HomeAnt Group Open-Sources Trillion-Parameter Ling-2.6-1T to Boost Quick Thinking
The Ling-2.6-1T, a trillion-parameter flagship model developed by Ant Group's Bailing Large Model team, was announced today as open-source for developers. Rather than simply increasing parameter count, it focuses on systematically improving instruction following, tool usage, and long-context handling in real-world complex tasks.
Architectural Innovation Powers an Efficient "Fast Thinking" Mechanism
Ling-2.6-1T uses an innovative hybrid architecture that cuts token costs via an enhanced reward strategy that reduces process redundancy. This fast-thinking mechanism enables high-quality output at lower cost while preserving the full capability of a trillion-parameter model, greatly improving the intelligence-to-cost ratio.
For complex workflows, the model improves learning of composite tasks. In several authoritative benchmark evaluations for execution tasks, Ling-2.6-1T shows strong multi-step execution abilities, achieving top-tier open-source performance in code generation, bug fixing, and precise reasoning under noisy conditions.
Full-Stack Compatibility Enables Enterprise Production Deployment
To make the trillion-parameter model usable in production, Ling-2.6-1T offers high compatibility with major Agent frameworks. It adapts to complex business scenarios involving multiple tools and constraints, aiming to serve as a core capability that can be deployed and maintained within enterprise systems.
The model is now available on open-source platforms like Hugging Face and ModelScope. To help developers worldwide try and evaluate it, the team also extended the free API call service on OpenRouter by one week, further lowering the barrier to using the trillion-parameter model.
Hugging Face: https://huggingface.co/inclusionAI/Ling-2.6-1T
ModelScope: https://www.modelscope.cn/models/inclusionAI/Ling-2.6-1T
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The Ling-2.6-1T, a trillion-parameter flagship model developed by Ant Group's Bailing Large Model team, was announced today as open-source for developers. Rather than simply increasing parameter count, it focuses on systematically improving instruction following, tool usage, and long-context handling in real-world complex tasks.
Architectural Innovation Powers an Efficient "Fast Thinking" Mechanism
Ling-2.6-1T uses an innovative hybrid architecture that cuts token costs via an enhanced reward strategy that reduces process redundancy. This fast-thinking mechanism enables high-quality output at lower cost while preserving the full capability of a trillion-parameter model, greatly improving the intelligence-to-cost ratio.
For complex workflows, the model improves learning of composite tasks. In several authoritative benchmark evaluations for execution tasks, Ling-2.6-1T shows strong multi-step execution abilities, achieving top-tier open-source performance in code generation, bug fixing, and precise reasoning under noisy conditions.
Full-Stack Compatibility Enables Enterprise Production Deployment
To make the trillion-parameter model usable in production, Ling-2.6-1T offers high compatibility with major Agent frameworks. It adapts to complex business scenarios involving multiple tools and constraints, aiming to serve as a core capability that can be deployed and maintained within enterprise systems.
The model is now available on open-source platforms like Hugging Face and ModelScope. To help developers worldwide try and evaluate it, the team also extended the free API call service on OpenRouter by one week, further lowering the barrier to using the trillion-parameter model.
Hugging Face: https://huggingface.co/inclusionAI/Ling-2.6-1T
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