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Qwen3 Next 80B A3B Instruct

AlibabaQwenOpen WeightApache 2.0 · Commercial OK

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
262K
모달리티
text

능력 레이더

37
general
35
coding
68
reasoning
45
science추정
50
agents
0
multimodal

전용 과학 벤치마크가 없을 때 Science는 추론 프록시를 사용하여 추정합니다.

랭킹

도메인#순위점수소스
Agents & Tools15
70.0
LS
Code Ranking193
42.0
AA
General Ranking246
42.0
AA
Math Reasoning130
67.0
AA
Science204
47.0
AA

벤치마크 점수 (LLM Stats)

Agents

BFCL-v370.3%자체 보고

Biology

GPQA72.9%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
IFEval87.6%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

Math Index
66.3
Intelligence Index
20.1
Coding Index
15.3
Mmlu Pro
0.8
Gpqa
0.7
Livecodebench
0.7
Aime 25
0.7
Lcr
0.5
Ifbench
0.4
Scicode
0.3
Tau2
0.2
Terminalbench Hard
0.1
Hle
0.1

LLM Stats 카테고리 점수

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

가격

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

속도

토큰/초159.3 tokens/s
첫 토큰 지연1.09s
첫 응답 지연1.09s

사용 가능한 프로바이더

(LS 내부 단위)
프로바이더입력 가격출력 가격
Novita150K1.5M

외부 링크