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Qwen3.7 Plus Thinking

qwen/qwen3.7-plus:thinking
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Qwen3.7 Plus Thinking

qwen/qwen3.7-plus:thinking
Back

Qwen3.7 Plus with thinking mode enabled for deeper multimodal reasoning, coding, tool use, screen reading, and productivity workflows.

Added Jun 1, 2026

Context Window

983.6K

Max Output

65.5K

Avg output tokens (7d)

1.5K tokens

85%

Input Price (Auto)

$0.40/1M

Output Price (Auto)

$1.60/1M

Cache Read (Auto)

$0.080/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

25.2

Better than 76% of models compared

Coding Index

55.9

Better than 67% of models compared

Agentic Index

17.5

Better than 55% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

17.4%

Better than 39% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

81.8%

Better than 25% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

912 Elo

Better than 42% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

756 Elo

Better than 36% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

12.2%

Better than 49% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

73.0%

Better than 69% of models compared

MLCR-AA

Medical long-context reasoning

9.4%

Better than 29% of models compared

Reasoning

HLE

Humanity's Last Exam

35.6%

Better than 83% of models compared

IFBench

Instruction-following benchmark

78.0%

Better than 97% of models compared

CritPt

Research-level physics reasoning

9.1%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

1.0%

Better than 42% of models compared

SciCode

Python programming for scientific computing

46.1%

Better than 42% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

22.5%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

27.7%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

90.0%

Better than 89% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

47.0%

Better than 94% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

93.0%

Better than 89% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

73.0%

Better than 69% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

12.8%

Last updated Oct 2, 2026

Artificial Analysis

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