WildClawBench, a 60‑question, Docker‑based benchmark from Shanghai AI Lab’s InternLM team, evaluates AI agents across six multimodal categories, exposing low ceilings for top models like Claude Opus 4.6, highlighting cost‑performance trade‑offs and the rapid rise of Chinese models such as GLM 5.
This article breaks down every architectural and training decision behind Gemma‑4—KV sharing, p‑RoPE, per‑layer embeddings, and a dual‑path MoE + dense MLP—while contrasting its efficiency and performance with Qwen‑3 and GLM‑5 across benchmarks, quantization strategies, and RL pipelines.
AutoClaw wraps the open‑source OpenClaw client, turning a half‑day installation into three simple steps—download, install, and auto‑configure—while adding seamless Feishu integration, support for GLM‑5 and pony‑alpha‑2 models, built‑in skills, and security recommendations for custom skill creation.
GLM‑5, the 744‑billion‑parameter open‑source LLM, introduces DeepSeek Sparse Attention, Multi‑latent Attention, Muon Split optimizer, and a fully asynchronous agentic reinforcement‑learning framework, achieving state‑of‑the‑art performance on long‑context, code, math, and multimodal benchmarks while running efficiently on domestic Chinese chips.
The article dissects GLM-5’s 744B‑parameter MoE design, 28.5 T token training corpus, novel Muon Split and MLA‑256 optimizations, DSA sparse attention, a fully asynchronous RL pipeline, extensive domestic chip adaptation, and benchmark results that place it on par with Claude Opus 4.5 and ahead of Gemini 3 Pro.
The article evaluates GLM‑5, the claimed strongest open‑source large language model, comparing its benchmark scores to Claude Opus, Gemini and GPT, detailing its DeepSeek‑inspired architecture, quantized FP8 deployment requirements, and step‑by‑step usage of Ollama’s free cloud model with Agent, data‑analysis and document‑generation features.
This article explains how Baidu's new GLM-5 large model is adapted to the Kunlun P800 XPU, detailing the async reinforcement learning framework Slime, optimization techniques like INT8 quantization and tensor‑parallelism, and provides step‑by‑step deployment commands using the open‑source vLLM‑Kunlun plugin.
The article reviews the newly released GLM-5 model, highlighting its ability to generate end‑to‑end system designs, write and debug backend code, and solve large‑scale engineering problems through detailed prompts, positioning it alongside GPT‑5.3 and Claude Opus in the competitive LLM landscape.
GLM-5, the new 744‑billion‑parameter open‑source LLM, expands on GLM‑4.5 with GlmMoeDsa architecture, achieves higher HLE benchmark scores than Claude Opus 4.5, demonstrates strong long‑context and agent capabilities, supports vLLM/SGLang, runs on various Chinese chips, and can directly generate Office documents.
GLM-5, a new large language model with 744 B parameters and 28.5 T tokens of training data, introduces DeepSeek sparse attention and an asynchronous RL system called slime, delivering strong benchmark gains on complex system engineering, long‑horizon agent tasks, and surpassing many open‑source competitors.
The GLM-5 architecture, uncovered from a GitHub PR, doubles the previous model to 745 B parameters, adopts DeepSeek‑V3 sparse attention and multi‑token prediction, features a 78‑layer MoE with 256 experts, supports a 202K‑token context window, and its rumored test model "Pony Alpha" sparked a 60% rise in Zhipu AI's stock amid a crowded AI release season.
An anonymous OpenRouter model dubbed "Pony Alpha" was verified as the new 745B‑parameter GLM-5, which reuses DeepSeek‑V3 architecture, supports sparse attention and multi‑token prediction, and has already caused a near‑40% jump in Zhipu AI’s stock while hinting at upcoming integration into the Transformers library.
