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Ant Bailing Unveils Ring-2.6-1T Trillion-Level Thinking Model with Configurable Inference Intensity
Ant Ling has officially unveiled its trillion-scale flagship reasoning model, Ring-2.6-1T. Built for demanding production settings like Agent workflows, engineering development, and scientific analysis, the model features a configurable Reasoning Effort mechanism at its core. This mechanism is designed to decouple reasoning capability from fixed resource usage, addressing the challenge of balancing reasoning costs with execution efficiency in practical applications.
Ring-2.6-1T provides two reasoning intensity modes: high and xhigh. The high mode is tailored for frequent Agent collaboration, with low token consumption and rapid multi-step execution, making it ideal for multi-turn interactions and task decomposition. The xhigh mode is built for extreme use cases like math competitions and complex logical reasoning, offering deeper thinking space.
In practical task evaluations, the high mode scored 87.60 on PinchBench, outperforming rivals like GPT-5.4xHigh and Claude-Opus-4.7xhigh. For high-difficulty reasoning, the xhigh mode achieved 95.83 on AIME26 and 88.27 on GPQA Diamond, demonstrating strong scientific understanding capabilities.
This launch signals a shift in large model competition from a pure focus on parameter scale to refined strategies centered on "reasoning efficiency." By providing adjustable thinking depth, Ring-2.6-1T gives developers more flexible cost-control options, facilitating the routine deployment of AI agents in enterprise workflows. The model is currently available for a free one-week trial on OpenRouter, with plans for an official open-source release soon, which is expected to further diversify the open-source landscape for trillion-scale reasoning models.
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Ant Ling has officially unveiled its trillion-scale flagship reasoning model, Ring-2.6-1T. Built for demanding production settings like Agent workflows, engineering development, and scientific analysis, the model features a configurable Reasoning Effort mechanism at its core. This mechanism is designed to decouple reasoning capability from fixed resource usage, addressing the challenge of balancing reasoning costs with execution efficiency in practical applications.
Ring-2.6-1T provides two reasoning intensity modes: high and xhigh. The high mode is tailored for frequent Agent collaboration, with low token consumption and rapid multi-step execution, making it ideal for multi-turn interactions and task decomposition. The xhigh mode is built for extreme use cases like math competitions and complex logical reasoning, offering deeper thinking space.
In practical task evaluations, the high mode scored 87.60 on PinchBench, outperforming rivals like GPT-5.4xHigh and Claude-Opus-4.7xhigh. For high-difficulty reasoning, the xhigh mode achieved 95.83 on AIME26 and 88.27 on GPQA Diamond, demonstrating strong scientific understanding capabilities.
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