阿里巴巴預覽 Qwen3.8-Max:2.4 兆參數多模態模型,緊接月之暗面開源 Kimi K3 之後發布
重點摘要
7 月 19 日,阿里巴巴 Qwen 團隊預覽了新一代旗艦模型 Qwen3.8-Max-Preview。研究團隊稱其為 2.4 兆參數模型,在基準測試中「僅次於 Fable 5」。目前預覽版已上線,但基準測試表、模型卡與授權條款尚未公開。這項公告於上海世界人工智能大會(WAIC)期間發布,距離月之暗面(Moonshot AI)開源 2.8 兆參數模型 Kimi K3 僅兩天。時機選擇與模型本身同樣值得關注。本文釐清阿里巴巴已確認的資訊與僅止於宣稱的內容,以下所有效能數據均須留意此前提。
On July 19, Alibaba’s Qwen team previewed Qwen3.8-Max-Preview, the next flagship in the Qwen family. The research team describes it as a 2.4 trillion-parameter model, ‘second only to Fable 5’ among the systems it benchmarked. The preview is live now. The benchmark table, model card, and license are not. The July 19th 2026 announcement landed during the World AI Conference (WAIC) in Shanghai. It also arrived two days after Moonshot AI released Kimi K3, a 2.8 trillion-parameter open-weight model. The timing is the story as much as the model. This article separates what Alibaba confirmed from what it only claimed. Every performance figure below carries that caveat. What Qwen announced The Qwen account posted that Qwen3.8 is launching and going open-weight soon. It called the model ‘one of the most powerful available today, comparable to leading frontier systems. The preview build is real and purchasable. Access runs through Alibaba’s Token Plan subscription. The preview is offered at 10% of standard pricing. Qwen developer Shuai Bai added technical detail. He described Qwen3.8 as the team’s first multimodal model above 1 trillion parameters. It processes text, images, video, and documents. Alibaba team states the model should beat Qwen3.7-Max on coding, full-stack development, data analysis, and office workflows. Interactive Explainer Qwen3.8 Explainer #mtp-qwen38-explainer *{box-sizing:border-box!important;margin:0;padding:0} #mtp-qwen38-explainer{ background:#0b0b0b!important;color:#e6e6e6!important; font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,Helvetica,Arial,sans-serif!important; border:1px solid #1e1e1e!important;border-radius:14px!important;overflow:hidden!important; max-width:860px;margin:0 auto!important;line-height:1.5; } #mtp-qwen38-explainer .hd{padding:22px 24px 18px;border-bottom:1px solid #1a1a1a;background:linear-gradient(180deg,#111 0%,#0b0b0b 100%)} #mtp-qwen38-explainer .kick{color:#76B900;font-size:11px;letter-spacing:2px;text-transform:uppercase;font-weight:700;margin-bottom:8px} #mtp-qwen38-explainer h2{font-size:21px;color:#fff;font-weight:700;line-height:1.25} #mtp-qwen38-explainer .sub{color:#8a8a8a;font-size:13px;margin-top:7px} #mtp-qwen38-explainer .body{padding:20px 24px 8px} #mtp-qwen38-explainer .tabs{display:flex;gap:8px;margin-bottom:18px;flex-wrap:wrap} #mtp-qwen38-explainer .tab{flex:1;min-width:120px;cursor:pointer;padding:11px 10px;border-radius:9px;border:1px solid #242424;background:#111;color:#bdbdbd;font-size:13px;font-weight:600;text-align:center;transition:.15s} #mtp-qwen38-explainer .tab:hover{border-color:#3a3a3a} #mtp-qwen38-explainer .tab.on{background:rgba(118,185,0,.12);border-color:#76B900;color:#a6e000} #mtp-qwen38-explainer .panel{display:none} #mtp-qwen38-explainer .panel.on{display:block;animation:mtpf .25s ease} @keyframes mtpf{from{opacity:0;transform:translateY(4px)}to{opacity:1;transform:none}} #mtp-qwen38-explainer .row{display:flex;gap:10px;padding:11px 13px;border-radius:9px;background:#111;border:1px solid #1c1c1c;margin-bottom:9px;align-items:flex-start} #mtp-qwen38-explainer .row .ic{flex:0 0 auto;width:20px;height:20px;border-radius:50%;font-size:12px;font-weight:800;display:flex;align-items:center;justify-content:center;margin-top:1px} #mtp-qwen38-explainer .ic.y{background:rgba(118,185,0,.15);color:#76B900} #mtp-qwen38-explainer .ic.n{background:rgba(255,120,90,.13);color:#ff7a5a} #mtp-qwen38-explainer .row .t{font-size:13.5px;color:#dcdcdc} #mtp-qwen38-explainer .row .t b{color:#fff} #mtp-qwen38-explainer .note{font-size:12px;color:#7c7c7c;margin:4px 2px 16px} #mtp-qwen38-explainer .chart{margin:6px 0 6px} #mtp-qwen38-explainer .bar{display:flex;align-items:center;gap:10px;margin-bottom:10px} #mtp-qwen38-explainer .bar .lab{flex:0 0 128px;font-size:12.5px;color:#cfcfcf} #mtp-qwen38-explainer .bar .lab