1:"$Sreact.fragment"
3:I[54118,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1737","static/chunks/fe207bbb-c84ed91deca4f888.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4094","static/chunks/4094-eaa6158623fe27ff.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4982","static/chunks/4982-a4b2ee93ffd82ffd.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1810","static/chunks/1810-cf4ca0391c1ddf2a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6397","static/chunks/6397-b7ccd1edf8aedf12.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7979","static/chunks/7979-1799576a8e82a4f3.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","144","static/chunks/144-1a048554a0613a1b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2610","static/chunks/2610-c41f461a4ed7cf24.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","528","static/chunks/528-7ef2006e50a52549.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9919","static/chunks/9919-b585857c6e10fb85.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9326","static/chunks/9326-103822841b140d80.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4077","static/chunks/4077-cba4e88bdb106467.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1924","static/chunks/1924-8c4ee98e9b0dbf83.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2530","static/chunks/2530-9eb218eca5ffc00b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7177","static/chunks/app/layout-75dbb92d3cda2f1c.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"Providers"]
4:I[98195,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1737","static/chunks/fe207bbb-c84ed91deca4f888.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4094","static/chunks/4094-eaa6158623fe27ff.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4982","static/chunks/4982-a4b2ee93ffd82ffd.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1810","static/chunks/1810-cf4ca0391c1ddf2a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6397","static/chunks/6397-b7ccd1edf8aedf12.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7979","static/chunks/7979-1799576a8e82a4f3.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","144","static/chunks/144-1a048554a0613a1b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2610","static/chunks/2610-c41f461a4ed7cf24.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","528","static/chunks/528-7ef2006e50a52549.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9919","static/chunks/9919-b585857c6e10fb85.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9326","static/chunks/9326-103822841b140d80.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4077","static/chunks/4077-cba4e88bdb106467.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1924","static/chunks/1924-8c4ee98e9b0dbf83.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2530","static/chunks/2530-9eb218eca5ffc00b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7177","static/chunks/app/layout-75dbb92d3cda2f1c.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"GoogleTagManager"]
5:I[4411,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1737","static/chunks/fe207bbb-c84ed91deca4f888.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4094","static/chunks/4094-eaa6158623fe27ff.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4982","static/chunks/4982-a4b2ee93ffd82ffd.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1810","static/chunks/1810-cf4ca0391c1ddf2a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6397","static/chunks/6397-b7ccd1edf8aedf12.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7979","static/chunks/7979-1799576a8e82a4f3.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","144","static/chunks/144-1a048554a0613a1b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2610","static/chunks/2610-c41f461a4ed7cf24.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","528","static/chunks/528-7ef2006e50a52549.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9919","static/chunks/9919-b585857c6e10fb85.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9326","static/chunks/9326-103822841b140d80.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4077","static/chunks/4077-cba4e88bdb106467.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1924","static/chunks/1924-8c4ee98e9b0dbf83.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2530","static/chunks/2530-9eb218eca5ffc00b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7177","static/chunks/app/layout-75dbb92d3cda2f1c.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"FilmAwardStrip"]
6:"$Sreact.suspense"
7:I[40459,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1737","static/chunks/fe207bbb-c84ed91deca4f888.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4094","static/chunks/4094-eaa6158623fe27ff.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4982","static/chunks/4982-a4b2ee93ffd82ffd.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1810","static/chunks/1810-cf4ca0391c1ddf2a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6397","static/chunks/6397-b7ccd1edf8aedf12.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7979","static/chunks/7979-1799576a8e82a4f3.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","144","static/chunks/144-1a048554a0613a1b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2610","static/chunks/2610-c41f461a4ed7cf24.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","528","static/chunks/528-7ef2006e50a52549.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9919","static/chunks/9919-b585857c6e10fb85.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9326","static/chunks/9326-103822841b140d80.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4077","static/chunks/4077-cba4e88bdb106467.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1924","static/chunks/1924-8c4ee98e9b0dbf83.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2530","static/chunks/2530-9eb218eca5ffc00b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7177","static/chunks/app/layout-75dbb92d3cda2f1c.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"Navbar"]
8:I[15848,[],""]
9:I[55256,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","8039","static/chunks/app/error-332e154bd557e323.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"default"]
a:I[62142,[],""]
19:I[79302,["4219","static/chunks/app/global-error-f07d85f704128170.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"default"]
:HL["/_next/static/media/7d4881bb7e1bf84d-s.p.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
:HL["/_next/static/css/c91c557421edbcc1.css?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","style"]
:HL["/_next/static/css/9c4f0c3be44ead33.css?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","style"]
2:T412,
(function () {
  window.dataLayer = window.dataLayer || [];
  function gtag() { window.dataLayer.push(arguments); }

  gtag('consent', 'default', {
    ad_storage: 'denied',
    ad_user_data: 'denied',
    ad_personalization: 'denied',
    analytics_storage: 'denied',
    functionality_storage: 'denied',
    personalization_storage: 'denied',
    security_storage: 'granted'
  });

