Recently, Ant Ling announced the official release of its trillion-scale flagship reasoning model, Ring-2.6-1T. Designed for complex production environments such as Agent workflows, engineering development, and scientific analysis, the model introduces a configurable Reasoning Effort mechanism at its core. This aims to break the fixed ratio between large model reasoning capabilities and resource consumption, solving the challenge of balancing reasoning costs and execution efficiency in real tasks.
Ring-2.6-1T offers two reasoning intensity modes: high and xhigh. The high mode is optimized for frequent Agent collaboration, featuring low Token cost and fast multi-step execution, suitable for multi-turn interactions and task decomposition; the xhigh mode is designed for extreme tasks such as math competitions and complex logical exploration, providing more comprehensive thinking space.
In real-task evaluations, the high mode achieved 87.60 points on PinchBench, surpassing competitors such as GPT-5.4xHigh and Claude-Opus-4.7xhigh; while in high-difficulty reasoning, the xhigh mode scored 95.83 on AIME26 and 88.27 on GPQA Diamond, demonstrating solid scientific understanding capabilities.
This release marks a shift in the competition of large models from purely focusing on parameter scale to refined operations around "reasoning efficiency." By offering open and adjustable thinking depth, Ring-2.6-1T provides developers with more flexible cost control tools, helping promote the routine deployment of AI agents in enterprise-level workflows. Currently, the model is available for a free one-week trial on OpenRouter, and it is planned to be officially open-sourced soon, which is expected to further enrich the landscape of trillion-scale reasoning models in the open-source ecosystem.
