Ling-3.0-flash-Fin

Introduction

Ling-3.0-flash-Fin is the first finance-enhanced model in the Ant Ling family. Developed by Ant Group with leading financial institutions and domain experts, it extends Ling-3.0-flash through continued training on high-quality financial data.

With 124B total parameters, 5.1B activated parameters, and a 256K context window, the model combines financial expertise with efficient inference for long-horizon agent workflows.

Highlights

End-to-end financial research: connects information retrieval, evidence review, calculation, modeling, and report preparation instead of treating them as isolated tasks.
Source-grounded financial search: Prioritizes authoritative sources to deliver accurate, complete, and traceable answers; FinFIRST is open-sourced alongside the model to enable transparent evaluation of these capabilities.
Multi-document financial reasoning: reconciles reporting periods, definitions, assumptions, and conflicting figures across annual reports, earnings releases, regulatory filings, and research materials.
Valuation and spreadsheet workflows: understands formulas, actual-versus-estimate updates, cross-sheet dependencies, balance checks, scenario analysis, and editable financial-model delivery.
Research-ready outputs: organizes facts, analysis, judgments, and charts into clear, reviewable materials for further editing and professional review.

Evaluation

Ling-3.0-flash-Fin was evaluated across FinFIRST, FinSearchComp Verified, FinCRAFT, Finance Agent, APEX-Agents, SpreadsheetBench, and τ³-Banking. These benchmarks cover source-grounded retrieval, investment research, long-horizon execution, valuation modeling, spreadsheet operations, and banking workflows. The model is competitive with both similarly sized models and substantially larger general-purpose models, with particular strength in source selection and tool-intensive financial tasks.

Local Serving

The current checkpoint is released in BF16. Because Ling-3.0-flash-Fin shares the same architecture as Ling-3.0-flash, it is compatible with the same SGLang and vLLM runtimes. For deployment instructions, see the Ling-3.0-flash deployment guide.

Important: Thinking mode is enabled by default. For optimal performance, we strongly recommend using temperature=1.0, top_p=0.95, and top_k=20 for general inference.

Limitations and Future Work

As our first finance-enhanced release, Ling-3.0-flash-Fin still requires further validation in complex, long-horizon workflows. Key assumptions, valuation results, and investment conclusions require professional review and do not constitute investment advice.

Future releases will explore finance-enhanced models at larger scales to further improve complex reasoning and long-horizon task execution.