Qwen3.5 27B is a native vision-language dense model optimized for fast responses while balancing quality and inference speed. Running inside a TEE (Trusted Execution Environment), with provider attestation support.
Added Mar 13, 2026
Model weightsContext Window
262.1K
Max Output
65.5K
Input Price (Auto)
$0.30/1M
Output Price (Auto)
$2.40/1M
Cache Read (Auto)
$0.15/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
22.9
Agentic work
T²-Bench Telecom (legacy)
Legacy fallback · Conversational AI agents in dual-control scenarios
93.9%
Better than 91% of models compared
Document reasoning
AA-LCR v1.1
Long context reasoning with updated grading
77.7%
Better than 82% of models compared
Reasoning
HLE
Humanity's Last Exam
23.9%
Better than 76% of models compared
IFBench
Instruction-following benchmark
75.6%
Better than 93% of models compared
CritPt
Research-level physics reasoning
0.3%
Coding
Terminal-Bench Hard (legacy)
Legacy fallback · Agentic coding and terminal use
32.6%
Better than 76% 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
75.1%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
85.8%
Better than 80% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
77.7%
Better than 82% of models compared
Last updated Sep 15, 2026
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