span{display:block;font-size:10.5px;color:#777} #mtp-qwen38-explainer .bar .track{flex:1;background:#151515;border-radius:6px;height:26px;position:relative;overflow:hidden} #mtp-qwen38-explainer .bar .fill{height:100%;border-radius:6px;display:flex;align-items:center;justify-content:flex-end;padding-right:8px;font-size:11.5px;font-weight:700;color:#0b0b0b;transition:width .8s cubic-bezier(.2,.7,.2,1)} #mtp-qwen38-explainer .fill.g{background:linear-gradient(90deg,#4a7500,#76B900)} #mtp-qwen38-explainer .fill.d{background:linear-gradient(90deg,#2c2c2c,#4d4d4d);color:#e6e6e6} #mtp-qwen38-explainer .fill.q{background:linear-gradient(90deg,#5c8f00,#9be000);box-shadow:0 0 14px rgba(118,185,0,.4)} #mtp-qwen38-explainer .calc{background:#0f0f0f;border:1px solid #1e1e1e;border-radius:11px;padding:16px} #mtp-qwen38-explainer .seg{display:flex;gap:6px;margin:10px 0 14px} #mtp-qwen38-explainer .seg button{flex:1;cursor:pointer;padding:9px 6px;border-radius:8px;border:1px solid #262626;background:#141414;color:#bcbcbc;font-size:12.5px;font-weight:600;transition:.15s} #mtp-qwen38-explainer .seg button.on{background:rgba(118,185,0,.14);border-color:#76B900;color:#a6e000} #mtp-qwen38-explainer .out{display:flex;gap:12px;flex-wrap:wrap} #mtp-qwen38-explainer .stat{flex:1;min-width:150px;background:#111;border:1px solid #1c1c1c;border-radius:9px;padding:13px 15px} #mtp-qwen38-explainer .stat .n{font-size:24px;font-weight:800;color:#76B900;line-height:1.1} #mtp-qwen38-explainer .stat .k{font-size:11px;color:#8a8a8a;text-transform:uppercase;letter-spacing:.5px;margin-top:5px} #mtp-qwen38-explainer .warn{margin-top:12px;font-size:12px;color:#e0b050;background:rgba(224,176,80,.08);border:1px solid rgba(224,176,80,.2);border-radius:8px;padding:9px 11px} #mtp-qwen38-explainer .bench{display:grid;grid-template-columns:repeat(3,1fr);gap:10px} #mtp-qwen38-explainer .bcard{background:#111;border:1px solid #1c1c1c;border-radius:9px;padding:13px} #mtp-qwen38-explainer .bcard .bn{font-size:22px;font-weight:800;color:#fff} #mtp-qwen38-explainer .bcard .bl{font-size:11px;color:#8a8a8a;margin-top:3px} #mtp-qwen38-explainer .ft{padding:13px 24px;border-top:1px solid #1a1a1a;display:flex;justify-content:space-between;align-items:center;flex-wrap:wrap;gap:8px} #mtp-qwen38-explainer .ft .mk{font-size:11.5px;color:#76B900;font-weight:700} #mtp-qwen38-explainer .ft .dt{font-size:11px;color:#666} @media(max-width:640px){ #mtp-qwen38-explainer .bench{grid-template-columns:1fr} #mtp-qwen38-explainer .bar .lab{flex:0 0 96px} #mtp-qwen38-explainer h2{font-size:18px} } Marktechpost Interactive Explainer Qwen3.8-Max-Preview: what Alibaba confirmed, what it only claimed A 2.4T-parameter multimodal preview shipped before any benchmark, model card, or license. Explore the facts below. Confirmed vs Claimed Parameter scale Serving-cost calculator Verified 3.7-Max baseline ✓Preview is live and purchasable. Qwen3.8-Max-Preview is sold through Alibaba Token Plan, Qoder, and QoderWork at 10% of standard pricing. ✓Sparse Mixture-of-Experts, multimodal. Developer Shuai Bai says it is the team’s first multimodal model above 1T parameters, handling images, video, and documents. ✓OpenAI and Anthropic protocol compatibility. Existing coding agents can point at Qwen3.8 without rebuilding their harnesses. ✕2.4 trillion parameters. This is Alibaba’s own figure. No model card or specification confirms it. ✕“Second only to Fable 5.” No benchmark table has been published. The ranking rests on internal evaluation. ✕Open weights “soon.” No date, no license, no Hugging Face repository. Alibaba’s last two Max flagships shipped closed. ✕Active parameters per token. Undisclosed — the single number that decides real serving cost for a sparse MoE model. Status as of July 19, 2026. Toggle a fact type using the tabs above. Total parameters, publicly disclosed frontier models, July 2026. Qwen3.8’s 2.4T is Alibaba’s claim, not a verified figure. Total count is not usable compute. A sparse MoE model activates only a fraction of these parameters per token. If the full 2.4T model shipped open-weight, what would it take to load? FP16 (16-bit) FP8 (8-bi
Related
相關文章

