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Ant Ling releases finance-enhanced model Ling-3.0-flash-Fin

Ant Ling releases its first finance-enhanced model, Ling-3.0-flash-Fin, which continues the Ling-3.0-flash architecture, maintaining 124B total parameters and 5.1B activated parameters. Through continued pre-training on financial corpora, domain post-training, and tool-use optimization, it strengthens the handling of complex financial content such as annual reports and financial workbooks. The model was tested on benchmarks including FinFIRST, FinSearchComp Verified, Finance Agent, APEX-Agents, SpreadsheetBench, and τ³-Banking, with its AA Intelligence Index v4.1.1 score rising from 38 to 41. The model will offer a one-month limited-time free API on OpenRouter, and its weights will be open-sourced next week.

Ant Ling Open-Sources Ling-3.0-flash Hybrid Reasoning Model

Ant Ling has open-sourced Ling-3.0-flash, a new-generation native hybrid reasoning model built on a MoE architecture with 124B total parameters and 5.1B activated parameters, and is simultaneously releasing a base version along with multiple precision versions including FP8, FP4, and INT4. The model supports three deployment approaches: cloud API calls, end-to-end local inference on a single NVIDIA DGX Spark using the MXFP4 or INT4 version, and high-performance deployment achieving an average output rate exceeding 1100 tokens/s under designated GPU test configurations. On the Artificial Analysis leaderboard, its output speed is 353 tokens/s, and in the AA Intelligence Index, the weighted average invocation cost per task is approximately $0.04.

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