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Ling 3.0 Flash

inclusionai/ling-3.0-flash
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Ling 3.0 Flash

inclusionai/ling-3.0-flash
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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 weights

Context 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

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

24.9

Better than 78% of models compared

Coding Index

50.6

Better than 60% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

3.2%

Better than 27% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

796 Elo

Better than 39% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

5.4%

Better than 29% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

73.0%

Better than 72% of models compared

Reasoning

HLE

Humanity's Last Exam

23.7%

Better than 75% of models compared

CritPt

Research-level physics reasoning

1.7%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 18% of models compared

SciCode

Python programming for scientific computing

42.0%

Better than 34% 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 78% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

73.0%

Better than 72% of models compared

Last updated Sep 22, 2026

Artificial Analysis

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