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.

  • Reasoning
  • Vision
  • Video Input

Added Feb 16, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.39
Output
$2.34
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Specifications

Context window
258K
Max output
65.5K
Parameters
397B / 17B
Total / active
Avg output (7d)
3.5K tokens
Longer than 98% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

18.4

Better than 62% of models compared

Coding Index

48.2

Better than 57% of models compared

Agentic Index

8.3

Better than 44% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

6.9%

Better than 33% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

72.4%

Better than 16% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

561 Elo

Better than 26% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

780 Elo

Better than 37% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

12.2%

Better than 48% 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

29.0%

Better than 77% 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 16% of models compared

SciCode

Python programming for scientific computing

44.8%

Better than 38% 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 78% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

14.0%

Last updated Oct 1, 2026

Artificial Analysis

Providers

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