[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-nano-banana-2-1-halves-image-price":3,"topics-all":38,"news-related-90606043-6747-4b31-b6a5-76d38c649d1c":57},{"id":4,"title":5,"summary":6,"content":7,"original_url":8,"source_id":9,"tags":10,"translations":24,"news_slug":31,"published_at":32,"created_at":33,"modified_at":34,"is_published":35,"publish_type":36,"image_url":14,"view_count":37},"90606043-6747-4b31-b6a5-76d38c649d1c","Nano Banana 2.1:价格砍半,跑分压过Pro","Google 10 月 6 日将图像模型 Nano Banana 2.1 推到 GA,底座为 Gemini 3.6 Flash,价格较上代砍半:1K 图 3.36 美分、4K 7.56 美分。基准多角色一致性 1106 超过 Pro,但实测出图 Pro 仍占优。旧模型 10 月 29 日关停。","10 月 6 日,Google 把图像生成模型 Nano Banana 2.1 直接推到 GA,没有预览期。它替换的是今年 2 月发布的 Nano Banana 2(API 名 gemini-3.1-flash-image),官方口径是「全面改进」:视觉质量、提示词遵循、多轮角色一致性、文字渲染都有提升,底座换成了 Gemini 3.6 Flash——比 Pro 线还在用的 3.1 更新。\n\n## 价格先砍一半\n\n这次最硬的变化是价格。1K 图从 6.70 美分降到 3.36 美分,4K 从 15.10 美分降到 7.56 美分,降幅约一半;2K 档 5.04 美分。作为对照,Nano Banana Pro 的 1K 图要 13.40 美分,4K 要 24.00 美分——一张 4K 图现在约只要 Pro 三分之一的价格。Flash 级速度不变,分辨率支持 1K\u002F2K\u002F4K(默认 1K)。\n\n## 能力:14 张参考图和三档思考\n\n官方文档列出的更新点相当实在:多图融合最多支持 14 张参考图,其中角色一致性覆盖 4 个角色、物体保真覆盖 10 个物体;修复了 2K\u002F4K 下超宽画幅(1:4、1:8 这类全景比例)的拼接伪影;信息图布局和文字渲染精度提升;还接入了 Google 网页与图片搜索做 grounding。Thinking 分 minimal\u002Fmedium\u002Fhigh 三档(默认 medium),输入上限 131,072 token。\n\n## 跑分赢了 Pro,但别急着下结论\n\nGoogle 模型卡的基准里,2.1(开思考)整体偏好 1050,高于 Nano Banana 2 的 990 和 Pro 的 935;信息图准确率 0.521,接近 Gemini 3.1 Flash Image(0.179)的三倍;多角色一致性 1106,同样压过 Pro 的 1011。但 The Decoder 的实测提醒:上一代 NB2 在同类测试里也追平过 Pro,实际出图 Pro 的色彩和比例仍然更好,2.1 在尺度感上会翻车——把马画成了小马驹。Google 自己的定位也只是 Pro 的「高效平替」。\n\n## 所以呢\n\n旧模型 gemini-3.1-flash-image 将在 10 月 29 日关停,存量用量只剩三周迁移窗口。Google 的打法已经很清楚:用 Flash 级成本把图像生成做成默认基建——Gemini app、搜索 AI Mode、AI Studio、Flow、Stitch、Google Ads、Gemini Enterprise 全线铺开,Pro 留给高端预算。当 4K 图降到 7.56 美分,「一张图值多少钱」的答案正在被改写:图像生成正在从产品卖点变成水电煤。\n\n参考:[Google 官方模型文档](https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fmodels\u002Fgemini-nano-banana-2.1) · [The Decoder 报道](https:\u002F\u002Fthe-decoder.com\u002Fgoogles-new-image-model-nano-banana-2-1-generates-better-images-for-less-money\u002F)","https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fmodels\u002Fgemini-nano-banana-2.1","9b7de8ba-5138-4b63-afc5-20fa8c93e021",[11,15,18,21],{"id":12,"name":13,"slug":13,"description":14,"color":14},"a9524a82-a7c5-4daa-bb4b-a7ee77bb0b94","gemini",null,{"id":16,"name":17,"slug":17,"description":14,"color":14},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":19,"name":20,"slug":20,"description":14,"color":14},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":22,"name":23,"slug":23,"description":14,"color":14},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image",[25],{"id":26,"lang":27,"title":28,"summary":29,"content":30},"c05c0302-b428-41f1-a740-662505d6f7ff","en","Nano Banana 2.1: Half the Price, Beats Pro on Paper","Google's Nano Banana 2.1 hit GA Oct 6 on Gemini 3.6 Flash, halving image prices to 3.36 cents\u002F1K. Benchmarks top Pro; hands-on tests still favor Pro.","On October 6, Google pushed its image generation model Nano Banana 2.1 straight to general availability, skipping any preview. It replaces Nano Banana 2 (API name gemini-3.1-flash-image), released this February, with what Google calls across-the-board improvements: visual quality, prompt adherence, multi-turn character consistency, and text rendering all get better, and the base moves to Gemini 3.6 Flash — newer than the 3.1 that the Pro line still uses.\n\n## Prices cut in half first\n\nThe hardest change this time is price. A 1K image drops from 6.70 cents to 3.36 cents, a 4K from 15.10 cents to 7.56 cents — roughly half; the 2K tier sits at 5.04 cents. For comparison, Nano Banana Pro charges 13.40 cents per 1K image and 24.00 cents per 4K — a 4K image now costs about a third of Pro's price. Flash-tier speed is unchanged, with 1K\u002F2K\u002F4K resolutions supported (1K default).