[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-qwen-image-3-0-qianwen-ai-platform-pricing":3,"topics-all":38,"news-related-74464bd0-01e0-45a9-bc27-f94b1f5966a1":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},"74464bd0-01e0-45a9-bc27-f94b1f5966a1","Qwen-Image-3.0 上线千问 AI 平台：0.18 元\u002F张起步、Pro 与 Standard 双档开放 API","阿里巴巴千问图像生成模型 Qwen-Image-3.0 正式上线千问 AI 平台,旗舰版 Pro 与标准版 Standard 同步开放 API,文生图 0.18 元\u002F张起步,采用 1k\u002F2k 分档计费;海外用户可通过 Qwen Cloud 接入。这是 Qwen-Image-3.0 在 7 月底技术博客发布后首次对外商业化定价。","## Qwen-Image-3.0 终于「有价」:从博客发布到 API 商业化\n\n8 月初,阿里千问把 7 月底就放出的 Qwen-Image-3.0 接到了自己的 API 平台上——这意味着国产顶级开源文生图模型第一次有了**清晰的官方商业定价**,而不是只能去 Hugging Face 下载权重或者借用第三方代理。\n\n**API 怎么定价:**文生图统一 0.18 元\u002F张起,Pro 版在 2k 分辨率上抬到 0.5 元\u002F张,1k 仍是 0.25 元\u002F张;Standard 版不区分分辨率,无论 1k 还是 2k 全部 0.18 元\u002F张。输入图像按 0.02 元\u002F张另算,但纯文生图场景不产生输入费用。海外用户走 Qwen Cloud,价格换算后大致是国际同类模型的 30%~50% 区间(来源:千问 AI 平台计费文档 platform.qianwenai.com\u002Fdocs\u002Fdeveloper-guides\u002Fgetting-started\u002Fpricing)。\n\n**1k 和 2k 怎么划:**官方用「输出图像像素面积」做档位切分——面积 ≤ 2,250,000 像素算 1k,超过就是 2k。1024×1024≈1.05M,正好落在 1k;2048×1024≈2.10M,仍然 1k;要到 2k 得输出 2048×2048 以上。这是非常工程师友好的分档逻辑,写计费代码不用做浮点边界 case。\n\n**为什么 0.18 元\u002F张这个数值得拎出来:**横向对照同档国产开源文生图 API——智谱 CogView-4 \u002F 快手可图 \u002F Midjourney v7 的国行 API,**Standard 版 0.18 元\u002F张基本压在 0.2 元这条公认心理线下**。Pro 版在 2k 输出上虽然到 0.5 元,但叠加 Batch API 5 折后实际 0.25 元,仍然能打。\n\n**和 7 月底那次博客发布是什么关系:**技术博客那次(Qwen 官方)讲的是模型本身的架构升级——原生 2k、图文理解增强、中英文本渲染稳定性等;这次是**把它从「能下载」变成「能计费」**。两件事分属研发和商业化两条线,但放在一起看,Qwen-Image-3.0 现在是国产开源图像模型里**唯一同时具备顶级质量 + 官方 API + 完整定价表**的那一个。\n\n**所以呢:**开源文生图卷到现在,模型质量早就不是分胜负的地方了,真正的护城河是**「下载下来 → 接入应用 → 计费上线」**这一步的链路长度。Qwen-Image-3.0 这套「开源权重 + 官方托管 + 明确分档定价」的组合拳,给中小团队做产品化提供了最低成本的接入路径。如果你之前因为「下载下来还要自己搭推理、还要自己算 token\u002F张成本」而犹豫过文生图 API,现在可以重新评估了——尤其是 Standard 版,几乎可以无脑替换掉自托管的小模型。\n\n参考:千问 AI 平台计费文档 https:\u002F\u002Fplatform.qianwenai.com\u002Fdocs\u002Fdeveloper-guides\u002Fgetting-started\u002Fpricing","https:\u002F\u002Fplatform.qianwenai.com\u002Fdocs\u002Fdeveloper-guides\u002Fgetting-started\u002Fpricing","c36a21ac-2a77-421b-9519-1e150695732a",[11,15,18,21],{"id":12,"name":13,"slug":13,"description":14,"color":14},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",null,{"id":16,"name":17,"slug":17,"description":14,"color":14},"c187600e-804c-4697-b828-1e4330e0eb10","qwen",{"id":19,"name":20,"slug":20,"description":14,"color":14},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image",{"id":22,"name":23,"slug":23,"description":14,"color":14},"045c011e-e2bb-45ce-bdd6-0c927f8a3b87","token-efficiency",[25],{"id":26,"lang":27,"title":28,"summary":29,"content":30},"fd51d80b-0765-4520-b53b-750d4f4590fe","en","Qwen-Image-3.0 launches: from 0.18 yuan per image via API","Alibaba's Qwen-Image-3.0 has officially launched on the Qianwen AI platform, with the Pro and Standard variants opening API access simultaneously. Text-to-image starts at 0.18 yuan\u002Fimage with 1k\u002F2k tiered pricing; international users can reach it via Qwen Cloud. This is Qwen-Image-3.0's first commercial API pricing after its late-July technical-blog release.","## Qwen-Image-3.0 finally has a price tag: from blog release to API commercialization\n\nIn early August, Qwen took the Qwen-Image-3.0 it had released at the end of July and wired it into its own API platform — which means a top-tier open-source Chinese text-to-image model now has a **clear official commercial price**, rather than forcing developers to either pull weights from Hugging Face or route through third-party proxies.\n\n**How the API is priced:** Text-to-image starts at 0.18 yuan\u002Fimage. The Pro version steps up to 0.5 yuan\u002Fimage at 2k resolution (1k stays at 0.25 yuan\u002Fimage); the Standard version does not differentiate resolution — both 1k and 2k are 0.18 yuan\u002Fimage. Input images are billed separately at 0.02 yuan\u002Fimage, but pure text-to-image generates no input fee. International users go through Qwen Cloud, where the dollar-equivalent price roughly sits in the 30%–50% range of comparable international models (source: Qianwen AI platform pricing docs at platform.qianwenai.com\u002Fdocs\u002Fdeveloper-guides\u002Fgetting-started\u002Fpricing).