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Qwen3.6 27B Thinking

qwen/qwen3.6-27b:thinking
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Qwen3.6 27B Thinking

qwen/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.

Added Apr 23, 2026

Model weights

Context Window

260.1K

Max Output

65.5K

Input Price (Auto)

$0.30/1M

Output Price (Auto)

$2.00/1M

Cache Read (Auto)

$0.030/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

21.4

Better than 70% of models compared

Coding Index

53.7

Better than 64% 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 39% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

82.3%

Better than 37% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

812 Elo

Better than 40% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

973 Elo

Better than 51% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

11.0%

Better than 47% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

77.3%

Better than 81% of models compared

Reasoning

HLE

Humanity's Last Exam

23.1%

Better than 75% 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 18% of models compared

SciCode

Python programming for scientific computing

42.8%

Better than 37% 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 81% of models compared

Last updated Sep 20, 2026

Artificial Analysis

Providers

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