Ring 2.6 1T

Ring-2.6-1T is a 1T-parameter thinking model with 63B active parameters from inclusionAI, built for real-world agent workflows that require both strong capability and operational efficiency. It is optimized for coding agents, tool use, and long-horizon task execution, delivering leading results on benchmarks including PinchBench, ClawEval, TAU2-Bench, and GAIA2-search. With adaptive reasoning effort across high and xhigh modes, the model dynamically allocates reasoning budget based on task complexity, enabling stronger performance with lower token overhead in tool-heavy and multi-turn agent workflows.

Ling 2.6 1T

Ling-2.6-1T is an instant (instruct) model from inclusionAI and the company's trillion-parameter flagship, designed for real-world agents that require fast execution and high efficiency at scale. It uses a 'fast thinking' approach to reduce costs to roughly a quarter of comparable models while maintaining top-tier performance. The model achieves state-of-the-art results on benchmarks such as AIME26 and SWE-bench Verified, and is well suited for advanced coding, complex reasoning, and large-scale agent workflows where both capability and efficiency are critical.

Ring 2.6 1T

Ring-2.6-1T is a 1T-parameter thinking model with 63B active parameters from inclusionAI, built for real-world agent workflows that require both strong capability and operational efficiency. It is optimized for coding agents, tool use, and long-horizon task execution, delivering leading results on benchmarks including PinchBench, ClawEval, TAU2-Bench, and GAIA2-search. With adaptive reasoning effort across high and xhigh modes, the model dynamically allocates reasoning budget based on task complexity, enabling stronger performance with lower token overhead in tool-heavy and multi-turn agent workflows.

Ling 2.6 1T

Ling-2.6-1T is an instant (instruct) model from inclusionAI and the company's trillion-parameter flagship, designed for real-world agents that require fast execution and high efficiency at scale. It uses a 'fast thinking' approach to reduce costs to roughly a quarter of comparable models while maintaining top-tier performance. The model achieves state-of-the-art results on benchmarks such as AIME26 and SWE-bench Verified, and is well suited for advanced coding, complex reasoning, and large-scale agent workflows where both capability and efficiency are critical.