A model that fires roughly 1.3B parameters per token out of 7.9B total is now being pitched as an agent backbone rather than a toy. Which makes me wonder where the efficiency curve actually flattens, because it clearly has not yet. The spec sheet is the interesting part. 256K context. Up to 32K tokens out. Native function calling and prompt caching, plus a switch between a thinking mode and an instant mode so you skip reasoning latency on turns that don’t need it. The whole thing is pointed at multi-turn tool loops instead of one-shot chat. This is Ling 3.0 Tiny, from InclusionAI, which is Ant Group’s lab. Those are the lab’s own reported numbers and I have not seen an independent eval yet, so weigh them accordingly. Saying this plainly because this sub will ask within four comments: there are no weights. No GGUF, nothing on HuggingFace, you can’t self-host it. It’s a hosted API you rent, reachable through Vercel’s gateway (free until 8am PT on Aug 14), plus OpenRouter and ZenMux. The flash-sized sibling is a different conversation. This one is closed. What actually interests me is the token burn math. If activated params keep falling like this, the boring middle layer of every agent gets close to free, and picking a model stops being an architecture decision and starts being a commodity purchase. Has anyone here run it head to head against a small open model on your own workload? I’d like to see where it loses. submitted by /u/truecakesnake
Originally posted by u/truecakesnake on r/ArtificialInteligence