  try {
    var match = document.cookie.match(/(?:^|;\s*)gdpr_consent=([^;]*)/);
    if (!match) return;
    var saved = JSON.parse(decodeURIComponent(match[1]));
    if (!saved || typeof saved !== 'object') return;
    gtag('consent', 'update', {
      analytics_storage: saved.analytics ? 'granted' : 'denied',
      ad_storage: saved.marketing ? 'granted' : 'denied',
      ad_user_data: saved.marketing ? 'granted' : 'denied',
      ad_personalization: saved.marketing ? 'granted' : 'denied',
      functionality_storage: saved.functional ? 'granted' : 'denied'
    });
  } catch (e) {
    /* malformed cookie â€” the denied default stands */
  }
})();
0:{"P":null,"b":"vX-6P7rGIK5v7u82yfIum","p":"","c":["","models","qwen-3.8-max"],"i":false,"f":[[["",{"children":["models",{"children":[["route","qwen-3.8-max","d"],{"children":["__PAGE__",{}]}]}]},"$undefined","$undefined",true],["",["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/_next/static/css/c91c557421edbcc1.css?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}],["$","link","1",{"rel":"stylesheet","href":"/_next/static/css/9c4f0c3be44ead33.css?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]],["$","html",null,{"lang":"en","suppressHydrationWarning":true,"className":"__variable_8c25df","children":[["$","head",null,{"children":[["$","script",null,{"id":"consent-mode-default","dangerouslySetInnerHTML":{"__html":"$2"}}],["$","script",null,{"id":"film-award-dismissed","dangerouslySetInnerHTML":{"__html":"try{if(document.cookie.split(';').some(function(c){return c.trim()===\"film_award_2027_dismissed=1\"}))document.documentElement.setAttribute(\"data-film-award-dismissed\",'')}catch(e){}"}}],["$","link",null,{"rel":"preconnect","href":"https://images.segmind.com"}],["$","link",null,{"rel":"preconnect","href":"https://segmind-resources.s3.amazonaws.com"}],["$","link",null,{"rel":"preconnect","href":"https://www.googletagmanager.com"}],["$","link",null,{"rel":"preload","href":"/fonts/Satoshi-Variable.woff2","as":"font","type":"font/woff2","crossOrigin":"anonymous"}]]}],["$","body",null,{"className":"antialiased bg-background text-foreground","children":["$","$L3",null,{"children":[["$","$L4",null,{"gtmId":"GTM-MHW23H4Z"}],["$","div",null,{"className":"min-h-screen flex flex-col","children":[["$","$L5",null,{}],["$","$6",null,{"fallback":["$","div",null,{"className":"h-16 bg-background/95 backdrop-blur border-b border-border"}],"children":["$","$L7",null,{}]}],["$","main",null,{"className":"flex-1","children":["$","$L8",null,{"parallelRouterKey":"children","error":"$9","errorStyles":[],"errorScripts":[],"template":["$","$La",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","div",null,{"className":"min-h-screen bg-background flex items-center justify-center px-4 sm:px-6 lg:px-8","children":["$","div",null,{"className":"max-w-lg w-full text-center","children":[["$","div",null,{"className":"mb-8","children":["$","div",null,{"className":"relative","children":[["$","h1",null,{"className":"text-9xl font-bold text-muted/20 dark:text-muted/10 select-none","children":"404"}],["$","div",null,{"className":"absolute inset-0 flex items-center justify-center","children":["$","div",null,{"className":"relative","children":[["$","div",null,{"className":"absolute -top-8 -left-8 w-16 h-16 bg-brand-100 dark:bg-brand-950 rounded-full flex items-center justify-center animate-bounce","children":["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-zap w-8 h-8 text-brand-500","children":["$Lb","$undefined"]}]}],"$Lc","$Ld"]}]}]]}]}],"$Le","$Lf","$L10","$L11"]}]}],[]],"forbidden":"$undefined","unauthorized":"$undefined"}]}],"$L12"]}],"$L13","$L14"]}]}]]}]]}],{"children":["models","$L15",{"children":[["route","qwen-3.8-max","d"],"$L16",{"children":["__PAGE__","$L17",{},null,false]},null,false]},null,false]},null,false],"$L18",false]],"m":"$undefined","G":["$19",[]],"s":false,"S":true}
1a:I[75289,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6541","static/chunks/fb4e7fc9-e31ad622f5fde25d.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9761","static/chunks/998e86a0-e112630ec4db17dc.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4094","static/chunks/4094-eaa6158623fe27ff.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4982","static/chunks/4982-a4b2ee93ffd82ffd.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1810","static/chunks/1810-cf4ca0391c1ddf2a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6118","static/chunks/6118-7328b7d3f629a5ef.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7979","static/chunks/7979-1799576a8e82a4f3.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","393","static/chunks/393-7cd2157fc3790475.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3985","static/chunks/3985-1f9bcb95de313d61.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2610","static/chunks/2610-c41f461a4ed7cf24.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4844","static/chunks/4844-011fe3465334e7d0.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9305","static/chunks/9305-0f5d0f2a661afddc.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","713","static/chunks/713-dcb3ca4f0233704d.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3504","static/chunks/3504-ae9bc2dc93d2ef47.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9919","static/chunks/9919-b585857c6e10fb85.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9326","static/chunks/9326-103822841b140d80.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1885","static/chunks/1885-f206f5873f09b3e2.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4077","static/chunks/4077-cba4e88bdb106467.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5126","static/chunks/5126-bdde68ce894e4935.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5642","static/chunks/5642-c80cb7ee6e914a6a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1924","static/chunks/1924-8c4ee98e9b0dbf83.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6372","static/chunks/6372-3e2880fc7d769d1f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1289","static/chunks/1289-4fdf72847f7ef615.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","8382","static/chunks/8382-004a14550e3769cc.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1951","static/chunks/app/models/%5Broute%5D/page-c5fd4df0a8a5cab4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],""]
1b:I[4717,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1737","static/chunks/fe207bbb-c84ed91deca4f888.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4094","static/chunks/4094-eaa6158623fe27ff.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4982","static/chunks/4982-a4b2ee93ffd82ffd.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1810","static/chunks/1810-cf4ca0391c1ddf2a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6397","static/chunks/6397-b7ccd1edf8aedf12.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7979","static/chunks/7979-1799576a8e82a4f3.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","144","static/chunks/144-1a048554a0613a1b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2610","static/chunks/2610-c41f461a4ed7cf24.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","528","static/chunks/528-7ef2006e50a52549.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9919","static/chunks/9919-b585857c6e10fb85.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9326","static/chunks/9326-103822841b140d80.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4077","static/chunks/4077-cba4e88bdb106467.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1924","static/chunks/1924-8c4ee98e9b0dbf83.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2530","static/chunks/2530-9eb218eca5ffc00b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7177","static/chunks/app/layout-75dbb92d3cda2f1c.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"Footer"]
