📝 摘要
✍️ 编辑摘要
这条资讯的核心议题是“not much happened today”。
从当前聚合摘要看，最值得先关注的是：DeepSeek launched V4.1-Flash, a new open-weight flagship model focused on extreme inference efficiency and low cost, featuring a 763B total-parameter causal encoder-decoder architecture with 8B active input and 16B active output parameters and 1M-token context. It scored 40 on the Artificial Analysis Intelligence Index, outperforming its predecessor and ranking just below GLM-5.3-Flash. The model supports text and image input, is available under an MIT license, and is accessible via US/API. The architecture introduces a novel causal encoder-decoder design aimed at reducing active compute and KV/cache costs, with a hybrid sparse/local approach and a unique vision encoder differing from recent Chinese models. Early layers use a SWA-only pattern, and the model has an effective depth of about 40 layers with 20 decoder layers. Baseten and Ollama have begun supporting and rolling out the model to users.。
如果你只看一遍，这条新闻与后续判断最相关的点是：这条资讯围绕“not much happened today”展开，建议结合来源列表和相关话题继续跟踪后续进展。
📌 关键信息
- DeepSeek launched V4.1-Flash, a new open-weight flagship model focused on extreme inference efficiency and low cost, featuring a 763B total-parameter causal encoder-decoder architecture with 8B active input and 16B active output parameters and 1M-token context. It scored 40 on the Artificial Analysis Intelligence Index, outperforming its predecessor and ranking just below GLM-5.3-Flash. The model supports text and image input, is available under an MIT license, and is accessible via US/API. The architecture introduces a novel causal encoder-decoder design aimed at reducing active compute and KV/cache costs, with a hybrid sparse/local approach and a unique vision encoder differing from recent Chinese models. Early layers use a SWA-only pattern, and the model has an effective depth of about 40 layers with 20 decoder layers. Baseten and Ollama have begun supporting and rolling out the model to users.