Qwen3.7 Plus
Qwen3.7 Plus is Alibaba's cost-effective Qwen 3.7 multimodal agent model for coding, tool use, productivity workflows, visual understanding, screen reading, and GUI interaction.
- Reasoning
- Vision
- Video Input
Added Jun 1, 2026
Pricing
Auto routing · per 1M tokens- Input
- $0.40
- Output
- $1.60
- Cache read
- $0.080
Specifications
- Context window
- 991.8K
- Max output
- 65.5K
- Avg output (7d)
- 114 tokens
- Longer than 9% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
25.2
Coding Index
55.9
Agentic Index
17.5
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
17.4%
Better than 39% of models compared
Harvey LAB-AA
Legal agentic work criterion pass rate
81.8%
Better than 25% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
915 Elo
Better than 42% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
770 Elo
Better than 36% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
12.2%
Better than 49% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
73.0%
Better than 68% of models compared
MLCR-AA
Medical long-context reasoning
9.4%
Better than 29% of models compared
Reasoning
HLE
Humanity's Last Exam
35.6%
Better than 83% of models compared
IFBench
Instruction-following benchmark
78.0%
Better than 97% of models compared
CritPt
Research-level physics reasoning
9.1%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
1.0%
Better than 42% of models compared
SciCode
Python programming for scientific computing
46.1%
Better than 40% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
22.5%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
27.7%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
90.0%
Better than 89% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
47.0%
Better than 94% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
93.0%
Better than 89% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
73.0%
Better than 68% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
13.3%
Last updated Oct 14, 2026
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