1c:I[35733,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6397","static/chunks/6397-b7ccd1edf8aedf12.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","341","static/chunks/app/models/(catalogue)/loading-03c2017343ab8539.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"Toaster"]
1d:I[20659,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1737","static/chunks/fe207bbb-c84ed91deca4f888.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4094","static/chunks/4094-eaa6158623fe27ff.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4982","static/chunks/4982-a4b2ee93ffd82ffd.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1810","static/chunks/1810-cf4ca0391c1ddf2a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6397","static/chunks/6397-b7ccd1edf8aedf12.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7979","static/chunks/7979-1799576a8e82a4f3.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","144","static/chunks/144-1a048554a0613a1b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2610","static/chunks/2610-c41f461a4ed7cf24.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","528","static/chunks/528-7ef2006e50a52549.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9919","static/chunks/9919-b585857c6e10fb85.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9326","static/chunks/9326-103822841b140d80.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4077","static/chunks/4077-cba4e88bdb106467.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1924","static/chunks/1924-8c4ee98e9b0dbf83.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2530","static/chunks/2530-9eb218eca5ffc00b.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7177","static/chunks/app/layout-75dbb92d3cda2f1c.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"LazyLayoutExtras"]
20:I[30661,[],"OutletBoundary"]
22:I[72272,[],"AsyncMetadataOutlet"]
24:I[30661,[],"ViewportBoundary"]
26:I[30661,[],"MetadataBoundary"]
b:["$","path","1xq2db",{"d":"M4 14a1 1 0 0 1-.78-1.63l9.9-10.2a.5.5 0 0 1 .86.46l-1.92 6.02A1 1 0 0 0 13 10h7a1 1 0 0 1 .78 1.63l-9.9 10.2a.5.5 0 0 1-.86-.46l1.92-6.02A1 1 0 0 0 11 14z"}]
c:["$","div",null,{"className":"absolute -top-4 -right-12 w-12 h-12 bg-brand-secondary-100 dark:bg-brand-secondary-950 rounded-full flex items-center justify-center animate-pulse","children":["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-search w-6 h-6 text-brand-secondary-500","children":[["$","circle","4ej97u",{"cx":"11","cy":"11","r":"8"}],["$","path","1qie3q",{"d":"m21 21-4.3-4.3"}],"$undefined"]}]}]
d:["$","div",null,{"className":"absolute -bottom-6 left-4 w-10 h-10 bg-green-100 dark:bg-green-950 rounded-full flex items-center justify-center animate-bounce delay-200","children":["$","div",null,{"className":"w-3 h-3 bg-green-500 rounded-full"}]}]
e:["$","div",null,{"className":"mb-8","children":[["$","h2",null,{"className":"text-2xl font-semibold text-foreground mb-4","children":"Page Not Found"}],["$","p",null,{"className":"text-muted-foreground text-lg mb-2","children":"Oops! The page you're looking for doesn't exist."}],["$","p",null,{"className":"text-muted-foreground","children":"It might have been moved, deleted, or you entered the wrong URL."}]]}]
f:["$","div",null,{"className":"flex flex-col sm:flex-row gap-4 justify-center items-center mb-12","children":[["$","$L1a",null,{"href":"/","className":"inline-flex items-center justify-center gap-2 px-6 py-3 bg-brand-600 dark:bg-brand-500 text-white rounded-lg hover:bg-brand-700 dark:hover:bg-brand-600 transition-colors font-medium text-base w-full sm:w-auto","children":[["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-house w-5 h-5","children":[["$","path","5wwlr5",{"d":"M15 21v-8a1 1 0 0 0-1-1h-4a1 1 0 0 0-1 1v8"}],["$","path","1d0kgt",{"d":"M3 10a2 2 0 0 1 .709-1.528l7-5.999a2 2 0 0 1 2.582 0l7 5.999A2 2 0 0 1 21 10v9a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2z"}],"$undefined"]}],"Go Home"]}],["$","$L1a",null,{"href":"/models","className":"inline-flex items-center justify-center gap-2 px-6 py-3 bg-surface border border-border text-foreground rounded-lg hover:bg-surface/80 transition-colors font-medium text-base w-full sm:w-auto","children":[["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-search w-5 h-5","children":[["$","circle","4ej97u",{"cx":"11","cy":"11","r":"8"}],["$","path","1qie3q",{"d":"m21 21-4.3-4.3"}],"$undefined"]}],"Browse Models"]}]]}]
10:["$","div",null,{"className":"border-t border-border pt-8","children":[["$","p",null,{"className":"text-sm text-muted-foreground mb-4","children":"Popular destinations:"}],["$","div",null,{"className":"flex flex-wrap justify-center gap-4 text-sm","children":[["$","$L1a",null,{"href":"/models/tags/image","className":"text-brand-600 dark:text-brand-400 hover:text-brand-700 dark:hover:text-brand-300 transition-colors","children":"Image Models"}],["$","span",null,{"className":"text-muted-foreground","children":"â€¢"}],["$","$L1a",null,{"href":"/models/tags/video","className":"text-brand-600 dark:text-brand-400 hover:text-brand-700 dark:hover:text-brand-300 transition-colors","children":"Video Models"}],["$","span",null,{"className":"text-muted-foreground","children":"â€¢"}],["$","$L1a",null,{"href":"/models/tags/llms","className":"text-brand-600 dark:text-brand-400 hover:text-brand-700 dark:hover:text-brand-300 transition-colors","children":"LLMs"}],["$","span",null,{"className":"text-muted-foreground","children":"â€¢"}],["$","$L1a",null,{"href":"/models","className":"text-brand-600 dark:text-brand-400 hover:text-brand-700 dark:hover:text-brand-300 transition-colors","children":"All Models"}]]}]]}]
11:["$","div",null,{"className":"mt-8","children":["$","$L1a",null,{"href":"/","className":"inline-flex items-center text-sm text-muted-foreground hover:text-foreground transition-colors","children":[["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-arrow-left w-4 h-4 mr-2","children":[["$","path","1l729n",{"d":"m12 19-7-7 7-7"}],["$","path","x3x0zl",{"d":"M19 12H5"}],"$undefined"]}],"Go back to homepage"]}]}]
12:["$","$L1b",null,{}]
13:["$","$L1c",null,{"closeButton":true,"toastOptions":{"duration":30000}}]
14:["$","$L1d",null,{}]
15:["$","$1","c",{"children":[null,["$","$L8",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$La",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}]]}]
16:["$","$1","c",{"children":[null,"$L1e"]}]
17:["$","$1","c",{"children":["$L1f",null,["$","$L20",null,{"children":["$L21",["$","$L22",null,{"promise":"$@23"}]]}]]}]
18:["$","$1","h",{"children":[null,[["$","$L24",null,{"children":"$L25"}],["$","meta",null,{"name":"next-size-adjust","content":""}]],["$","$L26",null,{"children":["$","div",null,{"hidden":true,"children":["$","$6",null,{"fallback":null,"children":"$L27"}]}]}]]}]
25:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]
21:null
28:I[53921,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7213","static/chunks/7213-ed3acee0c865cd60.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7256","static/chunks/app/models/%5Broute%5D/layout-23522cb6540a765c.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"default"]
2c:I[4704,[],"IconMark"]
29:Tb46,# Qwen3.8 Max: Multimodal Reasoning and Agentic Coding LLM

