🚀 Ring-2.6-1T is now open source.
A trillion-scale flagship thinking model built for real-world complex tasks: Agent workflows, coding & engineering, long-horizon tasks, complex reasoning, research, and enterprise automation.
It is designed to move beyond “answering” toward execution: understanding context, planning steps, calling tools, and staying stable across long task chains.
Highlights：
- Advanced agentic workflow support.
- Reasoning effort levels: high for agentic tasks, xhigh for complex reasoning.
- Scalable asynchronous RL via the IcePop algorithm, enabling stable, trillion-scale training for long-horizon agentic RL.
1/ Ring-2.6-1T shows strong performance across both Agent execution and hard reasoning.
Agent / workflow benchmarks with high:
• PinchBench: 87.60
• ClawEval: 63.82
• Tau2-Bench Telecom: 95.32
• Gaia2-search: 75.40
• SWE-Bench Verified: 74.00
Hard reasoning benchmarks with xhigh:
• AIME 26: 95.83
• GPQA Diamond: 88.27
• ARC-AGI-V2 Pass@2: 66.18
high is built for efficient production Agent workflows.
xhigh is built for deeper reasoning when the task needs more thinking.
Fast when needed. Deep when necessary.
2/ Demo: Code Generation
In Pi Coding Agent, we asked Ring-2.6-1T to search the web for different web design aesthetics and generate a large set of interactive guides in those styles.
This demo tests its tool use, task planning, visual translation, and front-end code generation capabilities.
3/ Demo: Bug Fixing in Productive Code Repo
In OpenCode, we used Ring-2.6-1T inside a real project repository to locate and fix a series of style-adaptation bugs, then generate the related documentation.
This demo tests its ability to explore a codebase, analyze issues, and solve problems in real engineering projects.
reasoning model — A model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.
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.
SWE-bench — The standard benchmark for AI coding agents: real GitHub issues from real repositories, scored by whether the agent's patch passes the project's own tests.
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.
Why it matters
An open trillion-scale reasoning modelA model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.Full definition → exposes a per-request effort switch, letting you spend fewer 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 → on production 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 → loops and reserve deep reasoning for the hard tasks.
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