Ring-2.6-1T - Ant Ling's trillion-scale deep reasoning model

What is Ring-2.6-1T

Ring-2.6-1T is a trillion-parameter deep reasoning model launched by Ant Ling, belonging to the Ring series and focusing on slow-thinking capabilities for complex cognitive tasks such as math competitions and code generation. The model adopts a MoE mixture-of-experts architecture, maintaining low activation costs at a high parameter scale, and reaches open-source leading levels on multiple reasoning benchmarks. As a core member of the Ling model matrix, it is laid out in coordination with the Ling fast-thinking series and the Ming multimodal series, covering full-scenario needs from instant execution to deep reasoning.

Ring-2.6-1T's main features

• Deep mathematical reasoning: Performs excellently on high-difficulty math competition benchmarks such as AIME and IMO, supporting multi-step complex derivations and proofs.
• Advanced code generation: Handles complex algorithm implementation, long code-chain completion, program logic analysis, and bug diagnosis.
• Long-chain logical decision-making: Addresses professional scenarios requiring multi-step causal reasoning, such as financial risk control and compliance review.
• Ultra-long text deep understanding: Supports structured parsing and logical deduction of 256K-level long text.

Ring-2.6-1T's technical principles

• MoE sparse activation architecture: The model adopts a Mixture of Experts design, with total parameters reaching the trillion level, but only part of the expert networks are activated during inference. Through a gated routing mechanism, it dynamically selects the most relevant expert subset to process inputs, significantly reducing the computational overhead and VRAM usage of a single inference while maintaining the knowledge capacity of a large model.
• Specialized optimization for deep reasoning: Architecture-level tuning is performed for "slow thinking" scenarios, strengthening the generation stability of Chain-of-Thought. The model introduces large amounts of mathematical proofs, code logic, and long-chain reasoning data during the pretraining stage, and adopts reinforcement learning alignment techniques during the post-training stage to improve self-verification and error-correction capabilities.
• Long-context reasoning mechanism: Supports 256K-level ultra-long context, and through improved positional encoding and attention mechanism optimization, achieves global logical association and deep deduction for long documents and long codebases, avoiding information forgetting and logical breaks in long-text scenarios.
• Collaborative architecture with the Ling series: As the reasoning flagship of the Ling model matrix, Ring-2.6-1T complements the Ling fast-thinking series (which adopts a hybrid MLA + Linear Attention architecture). Ring focuses on the accuracy of deep reasoning, while Ling is responsible for the response speed of efficient execution. The two share the underlying vocabulary and some basic capabilities, facilitating unified scheduling at the upper layer.
• Post-training alignment and safety: After large-scale instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF), it aligns with safety and usefulness goals while maintaining reasoning capabilities, ensuring the controllability and reliability of output results for complex tasks.

How to use Ring-2.6-1T

• Online experience: Through the OpenRouter platform https://openrouter.ai/inclusionai/ring-2.6-1t:free，免费调用官方免费 API, you can quickly test the model's reasoning capabilities without local deployment.