## What is Qwen3.8 Max?

Qwen3.8 Max is the flagship large language model from Alibaba's Qwen team and the most capable model in the Qwen family to date. It is a mixture-of-experts (MoE) system with 2.4 trillion total parameters that activates roughly 95 billion parameters per request, built on the Qwen3.5 architecture. The model accepts text and image input and returns text, with a context window of up to 1 million tokens. It is designed for complex reasoning, visual understanding, coding, and long-horizon agentic workflows â€” the kind of work where a model has to plan, act, verify, and iterate over many steps rather than answer a single prompt.

## Key Features

- **1M-token context** for whole-repository code, long documents, and multi-file research.
- **Multimodal input** â€” combine a text prompt with an image for vision-grounded reasoning.
- **Mixture-of-experts efficiency** â€” ~95B active parameters keep inference tractable at frontier scale.
- **Long-horizon agentic execution** â€” planning, tool use, self-verification, and iteration.
- **Large output budget** â€” up to 131,072 tokens per response.
- **OpenAI- and Anthropic-compatible** request formats for easy integration.

## Best Use Cases

Qwen3.8 Max shines on repository-scale coding agents, autonomous refactors, and bug-fixing loops that run across many steps. Its long context makes it strong for long-document analysis, legal and financial review, knowledge bases, and multi-step research assistants. The multimodal input supports document understanding, screenshot reasoning, and visual QA. Teams building agent frameworks can drop it into existing OpenAI/Anthropic-compatible clients.

## Prompt Tips and Output Quality

Give the model explicit, structured, multi-part instructions â€” it follows numbered requirements closely. In hands-on testing, a senior-engineer coding prompt produced a correct, well-commented Kadane's-algorithm implementation with accurate complexity analysis and passing test cases, finishing cleanly without truncation. For agentic runs, break goals into checkpoints so intermediate work can be inspected. For pure text tasks, leave the image field empty; add an image only when the task is vision-grounded.

## FAQs

**Is Qwen3.8 Max multimodal?** Yes â€” it accepts text and image input and returns text.

**What context length does it support?** Up to 1 million tokens, suited to whole repositories and long documents.

**What is it best at?** Agentic coding, long-horizon execution, long-context reasoning, and multimodal understanding.

**Who makes Qwen3.8 Max?** Alibaba's Qwen team; it is the flagship of the Qwen3.8 series.

**How do I call it?** Send a prompt to the API endpoint using an OpenAI-style messages request and read the generated text from the response.
2a:T7d2,# How to Use Qwen3.8 Max

Qwen3.8 Max is Alibaba's flagship multimodal reasoning LLM, built for long-context understanding, coding, and agentic workflows. This guide covers how to get reliable results.

## Sending a Request

Provide a `prompt` describing the task. The model returns text. For multimodal tasks, also pass a public `image` URL; leave it empty for pure text work. The endpoint speaks an OpenAI-style chat format, so structure your input as a user message and read the answer from the response content.

