Ant Group's New Decision Model Beats Microsoft and Jev, It Says
Ant Group’s digital technology unit, Ant Digital Technologies, released a decision model called LingDT-Decision-1.0 on October 22, pitching it at businesses that need machines to make fast, repeatable calls rather than hold conversations. The company says it handles structured decision tasks with higher accuracy, faster responses and lower token costs than rival decision models from Microsoft and Jev, and positions it squarely for industrial AI deployments.
The model comes in two sizes, 4B and 27B parameters. That split matters. A 4-billion-parameter model is small enough to run cheaply at high volume, while the 27-billion version can take on harder cases. Both are aimed at the unglamorous work that actually fills enterprise systems: approvals, routing, risk screening, pricing and scoring, where a wrong answer costs money and a slow one costs customers.
Decision models are a quieter branch of the AI boom. While chatbots and coding assistants grab headlines, much of the money in Chinese enterprise AI is being spent on systems that classify, rank and choose. These tasks are typically judged on accuracy and cost per call rather than on how fluently they write, which is why Ant’s pitch leans on token economics as much as on benchmark scores.
Ant’s claim to beat Microsoft and Jev is the kind of assertion that will be tested quickly. Benchmark comparisons in this field are notoriously sensitive to how tasks are framed, and vendors rarely publish the full evaluation setup. The more meaningful test is whether paying customers keep the model in production once the pilot period ends.
The release also fits a broader pattern among Chinese technology firms: building vertical models tuned to specific industries rather than competing head-on with general-purpose flagships. Ant Digital Technologies has spent years selling cloud, blockchain and risk-management tools to banks, insurers and retailers, giving it a distribution channel that a pure model lab would envy.
For global readers, the interesting question is not whether one benchmark tops another but whether cheaper, smaller decision models change the math on automation. If a 4B model can make routine calls at a fraction of the token cost of a large general model, the bottleneck shifts from model quality to data plumbing and trust, the parts of enterprise AI that no launch event fixes.
Ant has not disclosed pricing or a full customer list, and independent verification of the accuracy claims is still pending. What is clear is that the company is treating decision-making as a product category, not a feature bolted onto a chatbot.
The next checkpoint will be third-party evaluations and early customer disclosures. Until those arrive, treat the comparison with Microsoft and Jev as a claim to be checked, not a settled result.