Ling-3.0-flash-Fin, Ant Group’s new finance-focused open weights model, scores 23 on the Artificial Analysis Intelligence Index and 24 on the Finance & Accounting Index, and is on the Intelligence vs. Active Parameter Pareto Frontier
@AntLingAGI has released Ling-3.0-flash-Fin, a finance-focused model built on Ling-3.0-flash. Ant Group announced that it developed the model with financial institutions and industry experts to support financial research, including checking sources, building valuation spreadsheets and writing reports. This text-only model comes after their release of their image and video input-capable model Ling-3.0-flash-VL, which scored 25 on the Intelligence Index.
Key results:
➤ Ling-3.0-flash-Fin matches MiniMax-M2.7’s Intelligence Index score with roughly half the active parameters. Both score 23, while Flash-Fin activates 5.1B parameters per token compared with MiniMax-M2.7’s 10B.
➤ Ling-3.0-flash-Fin matches Ling-3.0-flash-VL at 24 on the Artificial Analysis Finance & Accounting Index. Fin has higher business knowledge accuracy than VL (17% vs. 11%), but also higher business knowledge hallucination (33% vs. 19%)
➤ Ling-3.0-flash-Fin scores slightly below…
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
Ant Group's Ling-3.0-flash-Fin matches MiniMax-M2.7's intelligence score with about half the active parameters (5.1B vs 10B), showing a finance-specialized model can be competitive at lower inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → cost.
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