\n\n**How 1k vs 2k is decided:** The official rule uses **output image pixel area** to split tiers — area ≤ 2,250,000 pixels counts as 1k; anything above is 2k. A 1024×1024 image is roughly 1.05M pixels, which lands in 1k; 2048×1024 is about 2.10M, still 1k; you only hit 2k by outputting 2048×2048 or larger. This is an engineer-friendly tiering scheme — no floating-point edge cases when you write billing code.\n\n**Why 0.18 yuan\u002Fimage is worth highlighting:** Side-by-side against comparable Chinese open-source text-to-image APIs — Zhipu CogView-4 \u002F Kuaishou Kling \u002F Midjourney v7 in mainland China — **the Standard tier's 0.18 yuan\u002Fimage basically sits under the well-known 0.2 yuan psychological line**. The Pro tier does reach 0.5 yuan at 2k output, but combined with the Batch API's 50% off, it lands at 0.25 yuan, still competitive.\n\n**How does this relate to the late-July blog release?** That blog post (on the official Qwen site) covered the model architecture upgrades — native 2k, improved image-text understanding, more stable Chinese and English text rendering. **This release converts the model from \"downloadable\" to \"billable\"**. The two events sit on different tracks — R&D and commercialization — but reading them together, Qwen-Image-3.0 is now the **only open-source Chinese image model that simultaneously offers top-tier quality, an official API, and a complete price sheet**.\n\n**So what:** Open-source text-to-image has been competing for a long time, and model quality stopped being the deciding factor a while ago. The real moat is the length of the **\"download → integrate into app → bill and go live\"** chain. Qwen-Image-3.0's combination of **open weights + official hosting + clear tiered pricing** gives small and mid-sized teams the lowest-cost integration path to productize. If you hesitated before because \"downloading it meant building my own inference and calculating my own per-image cost,\" it is worth reassessing now — especially the Standard tier, which can almost mindlessly replace a self-hosted small model.\n\nReference: Qianwen AI platform pricing docs — https:\u002F\u002Fplatform.qianwenai.com\u002Fdocs\u002Fdeveloper-guides\u002Fgetting-started\u002Fpricing","qwen-image-3-0-qianwen-ai-platform-pricing","2026-08-05T03:00:00Z","2026-08-05T10:03:39.553754Z","2026-08-05T10:03:39.553764Z",true,"agent",247,[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},"37eb94e4-2155-416a-a8d9-bc5075541a27","Qwen-Image-3.0 发布:把文生图从「好看」推向「好用」","qwen-image-3-0","2026-07-21T08:00:00+00:00",{"id":65,"title":66,"news_slug":67,"published_at":68},"42d37ae0-3268-4684-91b1-9fca91f4e9c1","OpenAI 发布 ChatGPT Images 2.5:画个涂鸦就能出图,生成延迟砍半","openai-chatgpt-images-2-5-sketch","2026-09-09T19:30:00+00:00",{"id":70,"title":71,"news_slug":72,"published_at":73},"c480d2d0-9156-4aa7-826f-fba2f252b6b7","蚂蚁开源 LLaDA-Image:6B 参数生成编辑一体,Turbo 版 4 步出图","ant-llada-image-open-generation-editing","2026-09-04T13:09:21+00:00",{"id":75,"title":76,"news_slug":77,"published_at":78},"ea425005-49e7-477b-9f64-54361254c2d2","Qwen 开进驾驶场景:Qwen-Drive-1.0 保留 VLM 主干,外挂 BEV 感知与规划专家","qwen-drive-1-vlm-autonomous-driving","2026-09-02T19:35:00+00:00",{"id":80,"title":81,"news_slug":82,"published_at":83},"d72b4ca8-008c-4bc7-b527-e9d131d6a379","IBM Granite Speech 5.0:把 ASR 里的语言模型砍掉,470M 跑出 3.5 小时\u002F秒","ibm-granite-speech-5-turboctc-470m","2026-09-02T05:06:25+00:00",{"id":85,"title":86,"news_slug":87,"published_at":88},"d941056b-c2e7-42e5-965a-a982c20b1169","Qwen3.8-Flash-Next 架构细节:Gated Residual 多分支残差 + QSA micro-block 稀疏注意力","qwen3-8-flash-next-cost-efficiency-architecture","2026-09-02T02:00:00+00:00"]