Ling-3.0-flash is a 124B-parameter Mixture-of-Experts model with approximately 5.1B parameters active per token. It prioritizes token efficiency and production-scale agentic inference, helping coding and tool-using agents complete more work within constrained latency and serving budgets.
Added Jul 23, 2026
Model weightsContext Window
262.1K
Max Output
32.8K
Input Price (Auto)
$0.075/1M
Output Price (Auto)
$0.22/1M
Cache Read (Auto)
$0.015/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
37.8
Coding Index
50.6
Agentic Index
29.3
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
85.5%
Better than 82% of models compared
HLE
Humanity's Last Exam
23.7%
Better than 79% of models compared
AA-LCR
Long context reasoning evaluation
67.0%
Better than 73% of models compared
GDPval-AA
Economically valuable tasks
30.3%
CritPt
Research-level physics reasoning
1.7%
Coding
SciCode
Python programming for scientific computing
41.1%
Better than 75% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
18.2%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
44.1%
Last updated Aug 18, 2026
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