385.3%자체 보고
Arena-Hard v282.7%자체 보고

Finance

MMLU-Pro80.6%자체 보고
MMLU-ProX76.7%자체 보고

General

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

Math

AIME 202569.5%자체 보고
HMMT2554.1%자체 보고
PolyMATH45.9%자체 보고

AA 평가 지수

(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 카테고리 점수

(LLM Stats (zeroeval))
Creativity
90
Writing
90
Instruction Following
80
Language
80
Legal
80
Structured Output
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

가격

입력 가격$0.5 / 1M 토큰
출력 가격$2 / 1M 토큰
혼합 가격 (3:1)$0.875 / 1M 토큰

속도

토큰/초186.3
첫 토큰 지연1.02s
첫 응답 지연1.02s

공급자 가격 순위

공급자 가격 순위

11개 공급자

최저가: OpenRouter최고가: Alibaba
공급자입력출력
1OpenRouter최저가
$0.09
$1.1
2Kilo Gateway
$0.0975
$0.78
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
11Alibaba주요
$0.5
$2

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