Ant Ling
@AntLingAGI
MoE model series with foundation (Ling), reasoning (Ring) and any-to-any (Ming) from Ant Group’s AGI initiative, @TheInclusionAI.
6 Following 10.8K Followers
Thanks @AdinaYakup and the @huggingface community for the continued recognition!
We feel happy to bring another 1T thinking model to the community! Comments and feedbacks welcome!
Ant group just dropped Ring-2.6-1T 🔥 1T reasoning model, built for real world agent workflows.
✨ MIT license
✨ 128K >> 256K context (YaRN)
✨ Async RL + IcePop training architecture
✨ Dual reasoning effort: "high" for fast agent loops, "xhigh" for deep reasoning = Better cost/performance tradeoff 👀
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Ant group just dropped Ring-2.6-1T 🔥 1T reasoning model, built for real world agent workflows.
✨ MIT license
✨ 128K >> 256K context (YaRN)
✨ Async RL + IcePop training architecture
✨ Dual reasoning effort: "high" for fast agent loops, "xhigh" for deep reasoning = Better cost/performance tradeoff 👀
Show more
🥳You could always experience the latest, fastest and the most easy to use open model on SGLang, this time for our latest reasoning model release of Ring-2.6-1T (limited 75% discount on OR
Thanks to @lmsysorg for another top notch day0 collaboration! 🥳
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🎉 Congrats on the release of Ring-2.6-1T, a trillion-parameter flagship for complex, real-world tasks. Day-0 support is now live in SGLang!
☑️ Enhanced Agent Execution: stable multi-step, tool-calling & long-horizon workflows
☑️ Reasoning Effort Control: high & xhigh modes to tune depth, speed & cost
☑️ Async RL + IcePop: efficient, stable trillion-parameter RL training
Run it now with SGLang!
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🎉 Congrats on the release of Ring-2.6-1T, a trillion-parameter flagship for complex, real-world tasks. Day-0 support is now live in SGLang!
☑️ Enhanced Agent Execution: stable multi-step, tool-calling & long-horizon workflows
☑️ Reasoning Effort Control: high & xhigh modes to tune depth, speed & cost
☑️ Async RL + IcePop: efficient, stable trillion-parameter RL training
Run it now with SGLang!
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Another day0 collaboration, another community win. Thanks @vllm_project team for the always reliable support~ 🫡🫡
Congrats to @AntLingAGI on Ring-2.6-1T going open! 🎉
The thinking sibling of Ling-2.6-1T — trillion-scale, built for agent execution and complex reasoning.
Day-0 vLLM support is ready.
🤗
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Congrats to @AntLingAGI on Ring-2.6-1T going open! 🎉
The thinking sibling of Ling-2.6-1T — trillion-scale, built for agent execution and complex reasoning.
Day-0 vLLM support is ready.
🤗
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Lovely video! Glad to work with @novita_labs and @OpenRouter to bring another newly build model, Ring-2.6-1T to our beloved users.
It is available on OpenRouter with 75% through May~
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🚀 Ring-2.6-1T is now open source (from @AntLingAGI).
Now 90% off on @OpenRouter via @novita_labs — a great time to start building and experimenting with large-scale agent workflows.
A trillion-scale reasoning model built for real-world agents.
Designed not just to answer —
but to execute:
planning steps, using tools, maintaining context, and completing complex workflows.
Highlights:
• Strong agent execution
• high / xhigh reasoning modes
• Async RL + IcePop training
Show more
🚀 Ring-2.6-1T is now open source (from @AntLingAGI).
Now 90% off on @OpenRouter via @novita_labs — a great time to start building and experimenting with large-scale agent workflows.
A trillion-scale reasoning model built for real-world agents.
Designed not just to answer —
but to execute:
planning steps, using tools, maintaining context, and completing complex workflows.
Highlights:
• Strong agent execution
• high / xhigh reasoning modes
• Async RL + IcePop training
Show more
🚀 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.
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