[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-gemini-3-1-flash-lite-ga-multimodal-1-50":3,"topics-all":36,"news-related-386ce7fe-6fde-4d4a-8438-8b90f16bb963":55},{"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},"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","**Google 于 5 月 7 日正式推出 Gemini 3.1 Flash-Lite 通用版本**，这是 Gemini 3 系列中速度最快、成本最低的模型，标志着 Google 在高效推理赛道上的最新落子。\n\n## 定位：速度与成本的极致平衡\n\nFlash-Lite 专为对延迟敏感、并发量大的企业场景打造，涵盖软件工程、客服、创意工具和金融等高实时性领域。Google 披露，该模型在分类任务上实现亚秒级响应，在高并发压力下 p95 延迟约为 1.8 秒，相较前代产品有显著提升。\n\n## 多模态能力落地\n\n值得注意的是，Flash-Lite 是 Gemini 3 系列中首款支持多模态（文本 + 图像）的 Lite 级别模型，支持工具调用（tool calling）和编排（orchestration）等 Agent 能力，标志着轻量级模型也能承载复杂 Agent 工作流。\n\n## 定价：再次拉低大模型使用门槛\n\nFlash-Lite 的定价为每百万输入 tokens 0.25 美元、每百万输出 tokens 1.50 美元，延续了 Google 近年来在高效率模型上持续压缩成本的策略。这也是 Google 面向大规模企业部署给出的最低单价方案。\n\n## 行业影响\n\nJetBrains、Gladly、Ramp 等企业已率先在生产环境中采用。Google 此番将 Flash-Lite 推至 GA（正式发布），既是对 Preview 阶段用户反馈的回应，也预示着今年 I\u002FO 大会上 Gemini 3.2 Flash 等更高端型号即将面世——Flash 系列正在成为 Google 覆盖企业需求的主力价格锚点。","https:\u002F\u002Fcloud.google.com\u002Fblog\u002Fproducts\u002Fai-machine-learning\u002Fgemini-3-1-flash-lite-is-now-generally-available","93669252-6081-4192-bf76-3a6814fc60cf",[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},"01598627-1ea6-4b27-a5d8-874971571a71","llm",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"c2214db0-a434-4d34-a7ac-0d7e2a0336b1","en","Gemini 3.1 Flash-Lite GA: the fastest, cheapest Gemini 3","**Google officially launched Gemini 3.1 Flash-Lite GA on May 7**, the fastest and lowest-cost model in the Gemini 3 series, marking Google's latest move in the high-efficiency inference race.\n\n## Positioning: The Ultimate Speed-Cost Balance\n\nFlash-Lite is purpose-built for latency-sensitive, high-concurrency enterprise scenarios, covering software engineering, customer service, creative tools, and finance. Google disclosed that the model achieves sub-second response on classification tasks, with p95 latency of about 1.8 seconds under high concurrency, a significant improvement over the previous generation.\n\n## Multimodal Capability Landing\n\nNotably, Flash-Lite is the first Lite-tier model in the Gemini 3 series to support multimodality (text + image), along with tool calling and orchestration Agent capabilities, marking that lightweight models can also carry complex Agent workflows.\n\n## Pricing: Lowering the LLM Bar Again\n\nFlash-Lite is priced at $0.25 per million input tokens and $1.50 per million output tokens, continuing Google's strategy of continuously compressing cost on high-efficiency models. This is also Google's lowest single-price solution for large-scale enterprise deployment.\n\n## Industry Impact\n\nCompanies including JetBrains, Gladly, and Ramp have been early adopters in production environments. Google's move to push Flash-Lite to GA is both a response to Preview-stage user feedback and a sign that more high-end models like Gemini 3.2 Flash are about to debut at this year's I\u002FO — the Flash series is becoming Google's anchor point for covering enterprise demand at various price points.","gemini-3-1-flash-lite-ga-multimodal-1-50","2026-05-08T11:04:00Z","2026-05-08T19:04:26.725264Z","2026-08-19T02:08:40.142862Z",true,"agent",292,[37,46],{"slug":38,"tag_slug":38,"title_zh":39,"title_en":40,"intro_zh":41,"intro_en":42,"id":43,"is_active":33,"created_at":44,"modified_at":45},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":47,"tag_slug":47,"title_zh":48,"title_en":49,"intro_zh":50,"intro_en":51,"id":52,"is_active":33,"created_at":53,"modified_at":54},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":56},[57,62,67,72,77,82],{"id":58,"title":59,"news_slug":60,"published_at":61},"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",{"id":63,"title":64,"news_slug":65,"published_at":66},"f093935e-e23c-4766-88d6-9302a370be2a","Gemini 套餐洗牌:免费版限 Flash-Lite","gemini-deep-think-ai-pro-tier-restructure","2026-10-09T00:00:00+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"260aa193-a883-4db9-9e05-7bf3c6f14f2f","Gemini 4 Argon 发布:输出上限 100 万 token,首批给安全防御者","gemini-4-argon-release","2026-10-01T13:11:08+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"039ff515-68e7-4f11-866a-1da97e26eb45","Gemini 3.8 Live 拿下 S2S 实时语音榜第一","gemini-3-8-live-voice-s2s-number-one","2026-09-15T17:00:00+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"e0a484e4-41e0-4f91-9b9b-a196bbdcf3ba","Gemini 3.8 Flash 双发:同价升级 + Cyber 走可信项目 Fairwind","gemini-3-8-flash-cyber-fairwind-launch","2026-09-03T03:00:00+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"7b9cdf6e-5ef0-4ece-ab6c-e8cec1b02397","Google 重组 DeepMind 领导层,Gemini 研发提速应对 Anthropic 与 OpenAI 竞争","google-deepmind-reshuffle-gemini-speed","2026-08-25T07:00:00+00:00"]