Ring-1T

Ring-1T is a trillion-parameter thinking model released by the InclusionAI Bailing team, built on the Ling 2.0 architecture and trained from the Ling-1T-base foundation model. It uses a Mixture-of-Experts design that activates roughly 50 billion parameters from a one-trillion-parameter pool during inference, giving it the depth of a frontier-scale model while keeping per-query compute more manageable. Training combined large-scale verifiable-reward reinforcement learning with a self-developed stabilization method called icepop, and the model was post-trained within the ASystem reinforcement learning framework to sharpen deep reasoning and natural language inference.

The intended use case for Ring-1T is heavyweight analytical work where chain-of-thought style reasoning matters more than raw chat throughput, such as competition mathematics, proof generation, code synthesis, and complex logical inference. It is described as achieving leading open-source performance on demanding benchmarks including IMO 2025 and ICPC World Finals 2025, signaling a focus on verifiable, multi-step problem solving rather than lightweight assistant tasks. For practitioners, this profile fits best when the workload benefits from long-context analysis and methodical reasoning rather than low-latency response.