GLM-4.6V scales its context window to 128k tokens in training, and achieves SoTA performance in visual understanding among models of similar parameter scales. Integrates native Function Calling capabilities, bridging 'visual perception' and 'executable action' for multimodal agents. Quantized at FP8.
Added Dec 11, 2025
Context Window
128.0K
Max Output
24.0K
Input Price (Auto)
$0.30/1M
Output Price (Auto)
$0.90/1M
Capabilities
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
17.1
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Coding Index
11.1
GPQA Diamond
Graduate-level scientific reasoning
56.6%
Better than 41% of models compared
HLE
Humanity's Last Exam
3.7%
Better than 8% of models compared
IFBench
Instruction-following benchmark
27.9%
Better than 14% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
30.7%
Better than 45% of models compared
AA-LCR
Long context reasoning evaluation
12.3%
Better than 28% of models compared
SciCode
Python programming for scientific computing
27.2%
Better than 43% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
3.0%
Better than 26% of models compared
AIME 2025
American Invitational Mathematics Examination 2025
26.3%
Better than 28% of models compared
MMLU-Pro
Professional and academic subject knowledge
75.2%
Better than 50% of models compared
Last updated May 15, 2026
Artificial AnalysisLiveCodeBench
Contamination-free coding benchmark
41.1%
Better than 49% of models compared
