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Qwen3.6 35B A3B Thinking

qwen/qwen3.6-35b-a3b:thinking
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Qwen3.6 35B A3B Thinking

qwen/qwen3.6-35b-a3b:thinking
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Qwen3.6 35B A3B is a native vision-language MoE model with hybrid attention. Compared to Qwen3.5 35B A3B, Alibaba reports stronger agentic coding, mathematical and code reasoning, and better spatial understanding (including object localization and detection).

Added Apr 19, 2026

Model weights

Context Window

262.1K

Max Output

16.4K

Avg output tokens (7d)

2K tokens

91%

Input Price (Auto)

$0.11/1M

Output Price (Auto)

$0.80/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

18.2

Better than 62% of models compared

Coding Index

41.9

Better than 48% of models compared

Agentic Index

13.1

Better than 47% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

5.2%

Better than 29% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

711 Elo

Better than 30% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

879 Elo

Better than 43% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

6.6%

Better than 32% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

71.7%

Better than 67% of models compared

Reasoning

HLE

Humanity's Last Exam

22.2%

Better than 72% of models compared

IFBench

Instruction-following benchmark

64.4%

Better than 75% of models compared

CritPt

Research-level physics reasoning

0.3%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

36.6%

Better than 18% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

18.8%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

50.5%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

84.1%

Better than 75% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

34.8%

Better than 80% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

95.3%

Better than 94% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

71.7%

Better than 67% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

19.0%

Last updated Sep 29, 2026

Artificial Analysis

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