Qwen3 30B A3B 2507 (Reasoning)
AlibabaQwen開源權重Apache 2.0 · 商用許可
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.
發布日期
2025-07-30
參數規模
30.5B
上下文長度
131K
支援模態
text
能力雷達圖
31
general
35
coding
73
reasoning
45
science
70
agents
0
multimodal
排行榜排名
基準測試分數 (LLM Stats)
(LLM Stats (zeroeval))Chat
Multi-IF
72.2%自報
General
Arena Hard
91.0%自報
BFCL
69.1%自報
Math
AIME 2024
80.4%自報
LiveBench
74.3%自報
AIME 2025
70.9%自報
Reasoning
GPQANYU + Cohere + Anthropic (2023)
65.8%自報
LiveCodeBench
62.6%自報
AA 評測指數
(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 分類評分
(LLM Stats (zeroeval))Creativity90
Writing90
Chat80
Math80
Instruction Following70
Language70
Physics70
Reasoning70
Structured Output70
General70
Biology70
Chemistry70
Communication70
Tool Calling70
Code60
定價
輸入價格$0.2 / 1M tokens
輸出價格$2.4 / 1M tokens
混合價格(3:1)$0.75 / 1M tokens
速度
Tokens/秒0.0
首Token延遲0.00s
首回答延遲0.00s
供應商價格排行
供應商價格排行
4 個供應商
最便宜: Novita最貴: Alibaba
供應商輸入輸出
1Novita最便宜
$0
$0
2DeepInfra
$0
$0
3Fireworks
$0
$0
4Alibaba主要
$0.2
$2.4
比較該模型在不同 API 供應商之間的定價。