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点属于高峰期，其他时间只消耗 50% 积分。双节期间相当于全天都按这个倍率计算。\n同时，刚上线的 GLM-5.3-FlashX 开始向 Coding Plan 用户滚动开放试用。用户提交申请并通过后，可以体验两周。FlashX 最高输出速度为 200 tokens\u002Fs，但消耗额度是普通 Flash 的 2.5 倍。",1790062560000,"Tue Sep 22 2026 07:36:00 GMT+0000 (Coordinated Universal Time)",{"id":27,"title":28,"content":29,"publishTime":30,"important":9,"sourceName":10,"language":18,"dateString":31},196142,"DFlash团队进军Mac，27B模型跑到144Token\u002Fs","动察 Beating AI 快讯，Inco AI 开源本地推理引擎 Splash，把 Qwen3.8-27B 在 M5 Max MacBook Pro 上跑到了最高 144 Token\u002Fs。LM Studio 随即接入，0.4.25 版本已经可以直接使用。\nInco 此前做出的 DFlash 已经进入 SGLang、vLLM、TensorRT-LLM 和 llama.cpp。Meta、NVIDIA、小米、Poolside 等也给自家模型配了 DFlash 加速小模型。DFlash 会先让小模型并行猜出多个 Token，再交给大模型批量验证，减少逐 Token 生成的计算。\nSplash 把这种优化扩到了整个本地推理引擎。它目前只支持 Qwen3.8-27B 和 Qwen3.6-35B-A3B，每个模型都单独配了 GPU kernel、内存方案和 DFlash 2 小模型。覆盖的模型少，但换来了更激进的性能优化。\n144 Token\u002Fs 是 M5 Max 上的最高演示值。换到 Inco 用来做横向测试的 48GB M5 Pro，Qwen3.8-27B 单路短上下文达到 74 Token\u002Fs；4 路并发时总吞吐达到 170 Token\u002Fs，是第二名的 3.9 倍。这也是 Splash 更适合 Agent 场景的地方。一个 Agent 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持续执行。它会跟踪任务进度，做完后再主动回来告诉你结果。",1789704180000,"Fri Sep 18 2026 04:03:00 GMT+0000 (Coordinated Universal Time)",{"id":39,"title":40,"content":41,"publishTime":42,"important":9,"sourceName":43,"language":18,"dateString":44},192969,"DeepSeek V4.1 Flash第三方实测：没夺回国模第一，但成本只有Kimi、GLM七分之一","动察 Beating AI 快讯，Artificial Analysis 完成 DeepSeek V4.1 Flash 独立测试，综合智能指数拿到 40 分。它已经超过自家 V4 Pro 0813 的 36 分，但仍低于 Kimi K3 的 44 分和 GLM-5.3 的 45 分。按这套第三方榜单，DeepSeek 还没夺回国产模型第一。 但价格差距非常夸张。V4.1 Flash 跑完一个 Intelligence Index 任务平均只花 0.27 美元，Kimi K3 和 GLM-5.3 都在 2 美元左右，相差约 7 倍。输出速度达到 197 Token\u002Fs，也远高于 Kimi K3 的约 36 Token\u002Fs 和 GLM-5.3 的约 58 Token\u002Fs。 Agent 依然是它最强的部分。AutomationBench-AA 上，V4.1 Flash 拿到 69%，追平 GPT-6 Astra，并超过 GLM-5.3 的 62%；长上下文测试 AA-LCR 也拿到 84%。缺点是它特别能说，AA 测试中单任务平均输出约 8.9 万 Token，比 V4 Pro 多 62%，吃掉了一部分价格优势。",1789103640000,"MarsBit","Fri Sep 11 2026 05:14:00 GMT+0000 (Coordinated Universal Time)",{"id":46,"title":47,"content":48,"publishTime":49,"important":9,"sourceName":10,"language":18,"dateString":50},192308,"DeepSeek开始模型和Agent一起训：V4.1 Flash为Harness专项优化","动察 Beating AI 快讯，DeepSeek Harness v0.1.5 发布。V4.1 Flash 在训练阶段就针对 Harness 做了专项训练和优化，覆盖标准、PTC 和极简三种模式。Harness 已经直接进入模型训练环节，不再只是模型发布后再接一个 Agent 外壳。\n新版还加入实验性的 Agent Teams。父子 Agent 可以双向通信，主 Agent 能给不同子 Agent 指定模型和推理强度；多个 Agent 还能共享任务、互相发消息，最后由主 Agent 汇总。\nV4.1 Flash 还有一个很特别的长任务优化：Agent 跑到一半修改系统提示词，也可以尽量保留已有 KV Cache。长任务不用因为前面的规则改了一点，就把后面大段上下文重新计算。\nWeb 端也加入文件上传和预览，支持 PDF、图片、代码等格式。插件可以往左右 Sidebar 和内容区接入自己的 UI，长会话的加载、恢复和内存占用也一起做了优化。",1789022580000,"Thu Sep 10 2026 06:43:00 GMT+0000 (Coordinated Universal 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