Ling 3.0 Flash
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.
- Tool Calling
Added Jul 23, 2026
Model weightsPricing
Auto routing · per 1M tokens- Input
- $0.075
- Output
- $0.22
- Cache read
- $0.015
Specifications
- Context window
- 262.1K
- Max output
- 32.8K
- Avg output (7d)
- 1.5K tokens
- Longer than 83% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
20.1
Coding Index
50.6
Agentic Index
19.3
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
3.2%
Better than 24% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
797 Elo
Better than 35% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
948 Elo
Better than 42% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
5.4%
Better than 27% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
73.0%
Better than 68% of models compared
Reasoning
HLE
Humanity's Last Exam
23.7%
Better than 71% of models compared
CritPt
Research-level physics reasoning
1.7%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 16% of models compared
SciCode
Python programming for scientific computing
42.0%
Better than 30% 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%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
85.5%
Better than 79% 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
22.4%
Last updated Oct 12, 2026
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