Ling-3.1-flash-1019: Hardware Needed and How to Run It Free

Ling-3.1-flash-1019 packs a massive 560 billion parameters and a 1 million-token context window. Its AA-Briefcase score jumps to 1524 Elo, a notable leap from its predecessor.

Ling-3.1-flash-1019 is a hybrid-linear mixture-of-experts model released by InclusionAI. It contains a total of 560 billion parameters, but only 25 billion are activated for each token processed. The architecture uses 63 KDA layers, nine gated MLA layers, and 512 routed experts, with eight experts active at any time. Its hidden size is 4096, and it supports a context length of one million tokens, far larger than most open models. The model is available under an MIT license on Hugging Face, and the weights can be downloaded for free.

Compared with the earlier Ling-3.1-flash, this version adds post-training optimizations that boost reasoning and real-world agentic performance. The AA-Briefcase rating climbs from roughly 1400 to 1524 Elo. AutomationBench-AA accuracy rises from 62 % to 68 %. Scientific benchmarks also improve, with CritPt moving from 18 % to 26 % and SciCode from 54 % to 57 %. The model’s context window expands dramatically to one million tokens, enabling much longer inputs without truncation. These changes make the model more capable in multi-step tasks and complex workflows.

The model card reports several benchmark improvements. The AA-Briefcase score, a measure of reasoning ability, reaches 1524 Elo, up from about 1400 for the prior version. AutomationBench-AA, which tests real-world agentic tasks, shows an accuracy of 68 percent, a six-point gain. On the scientific benchmark CritPt, the model scores 26 percent, up from 18 percent, and on SciCode it achieves 57 percent, improving from 54 percent. These numbers indicate stronger performance on both reasoning and domain-specific tasks.

Ling-3.1-flash-1019 shines in complex, multi-step reasoning and can handle very long inputs thanks to its one-million-token context window. It also shows measurable gains on automation and scientific benchmarks. However, the model requires substantial memory: at 4-bit precision with an 8 K context it needs about 349 GB, and even the smallest home machine that can hold it fully is a Mac Studio M3 Ultra with 512 GB of RAM. Running inference at full size yields roughly 26 tokens per second on that hardware. Additionally, there is currently no free API on OpenRouter, so users must download the weights directly.

If you need a model that can process extremely long texts or perform complex, multi-step reasoning, Ling-3.1-flash-1019 is worth exploring. It is suited for researchers experimenting with agentic workflows, developers building tools that require extensive context, and hobbyists who have access to high-end machines like a Mac Studio M3 Ultra. Because the weights are free under an MIT license, you can download and run the model yourself, but you should be prepared for the memory and compute requirements. Those comfortable with self-hosting and interested in cutting-edge open models will find this release valuable.

What do you need to run it at home? At 4-bit it takes about 349 gigabytes of memory. The smallest home machine that holds it fully is the Mac Studio M3 Ultra 512 GB. On the Mac Studio M3 Ultra 512 GB it writes about 26 tokens per second.

無料のAPIはまだありません。重みは公開されており、MITライセンスでHugging Faceからダウンロードできます。Arenaではまだ順位がついていません。投票が集まり次第、スコアを追加します。

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