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 weightsPricing
Auto routing · per 1M tokens- Input
- $0.39
- Output
- $2.34
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
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
18.4
Coding Index
48.2
Agentic Index
8.3
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
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