阿里甩出“配音”神器:能調整情緒,還會說方言
阿里旗下的千問大模型推出語音合成工具Qwen-Audio-3.0-TTS,支援情緒調整、方言及多語種,並可透過自然語言指令控制語氣與場景。該模型在第三方評測中獲得語音合成榜單第一,具備高品質的聲音復刻與抗噪能力。

手機、座艙、具身,中國最大端側獨角獸低調交出高分卷
面壁智能作為中國最大端側獨角獸,於WAIC展示手機、汽車、機器人等端側AI落地成果,並與三星、吉利等企業合作。該公司強調端側AI產業化需超越模型本身,建立跨終端適配、生態與標準,正逐步成為端側智能產業定義者。

關於面壁智能,聊聊我的一些新思考
賬號設置我的關注我的收藏申請的報道退出登錄登錄搜索36氪Auto數字時氪未來消費智能湧現未來城市啟動Power on36氪出海36氪研究院潮生TIDE36氪企服點評36氪財經職場bonus36碳後浪研究所暗湧Waves硬氪氪睿研究院媒體品牌企業號企服點評36Kr研究院36Kr創新諮詢企業服務核心服務城市之窗政府服務創投發佈LP源計劃VClubVClub投資機。

Token收費,不再“天經地義”?
賬號設置我的關注我的收藏申請的報道退出登錄登錄搜索36氪Auto數字時氪未來消費智能湧現未來城市啟動Power on36氪出海36氪研究院潮生TIDE36氪企服點評36氪財經職場bonus36碳後浪研究所暗湧Waves硬氪氪睿研究院媒體品牌企業號企服點評36Kr研究院36Kr創新諮詢企業服務核心服務城市之窗政府服務創投發佈LP源計劃VClubVClub投資機。

AI眼鏡繼續進化:不只看見,還要辦事
過去兩年,大模型技術加速導入智慧眼鏡,翻譯、拍攝等應用功能密集問世,成為市場主流。然而,隨著這些能力逐漸成為標配,AI眼鏡所面臨的新課題也隨之浮現——業界開始思考,如何讓眼鏡從「看見」進化到「辦事」,真正成為能主動協助用戶完成任務的隨身裝置。

16萬Star的OpenCode徹底重寫:API全部重做、Bun換Node、桌面端遷移Electron
擁有超過 16 萬顆 GitHub 星星、每月活躍開發者高達 750 萬人的開源專案 OpenCode,在 2026 年 7 月正式推出了 2.0 版本。這不僅是一次例行更新,而是從底層架構到使用者體驗的全面翻新。聯合創始人 Dax Raad 在近期受訪時坦言,這是他職業生涯中「第三次才做對」的產品:0 是原型試水,1.x 是市場驗證,而 2.0 則是在徹底理解整個領域後,從零開始的推倒重來。 這次重寫的核心變革之一,就是完全重構了應用程式介面(API)。