Ling-2.6-flash is an instant (instruct) model from inclusionAI with 104B total parameters and 7.4B active parameters, designed for real-world agents that require fast responses, strong execution, and high token efficiency. It delivers performance comparable to state-of-the-art models at a similar scale while significantly reducing token usage across coding, document processing, and lightweight agent workflows.
Added Apr 21, 2026
Context Window
262.1K
Max Output
32.8K
Input Price (Auto)
$0.080/1M
Output Price (Auto)
$0.24/1M
Capabilities
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
26.2
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Coding Index
23.2
Agentic Index
38.1
GPQA Diamond
Graduate-level scientific reasoning
59.3%
Better than 43% of models compared
HLE
Humanity's Last Exam
6.2%
Better than 52% of models compared
IFBench
Instruction-following benchmark
57.4%
Better than 75% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
86.0%
Better than 80% of models compared
AA-LCR
Long context reasoning evaluation
25.0%
Better than 44% of models compared
GDPval-AA
Economically valuable tasks
14.2%
Better than 54% of models compared
CritPt
Research-level physics reasoning
0.0%
Better than 30% of models compared
SciCode
Python programming for scientific computing
27.1%
Better than 40% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
21.2%
Better than 66% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
15.4%
Better than 33% of models compared
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
95.8%
Better than 4% of models compared
Last updated May 11, 2026
Artificial Analysis
