Qwen3 Vision‑Language model (235B MoE, ≈22B active) tuned for instruction following and grounded visual QA. Excels at image understanding, dense OCR, charts/diagrams, and multi‑image context.
Context Window
128.0K
Max Output
262.1K
Avg output tokens (7d)
217 tokens
Input Price (Auto)
$0.30/1M
Output Price (Auto)
$1.20/1M
Cache Read (Auto)
$0.15/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
9.9
Agentic work
T²-Bench Telecom (legacy)
Legacy fallback · Conversational AI agents in dual-control scenarios
35.1%
Better than 43% of models compared
Document reasoning
AA-LCR v1.1
Long context reasoning with updated grading
32.7%
Better than 34% of models compared
Reasoning
HLE
Humanity's Last Exam
6.6%
Better than 43% of models compared
IFBench
Instruction-following benchmark
42.7%
Better than 45% of models compared
CritPt
Research-level physics reasoning
0.0%
Coding
Terminal-Bench Hard (legacy)
Legacy fallback · Agentic coding and terminal use
6.8%
Better than 37% of models compared
LiveCodeBench
Contamination-free coding benchmark
59.4%
Better than 66% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
70.7%
Better than 65% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
82.3%
Better than 80% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
18.6%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
87.1%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
71.2%
Better than 50% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
32.7%
Better than 34% of models compared
Last updated Sep 11, 2026
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