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
$0.20
Output
$2.24

Specifications

Context window
262.1K
Max output
65.5K
Parameters
27B
Avg output (7d)
1.1K tokens
Longer than 74% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

21.4

Better than 70% of models compared

Coding Index

53.7

Better than 63% of models compared

Agentic Index

18.5

Better than 53% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

11.0%

Better than 32% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

0.9%

Better than 31% of models compared

AA-Briefcase v1.1

Agentic knowledge work (Elo)

816 Elo

Better than 33% of models compared

GDPval-AA v2.1

Economically valuable tasks (Elo)

986 Elo

Better than 42% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

11.0%

Better than 44% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

77.3%

Better than 76% of models compared

MLCR-AA

Medical long-context reasoning

10.6%

Better than 36% of models compared

Reasoning

HLE

Humanity's Last Exam

23.1%

Better than 70% 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 32% 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 76% of models compared

Last updated Oct 22, 2026

Artificial Analysis

Providers

Choose explicit providers for this model. Auto routing remains available as the default option.

Loading provider options…