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
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Specifications

Context window
262.1K
Max output
16.4K
Parameters
35B / 3B
Total / active
Avg output (7d)
1.8K tokens
Longer than 89% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

18.2

Better than 61% of models compared

Coding Index

41.9

Better than 48% of models compared

Agentic Index

13.1

Better than 46% 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 29% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

879 Elo

Better than 40% 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 66% of models compared

Reasoning

HLE

Humanity's Last Exam

22.2%

Better than 71% 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 17% 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 66% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

19.0%

Last updated Oct 1, 2026

Artificial Analysis

Providers

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