Ring-2.6-1T - Ant Financial's trillion-level deep inference model

What is Ring-2.6-1T?

Ring-2.6-1T is a trillion-parameter deep inference model launched by Ant Financial's Bailix series. It focuses on slow-thinking capabilities for complex cognitive tasks such as math competitions and code generation. The model employs a MoE hybrid expert architecture, maintaining low activation costs even with high parameter scales, and achieving leading open-source performance on multiple inference benchmarks. As a core member of Bailix's model matrix, it collaborates with the Ling fast-thinking series and the Ming multimodal series to cover all scenarios from immediate execution to deep inference.

Main functions of Ring-2.6-1T

• Deep Mathematical ReasoningIt performs exceptionally well in high-difficulty mathematical competition benchmarks such as AIME and IMO, and supports complex multi-step derivations and proofs.
• Advanced code generationHandling complex algorithm implementation, long code chain completion, program logic analysis, and bug diagnosis.
• Long-chain logical decision-makingIt is suitable for professional scenarios that require multi-step causal reasoning, such as financial risk control and compliance review.
• Deep understanding of ultra-long textsSupports structured parsing and logical deduction of long texts up to 256K in size.

Technical Principles of Ring-2.6-1T

• MoE Sparse Activation ArchitectureThe model employs a Mixture of Experts design with a total number of trillions of parameters, but only a subset of expert networks are activated during inference. A gating routing mechanism dynamically selects the most relevant subset of experts to process the input, significantly reducing the computational overhead and memory usage per inference while maintaining the large knowledge capacity of the model.
• Deep Reasoning Special OptimizationFor "slow thinking" scenarios, the architecture is optimized to enhance the stability of chain-of-thought generation. The model incorporates a large amount of mathematical proofs, code logic, and long-chain reasoning data during the pre-training phase, and employs reinforcement learning alignment techniques during the post-training phase to improve self-verification and error correction capabilities.
• Long context reasoning mechanismIt supports ultra-long contexts of up to 256K and optimizes them through improved positional encoding and attention mechanisms to achieve global logical association and in-depth deduction of long documents and code libraries, avoiding information forgetting and logical breaks in long text scenarios.
• Collaborative architecture with the Ling seriesAs the flagship inference module of the Bailing model matrix, the Ring-2.6-1T complements the Ling Quick Thinking series (which adopts a hybrid architecture of MLA + Linear Attention). Ring focuses on the accuracy of deep inference, while Ling is responsible for the responsiveness of efficient execution. The two share the underlying vocabulary and some basic capabilities, which facilitates unified scheduling at the upper layer.
• Post-training alignment and safetyThrough large-scale instruction fine-tuning and human feedback reinforcement learning (RLHF), the goal of safety and usefulness is aligned while maintaining reasoning ability, ensuring the controllability and reliability of the output results of complex tasks.

How to use Ring-2.6-1T

• Online experience: By using the OpenRouter platform https://openrouter.ai/inclusionai/ring-2.6-1t:free, you can call the official free API for free and quickly test the model's inference capabilities without local deployment.