We are launching Ring-2.6-1T, a trillion-parameter flagship thinking model engineered for real-world complex tasks and production env: 🚀
- Adjustable Thinking Effort: dynamic compute mechanism to flexibly balance cognitive depth, token cost, and execution speed;
- Agent-Optimized: Built for high-frequency workflows, delivering rapid multi-step execution and tool orchestration with SOTA stability;
- Deep Thinking: Unlocks the model's maximum capability ceiling for rigorous mathematical logic and scientific research;
1/4 Not all tasks need equal compute
Format conversion differs vastly from math olympiads.🧑🔬
Ring 1T delivers lower token overhead and rapid multi-step execution, making it the ideal production default for tool orchestration, coding, and multi-turn interactions. Benchmarks🫶
2/4 Agentic "high-ness"
In real-world task execution benchmarks, Ring-2.6-1T 'high' demonstrates top-tier stability and API routing capabilities, suitable for general and coding agent use cases:
- PinchBench: 87.60 (outperforming GPT-5.4 xHigh & Gemini-3.1-Pro high) @kilocode
- ClawEval: 63.82
- Tau2-Bench Telecom: 95.32 📊
3/4 Intelligence "overload-xhigh"
Built to provide thought space needed for rigorous logical analysis,🧠'xhigh' unlocks our highest capability ceiling for math, research, and multi-path exploration, suitable for planning and reasoning heavy use cases:
- AIME 26: 95.83
- GPQA Diamond: 88.27
- ARC-AGI-V2: 77.78
reasoning model — A model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
Ring-2.6-1T offers a dial between reasoning depth and tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → cost across 'high' and 'xhigh' modes, so AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → builders can tune spend per task class instead of paying full reasoning overhead on trivial steps.
Read the full thread on X ↗
