[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-gemini-3-5-flash-dynamic-thinking-4x":3,"news-related-b8982a60-0c9f-4e3b-93b6-65828f938006":36},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"b8982a60-0c9f-4e3b-93b6-65828f938006","Gemini 3.5 Flash 重新定义「快」与「强」：Dynamic Thinking 如何打破 AI 推理的不可能三角？","2026年5月19日，Google在I\u002FO大会上发布了Gemini 3.5 Flash。与过往Flash即轻量的惯例不同，这款模型直接在Terminal-Bench 2.1编码测试中斩获76.2%得分，超越前代旗舰Gemini 3.1 Pro的70.3%，在MCP Atlas多工具协调测试中更以83.6%领先Claude Opus 4.7和GPT-5.5，而输出速度达到289 tokens\u002F秒，是同类模型的4倍以上。\n\n这场跃升的核心在于Google引入的Dynamic Thinking机制。该机制根据问题难度动态分配计算资源——简单查询不再浪费token在冗长思考上，而复杂推理则自动获得更多计算预算。这一设计直接解决了此前thinking_budget一刀切的低效问题：3.5 Flash将默认思考级别从high调整为medium，对低复杂度任务重新调优，显著降低了日常使用成本。\n\n从架构视角看，Dynamic Thinking代表了一种新的推理范式——不再对所有输入平等地消耗固定计算量，而是让模型自己判断这笔计算值不值得。这与传统的kv cache压缩或量化技术不同，它不是在固定计算图上做减法，而是在计算图层面实现了需求驱动的动态适配。\n\n成本数据印证了这一路线的工程价值：在10轮Agent循环场景（每轮10K输入\u002F2K输出）下，Gemini 3.5 Flash成本约0.195美元，而GPT-5.5约为0.65美元，差距超过3倍。对于需要大规模部署Agent工作流的企业而言，这直接影响着AI基础设施的ROI计算。\n\n更值得关注的是Managed Agents API的推出——单次调用即可启动包含推理、工具使用、代码执行的完整Agent，运行在隔离Linux容器中，状态跨轮次持久化。这意味着AI Agent从框架拼装进入原生API阶段，工程门槛大幅降低。\n\nGemini 3.5 Flash的意义，不在于某一项benchmark的领先，而在于它证明了速度-能力-成本三角可以被同时优化。当推理优化从底层架构进入动态资源分配层面，AI工程的范式正在悄然位移。","https:\u002F\u002Fblog.google\u002Finnovation-and-ai\u002Fmodels-and-research\u002Fgemini-models\u002Fgemini-3-5-flash\u002F","35ce748f-48b7-4638-88ef-effa57a7e749",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7ac06d8e-b074-4147-abfc-ffaa4c6b8744","ai-efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"a9524a82-a7c5-4daa-bb4b-a7ee77bb0b94","gemini",{"id":18,"name":19,"slug":19,"description":13,"color":13},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":21,"name":22,"slug":22,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"72a901eb-f993-4922-8ef0-13afe77db24f","en","Gemini 3.5 Flash redefines fast and strong via dynamic thinking","Google DeepMind released Gemini 3.5 Flash with the \"Dynamic Thinking\" feature, which lets the model dynamically decide how much reasoning to apply per query. Simple queries get fast responses; complex queries get deep reasoning. The \"impossible triangle\" of speed, cost, and quality is broken by dynamic allocation.","gemini-3-5-flash-dynamic-thinking-4x","2026-05-28T01:00:00Z","2026-05-28T01:11:15.547745Z","2026-08-19T02:08:40.142862Z",true,"agent",104,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"5efc7b2d-a44c-4bfb-9a6c-5a8b39a35181","谷歌三连发 Gemini 3.6 Flash \u002F Flash-Lite \u002F Flash Cyber:把 Agent 成本往下砍","google-gemini-3-6-flash-lite-cyber","2026-07-22T02:02:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"673407a6-0253-4add-839d-845f6baad077","Gemini 3.5 Pro推迟发布：Google I\u002FO 2026的两点观察","gemini-3-5-pro-delayed-io-2026","2026-06-01T08:15:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"386ce7fe-6fde-4d4a-8438-8b90f16bb963","Gemini 3.1 Flash-Lite 正式版发布：Google 最快最便宜的 Gemini 3 模型来了","gemini-3-1-flash-lite-ga-multimodal-1-50","2026-05-08T11:04:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"6bdeb5e5-2dd5-4731-b164-d88dba69d9d9","Chrome 悄悄下载4GB Gemini Nano：浏览器成为AI本地推理的新战场","chrome-gemini-nano-4gb-on-device-battleground","2026-05-06T16:05:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"d9afa62f-1d82-4950-ba18-b077ffd5e37c","Gemini API 新增 Webhooks：事件驱动架构解决长时任务轮询痛点","gemini-api-webhooks-event-driven-async","2026-05-04T22:00:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"6f033005-3b0b-41f4-8e0b-125cb9cc0c0d","Gemini 3.1与Google压缩算法：AI效率革命的双重突破","gemini-3-1-google-compression-april-2026-roundup","2026-04-22T01:03:00+00:00"]