Today, we’re releasing Ling-3.0-flash—a hybrid-reasoning MoE model built for production-scale agents.
124B parameters. Just 5.1B active per token.
With 1/8 of the total and 1/12 of the active parameters, it matches or beats our 1T flagship model on most benchmarks shown.
Ling-3.0 starts with native hybrid-linear attention: KDA and MLA layers stacked 5:1.
KDA gives fine-grained control over long-range memory, while 1/64 expert activation makes MoE compute more efficient.
It supports 256K context natively and can scale to 1M.
Ling-3.0-flash is now live on OpenRouter—and free to use through August 3, 2026.
Try it in your coding, search, research, and tool-use workflows. Then show us what you build:
https://t.co/fOgz3YCqVc
Demo 1 — From one prompt to a 3D world.
Using Blender MCP, Ling-3.0-flash wrote Python, built a city with elevated roads, skyscrapers, and materials, set the camera path, and rendered an aerial video—showing spatial reasoning and long-horizon tool use.
mixture-of-experts — A model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
Why it matters
A 124B mixture-of-expertsA model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.Full definition → that activates only 5.1B parameters per tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → and holds 256K context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → natively targets long-horizon tool useA model's ability to call external functions — run code, search the web, edit files — instead of only generating text.Full definition → at close to small-model serving cost, worth benchmarking against larger hosted models.
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