China AI Native Industry Insights – 20261026 – Moonshot AI | MiniMax | Alibaba | more
Today’s digest highlights significant advancements from Moonshot AI, MiniMax, and Alibaba’s Ant Ling team. The overarching theme focuses on the evolution of AI models with enhanced capabilities in multimodal processing and intelligence benchmarks. Moonshot AI introduces the Kimi K3.1 Pro, a 3T-class multimodal model, emphasizing substantial progress in open-weight computing. Concurrently, MiniMax’s M3.1 Flash boasts model weights with an impressive 428 billion parameters, signifying a stride in AI scale and complexity. Additionally, Alibaba’s Ling-3.1-flash-1019 achieves superior intelligence and agentic benchmark scores, showcasing the rising sophistication and potential of modern AI systems. Discover more in Today’s China AI Native Industry Insights.
1. Moonshot AI launches Kimi K3.1 Pro, a 3T-class open-weight multimodal model
Moonshot AI introduced Kimi K3.1 Pro, a mixture-of-experts model with 2.8 trillion total parameters and 104 billion active parameters, supporting a 1-million-token context window and multimodal inputs. The model supports full-project coding including game development in Unreal, Unity and Godot, and parametric 3D modeling in Blender and CAD. Moonshot AI reports a 65% reduction in hallucination for financial modeling tasks and benchmark performance matching leading proprietary models in coding, agentic, and knowledge tasks. K3.1 Pro and a faster variant, K3.1 Pro Highspeed, are now available via Kimi.com, Kimi Work, Kimi Code, and the Kimi API, with open weights planned for release by November 4, 2026.
Read more: https://www.kimi.ai/blog/kimi-k3-1-pro
Video Credit: The original article
2. MiniMax Open-Sources M3.1 Flash Model Weights With 428B Parameters
MiniMax released the weights of its M3.1 Flash model to the public on Hugging Face and ModelScope. The model has 428 billion total parameters with 23 billion activated per token, using a mixture-of-experts design. MiniMax reports strong agentic performance, scoring 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents’ Last Exam, and 82.2 on CyberGym. The model also uses MiniMax Sparse Attention and DSpark speculative decoding to improve inference efficiency, and is accessible via API and the MCode desktop tool.
Read more: https://mp.weixin.qq.com/s/uqZpu1NYy7Iwn2Aj9OKL1g
Video Credit: NotebookLM
3. Alibaba’s Ant Ling team unveils Ling-3.1-flash-1019 with higher intelligence and agentic benchmark scores
Alibaba-affiliated team Ant Ling AGI introduced Ling-3.1-flash-1019, an updated version of its Ling-3.1-flash model. The model scored 44 on the Artificial Analysis Intelligence Index, up from 41 for the previous version. It also showed gains on agentic benchmarks, with AA-Briefcase rising from about 1400 to 1524 Elo and AutomationBench-AA improving from 62% to 68%. API access and open weights for the model are expected to be released soon.
Read more: https://x.com/i/web/status/2113226531522891851
Video Credit: NotebookLM
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