Qwen3.6 27B Thinking

Qwen3.6 27B is a native vision-language dense model with stronger agentic coding and STEM reasoning than Qwen 3.5 27B. It also improves spatial intelligence (including object localization/detection), plus video understanding, document OCR, and visual-agent workflows.

  • Reasoning
  • Vision
  • Video Input

Added Apr 23, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.30
Output
$2.00
Cache read
$0.030
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Specifications

Context window
260.1K
Max output
65.5K
Parameters
27B
Avg output (7d)
675 tokens
Longer than 54% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

21.4

Better than 68% of models compared

Coding Index

53.7

Better than 65% of models compared

Agentic Index

18.5

Better than 57% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

11.0%

Better than 34% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

82.3%

Better than 29% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

813 Elo

Better than 35% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

973 Elo

Better than 45% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

11.0%

Better than 43% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

77.3%

Better than 78% of models compared

Reasoning

HLE

Humanity's Last Exam

23.1%

Better than 71% of models compared

IFBench

Instruction-following benchmark

67.6%

Better than 81% of models compared

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

42.8%

Better than 33% of models compared

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

84.2%

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

94.2%

Better than 92% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

77.3%

Better than 78% of models compared

Last updated Oct 1, 2026

Artificial Analysis

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