## Choosing the Right Task

- **Coding and agents:** repo-scale refactors, bug fixes, test generation, and multi-step engineering tasks. Spell out the goal, constraints, and the exact deliverables you expect.
- **Long-context reasoning:** feed large documents, logs, or codebases and ask for synthesis, extraction, or step-by-step analysis. The 1M-token window keeps context intact.
- **Multimodal understanding:** attach an image for document parsing, screenshot reasoning, or visual QA.

## Prompting Tips

- Use explicit, numbered instructions â€” the model follows structured requirements closely.
- Ask for intermediate steps or checkpoints on long tasks so you can verify work before it compounds.
- Request a specific output format (code block, table, JSON) when you need clean, parseable results.

## Recommended Settings

Based on end-to-end testing on Segmind:

- **Prompt:** detailed and multi-part. A senior-engineer coding prompt (explain, implement, analyze complexity, add test cases) returned a correct, well-commented solution with passing tests and no truncation.
- **Image:** leave empty (`""`) for text-only tasks; supply a public image URL only for vision-grounded prompts.
- **Output:** expect thorough, well-structured responses; the model is deliberate, so allow time for complex, long-horizon requests.

Start with a clear single-task prompt, confirm the output quality, then scale up to longer agentic or long-document workflows.
1e:["$","$L28",null,{"route":"qwen-3.8-max","modelDetails":{"title":"Qwen3.8 Max","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen-3.8-max-9695.webp","type":"textToText","model_type":"SS","is_depreciated":false,"meta_description":"Alibaba's flagship multimodal LLM reasons over 1M-token context to power agentic coding and long-document analysis end to end.","seo_description":"$29","guide":"$2a","pricing_guide":"","artifacts":[],"default_image_output":"Qwen3.8 Max","default_response":null,"parameters":{"prompt":{"isRequired":true,"affectsPricing":false,"type":"str","description":"Text prompt driving the model; handles up to 1M-token context. Use structured, multi-step instructions for reasoning, coding, or long-document analysis.","isEnum":false,"hidden":false,"isAdvanced":false,"displayOrder":0,"component":"textarea","label":"Prompt","default":"You are a senior software engineer. Solve the following problem end to end.\n\nGiven an array of integers, write a Python function `max_subarray_sum(nums)` that returns the maximum sum of any contiguous subarray using Kadane's algorithm.\n\nIn your answer:\n1. Briefly explain the intuition behind Kadane's algorithm.\n2. Provide clean, commented Python code.\n3. Analyze the time and space complexity.\n4. Include 3 test cases (including one with all-negative numbers) and show the expected output.","displayValue":"You are a senior software engineer. Solve the following problem end to end.\n\nGiven an array of integers, write a Python function `max_subarray_sum(nums)` that returns the maximum sum of any contiguous subarray using Kadane's algorithm.\n\nIn your answer:\n1. Briefly explain the intuition behind Kadane's algorithm.\n2. Provide clean, commented Python code.\n3. Analyze the time and space complexity.\n4. Include 3 test cases (including one with all-negative numbers) and show the expected output."},"image":{"isRequired":false,"affectsPricing":false,"type":"image","description":"Optional public image URL for multimodal input. Add an image for visual reasoning, document understanding, or vision-grounded tasks.","isEnum":false,"hidden":false,"isAdvanced":false,"displayOrder":1,"component":"textarea","label":"Image","default":"","displayValue":""},"response_format":{"type":"object","format":"json","component":"textarea","label":"Response format","description":"Structured output. {\"type\": \"json_object\"} returns any valid JSON; {\"type\": \"json_schema\", \"json_schema\": {\"name\", \"strict\", \"schema\"}} enforces the given JSON Schema. Omit for plain text.","displayValue":{"type":"json_schema","json_schema":{"name":"answer","strict":true,"schema":{"type":"object","properties":{"answer":{"type":"string"}},"required":["answer"],"additionalProperties":false}}},"isRequired":false,"isAdvanced":true,"hidden":false,"affectsPricing":false,"isEnum":false,"displayOrder":2},"effort":{"isRequired":false,"affectsPricing":true,"type":"str","description":"How hard the model reasons before answering. Thinking is billed as output tokens, so higher levels cost more and take longer.","isEnum":true,"enum":["minimal","low","medium","high","xhigh","max"],"hidden":false,"isAdvanced":false,"displayOrder":3,"component":"select","label":"Reasoning effort","values":["minimal","low","medium","high","xhigh","max"]}},"endpoints":{"main":"/v1/qwen-3.8-max"},"api_notes":[],"model_outputs":null,"llm_capabilities":null,"license":"","github_link":null,"inference_provider":"OPENROUTER","created_ts":"2026-08-06 09:01:07.804846+00:00","slug":"qwen-3.8-max","name":"Qwen3.8 Max","description":"Multimodal reasoning and agentic coding with 1M-token context.","category":"textToText","modality":"llm","input":"text","output":"text","provider":"ALIBABA","thumbnail_url":"https://segmind-resources.s3.amazonaws.com/output/qwen-3.8-max-9695.webp","is_new":false,"coming_soon":false,"popularity_score":25,"category_percentile":37,"average_cost":0.043945,"latency_seconds":null,"created_at":"2026-08-06 09:01:07.804846+00:00","parameters_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"prompt":{"type":"string","title":"Prompt","description":"Text prompt driving the model; handles up to 1M-token context. Use structured, multi-step instructions for reasoning, coding, or long-document analysis.","default":"You are a senior software engineer. Solve the following problem end to end.\n\nGiven an array of integers, write a Python function `max_subarray_sum(nums)` that returns the maximum sum of any contiguous subarray using Kadane's algorithm.\n\nIn your answer:\n1. Briefly explain the intuition behind Kadane's algorithm.\n2. Provide clean, commented Python code.\n3. Analyze the time and space complexity.\n4. Include 3 test cases (including one with all-negative numbers) and show the expected output.","x-ui-metadata":{"order":0,"advanced":false,"hidden":false}},"image":{"type":"string","format":"uri","x-file-metadata":{"allowed-types":["image/*"]},"title":"Image","description":"Optional public image URL for multimodal input. Add an image for visual reasoning, document understanding, or vision-grounded tasks.","x-ui-metadata":{"order":1,"advanced":false,"hidden":false}},"response_format":{"type":"object","format":"json","title":"Response format","description":"Structured output. {\"type\": \"json_object\"} returns any valid JSON; {\"type\": \"json_schema\", \"json_schema\": {\"name\", \"strict\", \"schema\"}} enforces the given JSON Schema. Omit for plain text.","examples":[{"type":"json_schema","json_schema":{"name":"answer","strict":true,"schema":{"type":"object","properties":{"answer":{"type":"string"}},"required":["answer"],"additionalProperties":false}}}],"x-ui-metadata":{"order":2,"advanced":true,"hidden":false}},"effort":{"type":"string","title":"Reasoning effort","description":"How hard the model reasons before answering. Thinking is billed as output tokens, so higher levels cost more and take longer.","oneOf":[{"const":"minimal","title":"minimal"},{"const":"low","title":"low"},{"const":"medium","title":"medium"},{"const":"high","title":"high"},{"const":"xhigh","title":"xhigh"},{"const":"max","title":"max"}],"x-ui-metadata":{"order":3,"advanced":false,"hidden":false},"default":"minimal"}},"required":["prompt"],"examples":[{"prompt":"You are a senior software engineer. Solve the following problem end to end.\n\nGiven an array of integers, write a Python function `max_subarray_sum(nums)` that returns the maximum sum of any contiguous subarray using Kadane's algorithm.\n\nIn your answer:\n1. Briefly explain the intuition behind Kadane's algorithm.\n2. Provide clean, commented Python code.\n3. Analyze the time and space complexity.\n4. Include 3 test cases (including one with all-negative numbers) and show the expected output."}]}},"metrics":null,"children":"$L2b"}]
