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ModelScope releases open-weight Ling-3.1-flash agent model with 1M context

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ModelScope announces open weights for Ling-3.1-flash, a 560B MoE agent model that activates about 25B parameters per token and supports up to 1M context. The post reports an Artificial Analysis Intelligence Index score of 41 at about 210 tokens/s.

MXFP4 mixed precision cuts the model footprint from about 534GB to 292GB and the tested server setup from 8 accelerators to 4. The hosted API currently supports 256K context.

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The post gives concrete benchmark, speed, and footprint figures for an open-weight agent model, letting readers compare its deployment cost against alternatives.

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Ling-3.1-flash is now available with open weights: a 560B MoE agent model activating around 25B parameters per token, with support for up to 1M context.
🤖 https://modelscope.ai/models/inclusionAI/Ling-3.1-flash

🏆 Strong agent benchmarks: Leads the reported HealthBench Professional and FrontierSWE comparisons, and ranks second on GDPval-AA v2.1 among the models shown.
⚡ Fast and capable: Scores 41 on the Artificial Analysis Intelligence Index while generating around 210 tokens/s, placing it on the reported intelligence–speed Pareto frontier.
🎨 From prompts to working outputs: Ranks among the top open-weight models in Design Arena for data visualization, React Native, 3D, game development, SVG, and websites.
📦 Easier to deploy: MXFP4 mixed precision reduces the model footprint from around 534GB to 292GB and cuts the tested server setup from 8 accelerators to 4.
🧠 Built for long-running agents: Maintains goals, tool history, and intermediate results across hours-long workflows, with stronger JSON Schema tool calling for production integration.
The hosted API currently supports 256K context; the open model supports configurations up to 1M.

Source: ModelScope · x.comPublished · added here