Ant open-sources Ling-3.1-flash-1019 and Ling-3.1-flash-base
Ant’s Bailing team released the Ling-3.1-flash-1019 checkpoint for personal-agent workloads and simultaneously published BF16 Ling-3.1-flash-base weights under the MIT license. Both use a 560B-total, 25B-active mixture-of-experts design. The 1019 checkpoint supports a context window of up to 1 million tokens and scored 44 on Artificial Analysis Intelligence Index v4.3.2, three points above the previous version. Bailing says the base model achieved the highest score in 32 of 47 tests against DeepSeek-V4-Pro-Base and MiMo-V2.5-Pro-Base, but the supplied reports do not establish independent reproduction of that comparison.
The release gives developers a post-trained checkpoint for personal-agent tasks and MIT-licensed base weights they can use for further pretraining, fine-tuning or reinforcement learning. It also exposes a trade-off: Artificial Analysis figures cited in the reports show that 1019 produced about 40% more output tokens per Intelligence Index task and raised average per-task inference cost from $1.01 to $1.49.
Well, “coming soon” turned out to be five days.
I posted the earlier announcement last week. The 1019 weights are now on Hugging Face.
Still 560B total, 25B active, with a 1M context window. This is the update they reported at 44 on the AA Intelligence Index v4.3.2, up from 41. AutomationBench-AA went from 62% to 68%, according to the release.
They also put out Ling-3.1-flash-base in BF16 for further training. Glad they included that. It gives people a starting point for their own post-training instead of having to start with the finished chat model.
One small gotcha: the SGLang example in the 1019 README currently still points to inclusionAI/Ling-3.1-flash. Change the model path to inclusionAI/Ling-3.1-flash-1019 if you're copying that command for the new checkpoint. submitted by /u/BerryBlush_Rogers691
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#Ant#Bailing#Ling-3.1-flash-1019#Ling-3.1-flash-base#Ling-3.1-flash#Artificial Analysis#DeepSeek-V4-Pro-Base#MiMo-V2.5-Pro-Base