Ant Group's Bailing Open-Sources Ling-3.1-flash-1019: Agent Benchmark Scores Rise, Per-Task Cost Up Nearly 50%
Ant Group's Bailing lab open-sourced Ling-3.1-flash-1019, improving third-party benchmark scores but significantly increasing token consumption and per-task costs.
According to a report by Zhidongxi, Ant Group's AI lab Bailing open-sourced the Ling-3.1-flash-1019 model on October 28, specifically trained for Personal Agent scenarios, along with the base model Ling-3.1-flash-base. Both models use the MIT license, support up to a 1 million token context window, have 560 billion total parameters, and activate approximately 25 billion parameters per token.
Per Bailing's disclosure, the 1019 version scored 44 points in Artificial Analysis (AA)'s Intelligence Index v4.3.2 evaluation, an improvement of 3 points over the previous generation, ranking 4th among same-size open-weight models as of October 27. In the complex knowledge work test AA-Briefcase v1.1, its score rose from 1398 Elo to 1521 Elo; in the automation test AutomationBench-AA, it improved from 62% to 68%.
The performance boost comes with higher computational overhead. According to AA statistics, the 1019 version outputs an average of about 145,000 tokens per intelligence index task, an increase of over 40% compared to the previous generation, raising the average cost per task from $1.01 to $1.49. API pricing for the 1019 version is $0.30 per million input tokens and $0.90 per million output tokens.
This upgrade is closely tied to the needs of Ant Group's personal AI assistant, Lingguang. On October 27, Ant Group released Lingguang 2.0, adding cross-session memory and long-term task scheduling capabilities. Bailing stated that previously released models had already undergone alignment training based on feedback from applications like Lingguang. As Agents move from single conversations to long-term operations, the number of tokens consumed per task will directly impact service costs.