China AI

AIMiniMax has released M2.5, which it says reaches 80.2% on SWE-Bench Verified and 76.3% on BrowseComp with context management. The company reports 37% faster end-to-end runtime than M2.1 on SWE-Bench Verified and prices M2.5 at $1 per hour at 100 tokens per second, with a 50 tokens per second version at $0.30 per hour. Weights are available on Hugging Face, with inference support listed for SGLang, vLLM, Transformers, and KTransformers.

Why it matters: The source gives benchmark scores against Claude and GPT models plus per-task token and runtime figures, so readers can weigh the cost-speed tradeoff directly.

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. The model integrates DeepSeek Sparse Attention to reduce deployment cost and reports strong results on reasoning, coding, and agentic benchmarks against GLM-4.7, DeepSeek-V3.2, Kimi K2.5, and several frontier models.

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. The model scores 94.62 on OmniDocBench V1.5 and supports deployment through vLLM, SGLang, and Ollama, with an official SDK for document parsing.

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. The model card reports a 1T-parameter Mixture-of-Experts architecture with 32B activated parameters and a 256K context length, and it lists benchmark results against GPT-5.2, Claude 4.5 Opus, Gemini 3 Pro, DeepSeek V3.2, and Qwen3-VL-235B-A22B-Thinking. Weights and code are released under a Modified MIT License, with API access on the Moonshot platform.

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. The company reports gains over M2 on coding and agent benchmarks such as SWE-bench Verified (74.0) and VIBE average (88.6), and says it outperforms Claude Sonnet 4.5 on multilingual scenarios.

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.

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. The model is a 1T-parameter mixture-of-experts with 32B activated parameters and a 256k context window, and it uses native INT4 quantization for roughly 2x faster generation. The model card reports benchmark results on HLE, BrowseComp, and other tests, and recommends vLLM, SGLang, or KTransformers for deployment.

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. The model card reports up to 6.3x faster TPOT than MLA at 1M tokens and up to 75% lower KV cache needs, and says it outperforms full attention on long-context and RL-style benchmarks.

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. The models use Kimi Delta Attention in a 3:1 hybrid ratio with global MLA, cutting KV cache by up to 75% and boosting decoding throughput by up to 6x at 1M tokens. The KDA kernel is open-sourced in FLA, and the checkpoints were trained on 5.7T tokens.

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.