China AI

AIZ.ai launches GLM-5, scaling from 355B to 744B total parameters with 40B active and pre-training data from 23T to 28.5T tokens.

Why it matters: The source gives concrete scale, data, and benchmark comparisons against named frontier models, showing where GLM-5 sits among open-source and proprietary systems.

AIZ.ai has released GLM-OCR, a 0.9B-parameter multimodal OCR model for complex document understanding, under the MIT License.

Why it matters: The page gives benchmark scores, a 0.9B parameter size, and supported serving frameworks, which help readers weigh OCR deployment options against heavier alternatives.

AIZ.ai has released GLM-4.7-Flash, a 30B-A3B MoE model that it positions as the strongest model in the 30B class. The model reports SWE-bench Verified 59.2 and τ²-Bench 79.5, and supports local deployment through vLLM and SGLang.

Why it matters: The source lists benchmark scores against Qwen3-30B-A3B-Thinking-2507 and GPT-OSS-20B, letting readers compare the 30B-class MoE model directly with its named rivals.

AIMoonshot AI released Kimi K2.5, an open-source native multimodal agentic model built by continual pretraining on about 15 trillion mixed visual and text tokens.

Why it matters: The model card gives a full benchmark table against GPT-5.2, Claude 4.5 Opus, and Gemini 3 Pro, useful for comparing open multimodal agent models.

AIMiniMax has released MiniMax-M2.1 model weights on Hugging Face, with API access on the MiniMax Open Platform and the MiniMax Agent product.

Why it matters: The release pairs open weights with a broad benchmark table against Claude and GPT models, letting readers compare coding and agent claims directly.

AIXiaomi released and open-sourced MiMo-V2-Flash, a Mixture-of-Experts model with 309B total and 15B active parameters, under the MIT license.

Why it matters: The post gives architecture, speculative decoding speedup, and pricing figures, which help readers judge how the efficiency claims are achieved and what they cost.

AIMoonshot AI released Kimi K2 Thinking, an open-source thinking model that interleaves step-by-step reasoning with tool calls across 200 to 300 sequential invocations.

Why it matters: The model card gives benchmark tables, quantization details, and deployment settings, letting readers compare Kimi K2 Thinking against GPT-5 and other models on specific tasks.

AIMoonshot AI released Kimi Linear, a hybrid linear attention architecture with 48B total and 3B activated parameters and a 1M-token context length, on Hugging Face.

Why it matters: The model card gives concrete long-context speed and memory figures for a hybrid attention design, useful for judging whether linear attention can replace full attention in practice.

AIMoonshot AI has released Kimi-Linear-Base and Kimi-Linear-Instruct, both 48B total and 3B activated parameters with a 1M context length, on Hugging Face.

Why it matters: The model card gives concrete throughput and KV cache figures for a hybrid attention design, which helps readers weigh its long-context tradeoffs against full attention.