Qwen3 30B A3B 2507 (Reasoning)
AlibabaQwen开源权重Apache 2.0 · 商用许可
描述
Qwen3-30B-A3B is a smaller Mixture-of-Experts (MoE) model from the Qwen3 series by Alibaba, with 30.5 billion total parameters and 3.3 billion activated parameters. Features hybrid thinking/non-thinking modes, support for 119 languages, and enhanced agent capabilities. It aims to outperform previous models like QwQ-32B while using significantly fewer activated parameters.
发布日期
2025-07-30
参数规模
30.5B
上下文长度
131K
支持模态
text
能力雷达图
31
general
35
coding
73
reasoning
45
science
70
agents
0
multimodal
排行榜排名
基准测试分数 (LLM Stats)
(LLM Stats (zeroeval))Chat
Multi-IF
72.2%自报
General
Arena Hard
91.0%自报
BFCL
69.1%自报
Math
AIME 2024
80.4%自报
LiveBench
74.3%自报
AIME 2025
70.9%自报
Reasoning
GPQANYU + Cohere + Anthropic (2023)
65.8%自报
LiveCodeBench
62.6%自报
AA 评测指数
(Artificial Analysis)Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))97.6
Aime(MAA (Mathematical Association of America))90.7
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))80.5
Gpqa(NYU + Cohere + Anthropic (2023))70.7
Livecodebench(UC Berkeley + MIT + Cornell (2024))70.7
Lcr(Artificial Analysis)61.3
Aime 25(MAA (Mathematical Association of America))56.3
Math Index(Artificial Analysis)56.3
Ifbench(Google Research (2023))50.7
Scicode(UIUC + Argonne National Lab (2024))33.0
Tau2(Sierra + U Toronto + Vector Institute (2025))28.1
Coding Index(Artificial Analysis)12.1
Hle(Center for AI Safety + Scale AI (2025))10.3
Intelligence Index(Artificial Analysis)9.8
Tau Banking5.4
Terminalbench Hard(Stanford × Laude Institute (2026))5.3
Terminalbench V2 11.5
LLM Stats 分类评分
(LLM Stats (zeroeval))Creativity90
Writing90
Chat80
Math80
Instruction Following70
Language70
Physics70
Reasoning70
Structured Output70
General70
Biology70
Chemistry70
Communication70
Tool Calling70
Code60
定价
输入价格$0.2 / 1M tokens
输出价格$2.4 / 1M tokens
混合价格(3:1)$0.75 / 1M tokens
速度
Tokens/秒0.0
首Token延迟0.00s
首回答延迟0.00s
供应商价格排行
供应商价格排行
4 个供应商
最便宜: Novita最贵: Alibaba
供应商输入输出
1Novita最便宜
$0
$0
2DeepInfra
$0
$0
3Fireworks
$0
$0
4Alibaba主要
$0.2
$2.4
比较该模型在不同 API 供应商之间的定价。