Qwen3.6 35B A3B Thinking

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).

  • Reasoning
  • Vision
  • Video Input

Added Apr 19, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.11
Output
$0.80

Specifications

Context window
262.1K
Max output
16.4K
Parameters
35B / 3B
Total / active
Avg output (7d)
2.5K tokens
Longer than 95% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

18.2

Better than 64% of models compared

Coding Index

41.9

Better than 46% of models compared

Agentic Index

13.1

Better than 42% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

5.2%

Better than 27% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

0.5%

Better than 27% of models compared

AA-Briefcase v1.1

Agentic knowledge work (Elo)

713 Elo

Better than 27% of models compared

GDPval-AA v2.1

Economically valuable tasks (Elo)

892 Elo

Better than 38% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

6.6%

Better than 33% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

71.7%

Better than 65% of models compared

MLCR-AA

Medical long-context reasoning

5.0%

Better than 13% of models compared

Reasoning

HLE

Humanity's Last Exam

22.2%

Better than 69% 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 16% 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 65% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

19.6%

Last updated Oct 22, 2026

Artificial Analysis

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