23:{"metadata":[["$","title","0",{"children":"Qwen3.8 Max API | Segmind"}],["$","meta","1",{"name":"description","content":"Alibaba's flagship multimodal LLM reasons over 1M-token context to power agentic coding and long-document analysis end to end."}],["$","link","2",{"rel":"canonical","href":"https://www.segmind.com/models/qwen-3.8-max"}],["$","meta","3",{"property":"og:title","content":"Qwen3.8 Max API | Segmind"}],["$","meta","4",{"property":"og:description","content":"Alibaba's flagship multimodal LLM reasons over 1M-token context to power agentic coding and long-document analysis end to end."}],["$","meta","5",{"property":"og:image","content":"https://segmind-resources.s3.amazonaws.com/output/qwen-3.8-max-9695.webp"}],["$","meta","6",{"name":"twitter:card","content":"summary_large_image"}],["$","meta","7",{"name":"twitter:title","content":"Qwen3.8 Max API | Segmind"}],["$","meta","8",{"name":"twitter:description","content":"Alibaba's flagship multimodal LLM reasons over 1M-token context to power agentic coding and long-document analysis end to end."}],["$","meta","9",{"name":"twitter:image","content":"https://www.segmind.com/og/home.jpg"}],["$","link","10",{"rel":"shortcut icon","href":"/favicon.ico"}],["$","link","11",{"rel":"icon","href":"/favicon.ico","sizes":"any"}],["$","link","12",{"rel":"icon","href":"/favicon-48x48.png","type":"image/png","sizes":"48x48"}],["$","link","13",{"rel":"icon","href":"/favicon-192x192.png","type":"image/png","sizes":"192x192"}],["$","link","14",{"rel":"icon","href":"/favicon.svg","type":"image/svg+xml"}],["$","link","15",{"rel":"apple-touch-icon","href":"/favicon-48x48.png"}],["$","$L2c","16",{}]],"error":null,"digest":"$undefined"}
27:"$23:metadata"
2b:["$","$L8",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$La",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}]
2e:I[68382,["6693","static/chunks/fba4ba2d-460e04194f22506f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3009","static/chunks/6a7d804d-a5cc510ceffe206f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7505","static/chunks/c02d83f4-5637599209ef9447.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6541","static/chunks/fb4e7fc9-e31ad622f5fde25d.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9761","static/chunks/998e86a0-e112630ec4db17dc.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9509","static/chunks/9509-6125acd3006111c1.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4378","static/chunks/4378-3f82908971052aa9.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5289","static/chunks/5289-131715863bbab062.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4094","static/chunks/4094-eaa6158623fe27ff.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4982","static/chunks/4982-a4b2ee93ffd82ffd.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1810","static/chunks/1810-cf4ca0391c1ddf2a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6118","static/chunks/6118-7328b7d3f629a5ef.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","7979","static/chunks/7979-1799576a8e82a4f3.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","393","static/chunks/393-7cd2157fc3790475.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3985","static/chunks/3985-1f9bcb95de313d61.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","2610","static/chunks/2610-c41f461a4ed7cf24.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4844","static/chunks/4844-011fe3465334e7d0.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9305","static/chunks/9305-0f5d0f2a661afddc.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","713","static/chunks/713-dcb3ca4f0233704d.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","3504","static/chunks/3504-ae9bc2dc93d2ef47.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1742","static/chunks/1742-0ea812562873537a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1172","static/chunks/1172-89020d82938322e4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9919","static/chunks/9919-b585857c6e10fb85.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","9326","static/chunks/9326-103822841b140d80.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1885","static/chunks/1885-f206f5873f09b3e2.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","4077","static/chunks/4077-cba4e88bdb106467.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5126","static/chunks/5126-bdde68ce894e4935.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","5642","static/chunks/5642-c80cb7ee6e914a6a.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1924","static/chunks/1924-8c4ee98e9b0dbf83.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","6372","static/chunks/6372-3e2880fc7d769d1f.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1289","static/chunks/1289-4fdf72847f7ef615.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","8382","static/chunks/8382-004a14550e3769cc.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x","1951","static/chunks/app/models/%5Broute%5D/page-c5fd4df0a8a5cab4.js?dpl=dpl_CPYCfxesaX7RKfJUQZpH2JDib41x"],"default",1]
2d:T406,{"@context":"https://schema.org","@type":"FAQPage","@id":"https://www.segmind.com/models/qwen-3.8-max#faq","mainEntity":[{"@type":"Question","name":"Is Qwen3.8 Max multimodal?","acceptedAnswer":{"@type":"Answer","text":"Yes â€” it accepts text and image input and returns text."}},{"@type":"Question","name":"What context length does it support?","acceptedAnswer":{"@type":"Answer","text":"Up to 1 million tokens, suited to whole repositories and long documents."}},{"@type":"Question","name":"What is it best at?","acceptedAnswer":{"@type":"Answer","text":"Agentic coding, long-horizon execution, long-context reasoning, and multimodal understanding."}},{"@type":"Question","name":"Who makes Qwen3.8 Max?","acceptedAnswer":{"@type":"Answer","text":"Alibaba's Qwen team; it is the flagship of the Qwen3.8 series."}},{"@type":"Question","name":"How do I call it?","acceptedAnswer":{"@type":"Answer","text":"Send a prompt to the API endpoint using an OpenAI-style messages request and read the generated text from the response."}}]}2f:Tb46,# Qwen3.8 Max: Multimodal Reasoning and Agentic Coding LLM

