Ant Shuke Releases LingDT-Decision-1.0: 4B and 27B Models for Fast Decisions

On October 22, Chinese company Ant Shuke (蚂蚁数科), the technology arm of the Ant Group ecosystem, officially released LingDT-Decision-1.0, a decision-making model. The model is built for fast decisions on structured tasks and comes in two versions: a compact 4B and a heavier 27B parameters. According to the developers, it is more accurate, faster and cheaper in tokens than comparable decision-making models, including Microsoft's. The company says its target area is industrial AI.

Why a separate model for decisions

Most of the high-profile releases of recent months target general-purpose dialogue: reasoning, code, multimodality. Industrial customers have different problems. A bank needs to assess an application based on a set of fields. A logistics company needs to pick a route from a given set of options. A factory needs to decide what action to take when parameters drift out of range. The input here is structured, and the output usually boils down to a choice from a limited set of options or a short justification.

For these scenarios, a huge general-purpose model is overkill. It takes longer to respond, spends more tokens on unnecessary reasoning and costs more on every call. When requests number in the millions per day, a difference in token price translates directly into a difference in project margin. Ant Shuke is betting on exactly this economics: a specialized model should beat general-purpose competitors on three axes at once, namely accuracy, latency and cost.

Two sizes for different tasks

Shipping two configurations at once reflects a practice that has become established in enterprise AI. The 4B version suits scenarios where minimal latency and modest hardware requirements matter: it is easier to deploy inside a customer's perimeter or on limited compute. The 27B version is meant for more complex structured tasks, where you need headroom in quality and have to account for more factors.

Key parameters of the release:

• Name: LingDT-Decision-1.0
• Developer: Ant Shuke, a unit of the Ant Group ecosystem
• Release date: October 22
• Sizes: 4B and 27B parameters
• Specialization: fast decisions on structured tasks
• Claimed advantages: higher accuracy, faster response, lower token cost
• Target segment: industrial AI scenarios
• Status: the model is already available on the company's platforms

The name of the line hints at a link to the Ling family, under which the Ant Group ecosystem releases its language models. For the company, this is a way to turn a shared technology base into a set of narrow products it can sell to enterprise customers.

Comparison with Microsoft and the question of verification

The release's main marketing claim is that LingDT-Decision-1.0 is more accurate than Microsoft's solutions. Comparing itself with the American giant sets a clear bar: Chinese developers increasingly measure themselves not against domestic counterparts but directly against Western enterprise stacks. That said, claims of superiority on narrow benchmarks for structured tasks depend heavily on the test set and the question format. Independent measurements and pilots on specific customers' data will show the real picture.

The idea itself looks sound. Models from 4B to 27B have long since shown that, once tuned for a narrow task, they can compete with far larger systems. If Ant Shuke has indeed achieved the claimed combination of accuracy and cheap tokens, the model will find demand where general-purpose LLMs run into budget limits.

Who needs it and where

The release's practical value depends on your use case. For financial, insurance, logistics and manufacturing teams in China, it is one more option in the enterprise toolkit: a specialized model can be plugged into existing scoring and dispatch workflows. Anyone who currently pays for calls to large general-purpose APIs to make simple decisions over tables and fields should check whether a small specialized model, especially the 4B version, would be cheaper.

For the market as a whole, the signal is that competition is shifting from the "smartest model" to the "most cost-effective model for a specific process." The next step for Ant Shuke is to publish its test methodology and results on open and industry datasets. Without that, the comparison with Microsoft will remain a vendor's claim, and enterprise customers will choose based on their own pilots.