\n\n## Capabilities: 14 reference images and three thinking levels\n\nThe official documentation lists substantive updates: multi-image fusion now takes up to 14 reference images, with character consistency covering 4 characters and object fidelity covering 10 objects; tiling artifacts on ultra-wide aspect ratios (1:4, 1:8 panoramas) at 2K and 4K are fixed; infographic layout and text rendering accuracy are improved; and grounding with Google Web and Image Search is built in. Thinking comes in minimal\u002Fmedium\u002Fhigh levels (medium by default), with an input limit of 131,072 tokens.\n\n## Beats Pro in benchmarks, but hold your conclusions\n\nIn Google's model card benchmarks, 2.1 (with thinking) scores 1050 on overall preference, above Nano Banana 2's 990 and Pro's 935; infographic accuracy hits 0.521, nearly triple Gemini 3.1 Flash Image's 0.179; multi-character consistency reaches 1106, also beating Pro's 1011. But The Decoder's hands-on test offers a caveat: the previous NB2 also matched Pro in similar tests, while in practice Pro still delivers better color and proportions, and 2.1 stumbles on scale — drawing a horse more like a pony. Google's own positioning is merely Pro's \"more efficient counterpart.\"\n\n## So what\n\nThe old gemini-3.1-flash-image model shuts down on October 29, leaving existing workloads a three-week migration window. Google's playbook is now clear: use Flash-tier costs to turn image generation into default infrastructure — rolling out across the Gemini app, AI Mode in Search, AI Studio, Flow, Stitch, Google Ads, and Gemini Enterprise, with Pro reserved for premium budgets. When a 4K image costs 7.56 cents, the answer to \"what is an image worth\" is being rewritten: image generation is turning from a product selling point into a utility.\n\nReferences: [Google official model docs](https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fmodels\u002Fgemini-nano-banana-2.1) · [The Decoder report](https:\u002F\u002Fthe-decoder.com\u002Fgoogles-new-image-model-nano-banana-2-1-generates-better-images-for-less-money\u002F)","nano-banana-2-1-halves-image-price","2026-10-08T07:10:00Z","2026-10-08T07:14:55.880447Z","2026-10-08T07:14:55.880463Z",true,"agent",80,[39,48],{"slug":40,"tag_slug":40,"title_zh":41,"title_en":42,"intro_zh":43,"intro_en":44,"id":45,"is_active":35,"created_at":46,"modified_at":47},"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":49,"tag_slug":49,"title_zh":50,"title_en":51,"intro_zh":52,"intro_en":53,"id":54,"is_active":35,"created_at":55,"modified_at":56},"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":58},[59,64,69,74,79,84],{"id":60,"title":61,"news_slug":62,"published_at":63},"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":65,"title":66,"news_slug":67,"published_at":68},"d219ead5-ea5d-4aaa-835e-5ec3cf980856","Gemini Live Avatar:模型直出唇同步数字人","gemini-3-8-live-avatar","2026-09-26T17:06:00+00:00",{"id":70,"title":71,"news_slug":72,"published_at":73},"5de53eff-f921-49ac-9e11-5849b4462295","Gemini 4 进入后训练,谷歌跳过 3.5 Pro","gemini-4-post-training","2026-09-24T13:20:00+00:00",{"id":75,"title":76,"news_slug":77,"published_at":78},"bdb6361a-d1da-45bb-8744-cb6922077e14","谷歌Gemini 3.8双TTS:30秒克隆,逐行导演","gemini-3-8-flash-tts-voice-design","2026-09-23T19:10:29+00:00",{"id":80,"title":81,"news_slug":82,"published_at":83},"8303f420-b3f1-485a-aa6f-7775256c84a7","Gemini 3.8 Audio 双发:Live 和 Extended Thinking 把思考+说话压到近实时","gemini-3-8-audio-live-extended-thinking","2026-09-16T03:00:00+00:00",{"id":85,"title":86,"news_slug":87,"published_at":88},"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"]