## What is Qwen3.8 Max?

Qwen3.8 Max is the flagship large language model from Alibaba's Qwen team and the most capable model in the Qwen family to date. It is a mixture-of-experts (MoE) system with 2.4 trillion total parameters that activates roughly 95 billion parameters per request, built on the Qwen3.5 architecture. The model accepts text and image input and returns text, with a context window of up to 1 million tokens. It is designed for complex reasoning, visual understanding, coding, and long-horizon agentic workflows â€” the kind of work where a model has to plan, act, verify, and iterate over many steps rather than answer a single prompt.

## Key Features

- **1M-token context** for whole-repository code, long documents, and multi-file research.
- **Multimodal input** â€” combine a text prompt with an image for vision-grounded reasoning.
- **Mixture-of-experts efficiency** â€” ~95B active parameters keep inference tractable at frontier scale.
- **Long-horizon agentic execution** â€” planning, tool use, self-verification, and iteration.
- **Large output budget** â€” up to 131,072 tokens per response.
- **OpenAI- and Anthropic-compatible** request formats for easy integration.

## Best Use Cases

Qwen3.8 Max shines on repository-scale coding agents, autonomous refactors, and bug-fixing loops that run across many steps. Its long context makes it strong for long-document analysis, legal and financial review, knowledge bases, and multi-step research assistants. The multimodal input supports document understanding, screenshot reasoning, and visual QA. Teams building agent frameworks can drop it into existing OpenAI/Anthropic-compatible clients.

## Prompt Tips and Output Quality

Give the model explicit, structured, multi-part instructions â€” it follows numbered requirements closely. In hands-on testing, a senior-engineer coding prompt produced a correct, well-commented Kadane's-algorithm implementation with accurate complexity analysis and passing test cases, finishing cleanly without truncation. For agentic runs, break goals into checkpoints so intermediate work can be inspected. For pure text tasks, leave the image field empty; add an image only when the task is vision-grounded.

## FAQs

**Is Qwen3.8 Max multimodal?** Yes â€” it accepts text and image input and returns text.

**What context length does it support?** Up to 1 million tokens, suited to whole repositories and long documents.

**What is it best at?** Agentic coding, long-horizon execution, long-context reasoning, and multimodal understanding.

**Who makes Qwen3.8 Max?** Alibaba's Qwen team; it is the flagship of the Qwen3.8 series.

**How do I call it?** Send a prompt to the API endpoint using an OpenAI-style messages request and read the generated text from the response.
30:T7d2,# How to Use Qwen3.8 Max

Qwen3.8 Max is Alibaba's flagship multimodal reasoning LLM, built for long-context understanding, coding, and agentic workflows. This guide covers how to get reliable results.

## Sending a Request

Provide a `prompt` describing the task. The model returns text. For multimodal tasks, also pass a public `image` URL; leave it empty for pure text work. The endpoint speaks an OpenAI-style chat format, so structure your input as a user message and read the answer from the response content.

## Choosing the Right Task

- **Coding and agents:** repo-scale refactors, bug fixes, test generation, and multi-step engineering tasks. Spell out the goal, constraints, and the exact deliverables you expect.
- **Long-context reasoning:** feed large documents, logs, or codebases and ask for synthesis, extraction, or step-by-step analysis. The 1M-token window keeps context intact.
- **Multimodal understanding:** attach an image for document parsing, screenshot reasoning, or visual QA.

