DataLearner logoDataLearnerAI
Latest AI Insights
Model Leaderboards
Benchmarks
Model Directory
Model Comparison
Resource Center
Tools
LanguageEnglish
DataLearner logoDataLearner AI

A knowledge platform focused on LLM benchmarking, datasets, and practical instruction with continuously updated capability maps.

Products

  • Leaderboards
  • Model comparison
  • Datasets

Resources

  • Tutorials
  • Editorial
  • Tool directory

Company

  • About
  • Privacy policy
  • Data methodology
  • Contact

© 2026 DataLearner AI. DataLearner curates industry data and case studies so researchers, enterprises, and developers can rely on trustworthy intelligence.

Privacy policyTerms of service
Page navigation
目录
Model catalogQwen3.6-35B-A3B
QW

Qwen3.6-35B-A3B

推理大模型

Qwen3.6-35B-A3B (MoE 架构, 35B 总参数, 3B 激活参数)

Release date: 2026-04-1651
Live demoGitHubHugging FaceCompare
Parameters
350.0亿
Context length
200K
Chinese support
Supported
Reasoning ability

Qwen3.6-35B-A3B (MoE 架构, 35B 总参数, 3B 激活参数) is an AI model published by 阿里巴巴, released on 2026-04-16, for 推理大模型, with 350.0B parameters, and 200K tokens context length, under the Apache 2.0 license.

Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology

Qwen3.6-35B-A3B

Model basics

Reasoning traces
Supported
Thinking modes
Thinking modes not supported
Context length
200K tokens
Max output length
80000 tokens
Model type
推理大模型
Release date
2026-04-16
Model file size
No data
MoE architecture
Yes
Total params / Active params
350.0B / 30B
Knowledge cutoff
No data
Qwen3.6-35B-A3B

Open source & experience

Code license
Apache 2.0
Weights license
Apache 2.0- 免费商用授权
GitHub repo
https://github.com/QwenLM/Qwen3
Hugging Face
https://huggingface.co/Qwen/Qwen3.6-35B-A3B
Live demo
https://chat.qwen.ai
Qwen3.6-35B-A3B

Official resources

Paper
Qwen3.6-35B-A3B:智能体编程利器,现已开源
DataLearnerAI blog
No blog post yet
Qwen3.6-35B-A3B

API details

API speed
4/5
No public API pricing yet.
Qwen3.6-35B-A3B

Benchmark Results

Qwen3.6-35B-A3B currently shows benchmark results led by GPQA (1 / 14, score 86), MMLU Pro (16 / 117, score 85.20), LiveCodeBench (20 / 109, score 80.40). This page also consolidates core specs, context limits, and API pricing so you can evaluate the model from benchmark results and deployment constraints together.

Thinking
All modesThinking
Thinking mode details (1)
All thinking modesDefault (On)

综合评估

4 evaluations
Benchmark / mode
Score
Rank/total
C-Eval
On
90
6 / 6
GPQA
On
86
1 / 14
MMLU Pro
On
85.20
16 / 117
HLE
On
21.40
75 / 131

编程与软件工程

4 evaluations
Benchmark / mode
Score
Rank/total
LiveCodeBench
On
80.40
20 / 109
SWE-bench Verified
On
73.40
31 / 96
SWE-bench Multilingual
On
67.20
7 / 9
SWE-Bench Pro - Public
On
49.50
17 / 26

AI Agent - 工具使用

2 evaluations
Benchmark / mode
Score
Rank/total
Terminal Bench 2.0
On
51.50
19 / 33
Tool Decathlon
On
26.90
6 / 6

数学推理

2 evaluations
Benchmark / mode
Score
Rank/total
AIME 2026
On
92.70
5 / 12
IMO-AnswerBench
On
78.90
9 / 9
View benchmark analysisCompare with other models
Qwen3.6-35B-A3B

Publisher

阿里巴巴
阿里巴巴
View publisher details
Qwen3.6-35B-A3B (MoE 架构, 35B 总参数, 3B 激活参数)

Model Overview

Qwen3.6-35B-A3B 是阿里云通义实验室于 2026 年 4 月 16 日发布并开源的新一代大型语言模型,属于 Qwen3.6 系列中的高效稀疏混合专家(MoE)模型[reference:2]。

模型概览与核心定位

Qwen3.6-35B-A3B 采用 MoE 架构,拥有 350 亿总参数,但在每次推理时仅需激活 30 亿参数[reference:3]。此设计使其在显著降低计算成本与推理延迟的同时,仍能保持强大的性能表现[reference:4]。该模型的核心定位是作为一款“智能体编程利器”,在智能体(Agentic)编程任务上表现卓越,大幅超越其前代 Qwen3.5-35B-A3B,并可与更大体量的稠密模型(如 Qwen3.5-27B、Gemma-31B)相媲美[reference:5]。作为一款原生多模态模型,它支持图文、文档分析及空间智能等多种任务,延续了“思考/非思考”双模式,是当时最具通用性的开源模型之一[reference:6]。

架构与技术规格

  • 模型参数:总参数量 350 亿(35B),激活参数量 30 亿(3B)[reference:7]。
  • 上下文窗口:原生支持 200K tokens 上下文,部分任务评测中使用了 256K 上下文配置[reference:8][reference:9]。
  • 架构特点:采用稀疏混合专家(MoE)架构,以较低的激活参数和计算成本实现高效推理[reference:10]。

核心能力与支持模态

  • 模态支持:原生支持多模态(图文、空间智能等)[reference:11]。
  • 能力详述:
    • 卓越的智能体编程能力:在 SWE-bench Verified (73.4)、SWE-bench Multilingual (67.2)、Terminal-Bench 2.0 (51.5) 等编程基准上表现突出,超越前代模型[reference:12]。
    • 强大的多模态感知与推理:在视觉语言任务中表现优异,RefCOCO 得分 92.0、ODInW13 得分 50.8,在多数基准上与 Claude Sonnet 4.5 持平甚至超越[reference:13]。
    • 混合思考能力:支持思考(Thinking)与非思考(Non-Thinking)两种模式,可根据任务复杂度灵活切换[reference:14]。

性能与基准评测

官方公布的部分基准测试得分如下:

  • 自然语言与编程:MMLU-Pro (85.2)[reference:15], GPQA (86.0)[reference:16], LiveCodeBench v6 (80.4)[reference:17], SkillsBench Avg5 (28.7)[reference:18]。
  • 多模态与视觉:MMMU (81.7)[reference:19], MMMU-Pro (75.3)[reference:20], Mathvista(mini) (86.4)[reference:21]。

应用场景与限制

  • 推荐用例:智能体编程、多模态内容理解与生成、空间智能任务、代码生成、需要复杂推理的任务[reference:22]。
  • 已知局限:官方在发布时未明确列出具体限制。模型虽支持长上下文,但最大输出长度约为 80K tokens[reference:23]。

访问方式与许可

  • 模型权重:完全开源,可通过 Hugging Face 和 ModelScope 下载[reference:24]。
  • 在线体验:可在 Qwen Studio 进行交互对话[reference:25]。
  • API 调用:即将通过阿里云百炼平台以 qwen3.6-flash 的名称提供 API 服务[reference:26]。
  • 许可证:Apache 2.0[reference:27]。

DataLearner 官方微信

欢迎关注 DataLearner 官方微信,获得最新 AI 技术推送

DataLearner 官方微信二维码