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Qwen3.5 35B A3B

qwen3.5-35b-a3b
BackTry Model
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Qwen3.5 35B A3B

qwen3.5-35b-a3b
BackTry Model

Qwen3.5 35B A3B is a native vision-language MoE model with hybrid attention designed for efficient inference and strong general performance.

Added Feb 24, 2026

Model weights

Context Window

260.1K

Max Output

65.5K

Input Price (Auto)

$0.23/1M

Output Price (Auto)

$1.80/1M

Cache Read (Auto)

$0.11/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

24.3

Better than 62% of models compared

Coding Index

37.0

Better than 48% of models compared

Agentic Index

11.8

Better than 37% of models compared

Reasoning

GPQA Diamond

Graduate-level scientific reasoning

81.9%

Better than 73% of models compared

HLE

Humanity's Last Exam

13.4%

Better than 66% of models compared

IFBench

Instruction-following benchmark

44.5%

Better than 50% of models compared

T²-Bench Telecom

Conversational AI agents in dual-control scenarios

86.3%

Better than 79% of models compared

AA-LCR

Long context reasoning evaluation

60.3%

Better than 63% of models compared

GDPval-AA

Economically valuable tasks

14.8%

CritPt

Research-level physics reasoning

0.6%

Coding

SciCode

Python programming for scientific computing

29.3%

Better than 40% of models compared

Terminal-Bench Hard

Agentic coding and terminal use

10.6%

Better than 44% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

15.7%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

93.4%

Last updated Aug 11, 2026, 12:00 PM

Artificial Analysis

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