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Qwen3.5 397B A17B Thinking

qwen/qwen3.5-397b-a17b:thinking
Provider logo

Qwen3.5 397B A17B Thinking

qwen/qwen3.5-397b-a17b:thinking

Qwen 3.5's open-source 397B MoE model (17B active params) with hybrid linear attention and extended reasoning. Supports text, image, and video input with a 256K context window.

Added Feb 16, 2026

Model weights

Context Window

258.0K

Max Output

65.5K

Avg output tokens (7d)

3.6K tokens

98%

Input Price (Auto)

$0.39/1M

Output Price (Auto)

$2.34/1M

Cache Read (Auto)

$0.20/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

19.1

Better than 67% of models compared

Coding Index

48.2

Better than 56% of models compared

Agentic Index

10.6

Better than 45% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

6.9%

Better than 38% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

563 Elo

Better than 29% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

905 Elo

Better than 43% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

12.2%

Better than 54% 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

29.0%

Better than 80% of models compared

IFBench

Instruction-following benchmark

78.8%

Better than 97% of models compared

CritPt

Research-level physics reasoning

1.7%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 18% of models compared

SciCode

Python programming for scientific computing

44.8%

Better than 41% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

30.8%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

88.9%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

89.3%

Better than 87% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

40.9%

Better than 87% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

95.6%

Better than 95% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

77.3%

Better than 81% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

20.3%

Last updated Sep 11, 2026

Artificial Analysis

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