Ling-3.1-flash is now open source: ~560B total parameters, ~25B active per token and up to 1M context. Built for real work, it can turn a rough brief into a page, mobile UI or interactive prototype teams can review and refine. See the demo.

For local serving, MXFP4 experts and block-FP8 dense layers cut weights from ~534GB to ~292GB; a tested Hopper setup moved from 8 GPUs to 4. In separate tests, 8 GPUs averaged 604ms to first token and 4 GPUs reached ~1.36 QPS. Workloads differed.

For a business assistant, capability matters alongside response speed. On the Artificial Analysis leaderboard, Ling-3.1-flash pairs an Intelligence Index of 41 with ~210 output tokens/s. This helps teams assess both dimensions for frequent use.

High-frequency use also has a budget constraint. A second Artificial Analysis view plots output speed against blended price: Ling-3.1-flash sits at ~210 tokens/s and ~$0.19 per 1M tokens. Provider prices may differ from this benchmark measure.

What can teams build? Ling-3.1-flash drafts React Native screens, editable SVGs, websites and Web 3D prototypes. In Design Arena Open Weights, it ranks #3 in React Native and 3D, #4 in SVG and #5 in Website. Review the outputs in your own workflow.

Thanks to @sgl_project and @vllm_project for supporting the Ling family across open inference stacks. Their work gives developers more ways to deploy and tune Ling models in their own infrastructure.

Get the open weights and model docs: Hugging Face: ModelScope:

Join the Ant Ling community on Discord:

Finally, been waiting for this, as someone who used Ling-3.1-flash extensively via api i can tell you have done a phenomenal job with it.

开源了！Ling 这个模型越用越好用(òωó)👍

太期待了 终于等到你！

哇塞，开源了，之前使用过，效果非常好，模糊的需求就可以生成质量很高的作品。

终于开源了！可以升级到 3.1 了！

终于等到你🎉

review and refine is the part i'd want demoed. a generated page always looks right on its own. i had two copies of the same screen this week, one in the brand font and one not, and both passed a look-at-it review

congrats on the open-weights drop 🔥 already cooking quants for it :)

总参数约 560B，每 token 激活约 25B 量化后权重从约 534GB 压到约 292GB（MXFP4 专家 + block-FP8 稠密层） 实测 Hopper：8 卡收到 4 卡；8 卡首 token 约 604ms，4 卡约 1.36 QPS

🔥🔥

等了好久 终于等到了，可以好好体验下大模型的能力了

I've tested it. It is pretty cool .

I tested it myself, and this model has really high aesthetic standards - it actually looks even better than the frontend design I spent months working on, hahaha

开源好啊，我也要升级

5600亿，真的不同凡响。。

When ling 3.1 tiny?