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
Avg output tokens (7d)
1.7K tokens
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
27.4
Coding Index
50.6
Agentic Index
21.1
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
85.5%
Better than 79% of models compared
HLE
Humanity's Last Exam
23.7%
Better than 76% of models compared
AA-LCR
Long context reasoning evaluation
73.0%
Better than 73% of models compared
GDPval-AA
Economically valuable tasks
27.0%
CritPt
Research-level physics reasoning
1.7%
Coding
SciCode
Python programming for scientific computing
42.0%
Better than 26% 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 Sep 7, 2026
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