Qwen3.6 35B A3B (Reasoning)
AlibabaQwen开源权重Apache 2.0 · 商用许可
描述
Qwen3.6-35B-A3B is the first open-weight variant of the Qwen3.6 series, a multimodal Mixture-of-Experts model with 35B total parameters and 3B activated. It pairs a vision encoder with a hybrid 40-layer language model that interleaves Gated DeltaNet linear-attention blocks and Gated Attention blocks (10 × (3 × DeltaNet + 1 × Attention)) over 256 experts (8 routed + 1 shared, expert dim 512). The release prioritizes stability and real-world utility, with substantial gains in agentic coding (frontend workflows, repo-level reasoning) and a new option to preserve reasoning context across turns. Native context length is 262K tokens, extensible to ~1M via YaRN, and the model thinks by default.
发布日期
2026-04-16
参数规模
35.0B
上下文长度
262K
支持模态
audio, image, text, video
能力雷达图
19
general
41
coding
84
reasoning
55
science
50
agents
80
multimodal
排行榜排名
基准测试分数 (LLM Stats)
(LLM Stats (zeroeval))Agents
TAU3-Bench
67.2%自报
MCP-Mark
37.0%自报
VITA-Bench
35.6%自报
Toolathlon
26.9%自报
DeepPlanning
25.9%自报
Code
ZClawBench
52.6%自报
Claw-Eval
50.0%自报
NL2Repo
29.4%自报
SkillsBench
28.7%自报
General
C-Eval
90.0%自报
Language
MMLU-Redux
93.3%自报
MMLU-Pro
85.2%自报
Math
AIME 2026
92.7%自报
HMMT 2025
90.7%自报
HMMT25
89.1%自报
MathVista-Mini
86.4%自报
HMMT Feb 26
83.6%自报
IMO-AnswerBench
78.9%自报
Multimodal
VideoMMMU
83.7%自报
VideoMME w/o sub.
82.5%自报
MMMU
81.7%自报
Reasoning
GPQANYU + Cohere + Anthropic (2023)
86.0%自报
LiveCodeBench v6
80.4%自报
CharXiv-R
78.0%自报
SWE-Bench Verified
73.4%自报
SWE-bench Multilingual
67.2%自报
SuperGPQA
64.7%自报
MCP Atlas
62.8%自报
Terminal-Bench 2.0Stanford × Laude Institute (2026)
51.5%自报
SWE-Bench ProPrinceton NLP (2024)
49.5%自报
Humanity's Last Exam
21.4%自报
Search
WideSearch
60.1%自报
Video
MLVU
86.2%自报
Vision
MMBench-V1.1
92.8%自报
AI2D
92.7%自报
RefCOCO-avg
0.92 / 100自报
OmniDocBench 1.5
89.9%自报
VideoMME w sub.
86.6%自报
RealWorldQA
85.3%自报
EmbSpatialBench
0.84 / 100自报
CC-OCR
81.9%自报
MMMU-Pro
75.3%自报
MVBench
74.6%自报
LVBench
71.4%自报
Hallusion Bench
69.8%自报
RefSpatialBench
0.64 / 100自报
SimpleVQA
0.59 / 100自报
ODinW
50.8%自报
ZEROBench-Sub
0.34 / 100自报
AA 评测指数
(Artificial Analysis)Tau2(Sierra + U Toronto + Vector Institute (2025))95.3
Gpqa(NYU + Cohere + Anthropic (2023))84.1
Lcr(Artificial Analysis)71.7
Ifbench(Google Research (2023))64.4
Terminalbench V2 144.9
Coding Index(Artificial Analysis)41.9
Scicode(UIUC + Argonne National Lab (2024))36.6
Terminalbench Hard(Stanford × Laude Institute (2026))34.8
Hle(Center for AI Safety + Scale AI (2025))22.2
Intelligence Index(Artificial Analysis)18.2
Tau Banking9.3
Terminalbench V4 00.0
LLM Stats 分类评分
(LLM Stats (zeroeval))Language90
Structured Output90
Biology90
Long Context80
Math80
Multimodal80
Physics80
Spatial Reasoning80
Embodied80
Grounding80
Healthcare80
Chemistry80
Text-to-image80
Video80
Legal70
Reasoning70
Finance70
Frontend Development70
General70
Vision70
Image To Text60
Search60
Economics60
Code50
Tool Calling50
Agents40
定价
输入价格$0.375 / 1M tokens
输出价格$2.25 / 1M tokens
混合价格(3:1)$0.844 / 1M tokens
速度
Tokens/秒144.9
首Token延迟1.14s
首回答延迟38.38s
供应商价格排行
供应商价格排行
4 个供应商
最便宜: DeepInfra最贵: Alibaba
供应商输入输出
1DeepInfra最便宜
$0
$0
2EmpirioLabs AI
$0.07
$0.42
3Venice AI
$0.1
$1
4Alibaba主要
$0.375
$2.25
比较该模型在不同 API 供应商之间的定价。