Ant Digital Launches LingDT-Decision-1.0 AI Model
The new decision-making AI model offers significant improvements in speed, token efficiency, and accuracy for businesses.
Updated on Oct. 22, 2026 in Artificial Intelligence
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Ant Digital Technologies has introduced its LingDT-Decision-1.0 model, designed specifically for complex business decision-making tasks. The model is available in 4B and 27B parameter versions and has already secured the top ranking across 23 open-source benchmarks.
Why it matters
By selecting from predefined options rather than generating text for external parsing, this system significantly reduces computational overhead. This approach allows organizations to streamline decision processes while minimizing errors and operational costs.
The LingDT-Decision-1.0 system achieves a 90% reduction in token usage and improves response times by up to 10 times compared to standard systems. Its 27B parameter version currently leads in combined scores across 23 open-source benchmarks.
The players
Ant Digital Technologies
This technology firm specializes in developing advanced artificial intelligence solutions tailored for enterprise decision-making processes.
The details
The model functions by executing sequential decisions based on specific task dependencies rather than traditional generative text outputs. It has demonstrated superior accuracy in risk identification and hallucination recall when compared to existing models like Microsoft-Decision-1, Jev 1.13.0, and Fastino/GLiDE.
Timeline
October 22, 2026: The LingDT-Decision-1.0 model is officially introduced.
The Tech Race
This release signals a pivot in the enterprise AI arms race, prioritizing high-speed, decision-centric logic over general-purpose generative text models. By outperforming established tools like Microsoft-Decision-1, Ant Digital is challenging the dominance of traditional generative frameworks.
Developers and businesses can now test the 4B and 27B model versions to determine how the increased accuracy and lower token usage improve their specific automated workflows. These technical improvements suggest faster deployment cycles and reduced computational costs for enterprise users.
The takeaway
The shift toward decision-specific AI highlights the growing need for specialized models that prioritize speed and reliability over generic output. Businesses looking to optimize their automated workflows should monitor how these efficiency gains scale in real-world application settings.
Further reading
Learn more about advancements in Artificial Intelligence across the industry.
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Do you trust automated AI models to make complex business decisions for your company?