## Prompting Tips

- Use explicit, numbered instructions â€” the model follows structured requirements closely.
- Ask for intermediate steps or checkpoints on long tasks so you can verify work before it compounds.
- Request a specific output format (code block, table, JSON) when you need clean, parseable results.

## Recommended Settings

Based on end-to-end testing on Segmind:

- **Prompt:** detailed and multi-part. A senior-engineer coding prompt (explain, implement, analyze complexity, add test cases) returned a correct, well-commented solution with passing tests and no truncation.
- **Image:** leave empty (`""`) for text-only tasks; supply a public image URL only for vision-grounded prompts.
- **Output:** expect thorough, well-structured responses; the model is deliberate, so allow time for complex, long-horizon requests.

Start with a clear single-task prompt, confirm the output quality, then scale up to longer agentic or long-document workflows.
1f:[["$","script",null,{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"SoftwareApplication\",\"@id\":\"https://www.segmind.com/models/qwen-3.8-max\",\"name\":\"Qwen3.8 Max\",\"description\":\"Alibaba's flagship multimodal LLM reasons over 1M-token context to power agentic coding and long-document analysis end to end.\",\"url\":\"https://www.segmind.com/models/qwen-3.8-max\",\"applicationCategory\":\"AI Model\",\"applicationSubCategory\":\"textToText\",\"operatingSystem\":\"Any\",\"image\":\"https://segmind-resources.s3.amazonaws.com/output/qwen-3.8-max-9695.webp\",\"offers\":{\"@type\":\"Offer\",\"availability\":\"https://schema.org/InStock\",\"priceCurrency\":\"USD\",\"price\":\"0.043945\"},\"provider\":{\"@type\":\"Organization\",\"@id\":\"https://www.segmind.com/#organization\",\"name\":\"Segmind\",\"url\":\"https://www.segmind.com\",\"logo\":\"https://www.segmind.com/favicon.svg\"}}"}}],["$","script",null,{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"$2d"}}],null,["$","$L2e",null,{"route":"qwen-3.8-max","modelDetails":{"title":"Qwen3.8 Max","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen-3.8-max-9695.webp","type":"textToText","model_type":"SS","is_depreciated":false,"meta_description":"Alibaba's flagship multimodal LLM reasons over 1M-token context to power agentic coding and long-document analysis end to end.","seo_description":"$2f","guide":"$30","pricing_guide":"","artifacts":"$1e:props:modelDetails:artifacts","default_image_output":"Qwen3.8 Max","default_response":null,"parameters":"$1e:props:modelDetails:parameters","endpoints":"$1e:props:modelDetails:endpoints","api_notes":"$1e:props:modelDetails:api_notes","model_outputs":null,"llm_capabilities":null,"license":"","github_link":null,"inference_provider":"OPENROUTER","created_ts":"2026-08-06 09:01:07.804846+00:00","slug":"qwen-3.8-max","name":"Qwen3.8 Max","description":"Multimodal reasoning and agentic coding with 1M-token context.","category":"textToText","modality":"llm","input":"text","output":"text","provider":"ALIBABA","thumbnail_url":"https://segmind-resources.s3.amazonaws.com/output/qwen-3.8-max-9695.webp","is_new":false,"coming_soon":false,"popularity_score":25,"category_percentile":37,"average_cost":0.043945,"latency_seconds":null,"created_at":"2026-08-06 09:01:07.804846+00:00","parameters_schema":"$1e:props:modelDetails:parameters_schema"},"relatedTemplates":[],"popularModels":[{"slug":"qwen3-vl-flash","title":"Qwen 3 VL Flash","description":"Fast, affordable vision-language model with 262K context OCR.","type":"textToText","partner":"ALIBABA","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-flash-5506.webp","default_image_output":"","is_new":false,"coming_soon":false,"is_visible":true,"segmind":true,"is_depreciated":false,"parameters":null,"latency":1.90857,"average_cost":0.00008282637632338787,"created_at":"2026-03-25 09:44:27.753308+00:00","popularity_score":83,"popularity_scale":"score","category_percentile":98},{"slug":"qwen-flash","title":"Qwen Flash","description":"Fastest low-cost LLM with 1M context for high-volume tasks.","type":"textToText","partner":"ALIBABA","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen-flash-3929.webp","default_image_output":"","is_new":false,"coming_soon":false,"is_visible":true,"segmind":true,"is_depreciated":false,"parameters":null,"latency":1.00187,"average_cost":0.00010557038321167881,"created_at":"2026-03-25 11:20:18.841471+00:00","popularity_score":72,"popularity_scale":"score","category_percentile":93},{"slug":"qwen3-vl-plus","title":"Qwen 3 VL Plus","description":"Powerful visual QA and document analysis from images.","type":"textToText","partner":"ALIBABA","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-plus-3248.webp","default_image_output":"","is_new":false,"coming_soon":false,"is_visible":true,"segmind":true,"is_depreciated":false,"parameters":null,"latency":2.29675,"average_cost":0.000526159748542274,"created_at":"2026-03-25 09:44:27.011897+00:00","popularity_score":72,"popularity_scale":"score","category_percentile":95},{"slug":"qwen-plus","title":"Qwen Plus","description":"Mid-tier 1M context LLM for summarization and content tasks.","type":"textToText","partner":"ALIBABA","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen-plus-5506.webp","default_image_output":"","is_new":false,"coming_soon":false,"is_visible":true,"segmind":true,"is_depreciated":false,"parameters":null,"latency":1.62626,"average_cost":0.0003784640371229699,"created_at":"2026-03-25 11:20:13.259483+00:00","popularity_score":60,"popularity_scale":"score","category_percentile":85}]}]]
