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On October 22, 2026, Ant Digital Technologies released the decision model LingDT-Decision-1.0, offering two versions, 4B and 27B, aimed at high-frequency decision tasks in scenarios such as customer service, risk control, and finance, and it has already launched on the DTMaaS platform with a limited-time free trial. The model supports native multiple-choice and ordered decision-making (DAG). According to a same-day report by Leiphone, the company states that in scenarios such as customer service, risk control, and finance, compared with large language models it can reduce Token consumption by over 90% and improve response latency by 10x; across 23 open-source evaluation benchmarks, its overall score exceeds decision models such as Microsoft-Decision-1 and Jev 1.13.0, and the 27B version has the highest accuracy among models of the same parameter count. A same-day report by Zhidongxi states that the 27B version ranks first in overall score across 23 open-source evaluation benchmarks; Ant Digital Technologies' tests show that in data annotation scenarios, compared with DeepSeek-V4-flash, Token consumption is reduced by about 97% and processing latency improved by about 10x, and in intelligent customer service scenarios, compared with Qwen-3.7-flash, Token consumption is reduced by 90% and risk identification accuracy rises from 76% to 89%. The model is currently available on the DTMaaS platform for a limited-time free trial.
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Oct 22
Zhidx
Ant Digital Technologies releases decision model LingDT-Decision-1.0, 27B version ranks first in overall score
Ant Digital Technologies releases decision model LingDT-Decision-1.0, offering 4B and 27B versions, targeting high-frequency decision tasks in scenarios such as customer service, risk control, and finance, and now available for a limited-time free trial on the DTMaaS platform. The model supports native multiple-choice and sequential decision-making (DAG), and the 27B version ranks first in overall score across 23 open-source evaluation benchmarks, surpassing models such as Microsoft-Decision-1 and Jev 1.13.0. Ant Digital Technologies' tests show that in data annotation scenarios, Token consumption is reduced by about 97% and processing time improved by about 10x compared with DeepSeek-V4-flash; in intelligent customer service scenarios, Token consumption is reduced by 90% and risk identification accuracy increased from 76% to 89% compared with Qwen-3.7-flash.
Leiphone
Ant Group Digital Technologies releases decision model LingDT-Decision-1.0, offering two versions: 4B and 27B
Ant Group Digital Technologies has released the decision model LingDT-Decision-1.0, offering two versions, 4B and 27B. It is now live on the Ant Group Digital Technologies DTMaaS platform with a limited-time free trial. According to the company, in scenarios such as customer service, risk control, and finance, it can reduce Token consumption by over 90% and improve response time by 10x compared with large language models. The model supports native multiple-choice and sequential decision-making (DAG), and its overall score on 23 open-source evaluation benchmarks surpasses decision models such as Microsoft-Decision-1 and Jev 1.13.0; the 27B version has the highest accuracy among models of the same parameter size.
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Ant Digital Technologies Releases Decision Model LingDT-Decision-1.0, Cutting Token Consumption by Over 90%
Ant Digital Technologies has released its decision model LingDT-Decision-1.0, purpose-built for rapid industrial decision-making, offering 4B and 27B versions. It is now live on the Ant Digital Technologies DTMaaS platform with a limited-time free trial. The model natively supports multiple-choice and sequential decision-making (DAG), and outperforms decision models such as Microsoft-Decision-1 and Jev 1.13 across 23 open-source evaluation benchmarks, with the 27B version achieving the highest accuracy among models of the same parameter size. Tests by Ant Digital Technologies in customer service, risk control, finance and other scenarios show that, when handling the same tasks, it can reduce Token consumption by over 90% compared with large language models and improve response time by 10x.
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