Z.ai has open-sourced its Ox Alpha large language model under the official name GLM-5.3-Flash, boasting ten times greater cost-efficiency than its predecessor.
2 editorial reports · 4 verified social mentions. The most authoritative report leads while later evidence completes the story.
Megathread for discussing the release of GLM-5.3-Flash. Quants Fine-Tunes & Abliterations Chat Templates Inference Server Support & Configuration Experiences, Benchmarks & Model Comparisons We'll try to clean up future duplicates around the release and point them here. Highlights GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series, and the first open-weight release of the glm5_next architecture. Z.ai's pitch: outperforms GLM-5.2 at one-tenth the price while approaching Claude Opus 4.8 on coding and agentic benchmarks. It introduces: Hybrid Sparse + Linear Attention: 45 layers laid out as repeating blocks of 3x KDA linear attention followed by 1x DeepSeek-style sparse attention (34 linear / 11 sparse layers). The sparse layers use a lightning indexer (32 heads, dim 128)
Open mention🤖 Thank you for participating in the Stealth Ox Alpha testing period. Thank you for participating in the Stealth Ox Alpha testing period. This model was ZAI's GLM-5.3 Flash. Use it now: https://openrouter.ai/z-ai/glm-5.3-flash submitted by /u/paulrich_nb [link] [comm... 📰 Source: Artificial Intelligence (AI) 🔗 Link: https://www.reddit.com/r/artificial/comments/1vz32jh/thank_you_for_participating_in_the_stealth_ox/ # AI # ArtificialIntelligence
Open mention📊 Alibaba previews Qwen4 architecture Alibaba released Qwen3.8-Flash-Next, an open multimodal model and early preview of the architecture planned for Qwen4. It has 125B parameters, but uses only 6B for each text fragment. Another 51B sit in separate memory for frequent combinations. The design keeps the model large while reducing compute. Alibaba says training is about 9 times cheaper than Qwen3.7-Plus, while the model already performs better on coding, tool-use tasks, and long office workflows. Long-context support reaches 262,000 tokens by default and up to 1 million tokens in extended mode. At 1 million tokens, a new retrieval mechanism can significantly speed up processing. The weights are already open on Hugging Face. 📊@tech
Open mention🔖 A comprehensive resource on LLMs in one repository We found an open-source course covering Transformers, LoRA, RAG, prompts, model editing, and other key topics. After each chapter, you can immediately access the original sources – the authors have compiled papers and collections from arXiv. ⛓Link to GitHub https://github.com/ZJU-LLMs/Foundations-of-LLMs
Open mention