# Ant Ling — What It Takes to Turn a General Model into a Domain Expert

- Company: Ant Ling (ant-ling.com)
- Announced: 2026-09-18
- Category: new-model
- Coverage: not counted
- Announcement: yes
- Group: models
- Source: https://developer.ant-ling.com/en/blogs/general-model-domain-expert
- Record: https://forck.live/items/24486-what-it-takes-to-turn-a-general-model-into-a-domain-expert
- Subject: Ling / Ring / Ming
- Models affected: Ling-3.0-flash, Ling-3.0-flash-Fin, Ling-3.0-flash-VL
- Context window: 256K context window

Ant Ling has extended Ling-3.0-flash into a financial domain model, Ling-3.0-flash-Fin, through continued training, task-specific post-training and tool-use optimization. The company says a general model can discuss a domain long before it can be trusted to work in one, and that professional tasks demand the right source, entity, reporting period and a checkable calculation. Ling-3.0-flash-Fin keeps the same sparse MoE architecture as Ling-3.0-flash, with 124B total parameters, 5.1B activated per inference step and a 256K context window. Its target is the chain of work around an answer: retrieve, verify, calculate, model, write and preserve enough context for the work to be inspected. Ant Ling says the model can read a company report and a seven-sheet Excel model with more than 5,000 formulas, reconcile a 2026 Q2 forecast with reported actuals and update linked formulas without breaking formula lineage. The company released FinFIRST alongside the model, an open benchmark for financial search agents developed with support from financial specialists. FinFIRST's first release contains 123 expert-authored and validated tasks from more than 50 financial professionals, with 701 atomic scoring items and 12,300 rubric points, and reports Atomic, Loose Pass and Strict Pass.

## Evidence

Verbatim from https://developer.ant-ling.com/en/blogs/general-model-domain-expert:

> Ling-3.0-flash-Fin is our first financial enhancement of Ling-3.0-flash. Built on the same sparse MoE architecture—124B total parameters, with 5.1B activated per inference step and a 256K context window—it combines high-quality financial continued training, domain post-training, and tool-use optimization.

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