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 Ling-3.0-flash-VL on professional knowledge work. It scores 1171 Elo on GDPval-AA v2 and 967 on AA-Briefcase, compared with 1225 and 986 respectively for Ling-3.0-flash-VL. Both benchmarks test agents on professional tasks such as producing documents and spreadsheets.
➤ Difficult agentic tasks remain a challenge for Ling-3.0-flash-Fin. It scores 7% on AutomationBench-AA, which tests workflows across business apps while respecting guardrails, compared with 16% for the flash-VL model. Both models score 0% on Terminal-Bench v4.0, which tests difficult terminal-use tasks.
➤ Ling-3.0-flash-Fin uses more output tokens than the flash-VL model and MiniMax-M2.7. It averages ~67k output tokens per Intelligence Index task, about 34% more than VL (~50k) and 3.2x MiniMax-M2.7 (~21k).
Additional model details:
➤ Type: Open weights reasoning model.
➤ Size: 124B total parameters, 5.1B active per token (MoE).
➤ Context window: 256K tokens.
➤ Modalities: Text input and output.
➤ API availability: Available through @OpenRouter, including a rate-limited free endpoint.
➤ License: MIT.