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","qwq-plus"],"i":false,"f":[[["",{"children":["models",{"children":[["route","qwq-plus","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","$L15",{"children":[["route","qwq-plus","d"],"$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:Tf7b,# QwQ Plus â€” Advanced Reasoning Language Model

## What is QwQ Plus?

QwQ Plus is Alibaba Cloud's flagship reasoning language model, built on the QwQ-32B architecture and significantly enhanced through reinforcement learning. Unlike standard language models that respond immediately, QwQ Plus operates in thinking-only mode â€” it always deliberates internally before generating a final answer. This deep reasoning process makes it exceptionally capable on tasks that require multi-step logic, mathematical derivation, and complex problem decomposition.

With a 131,072-token context window and 32.5 billion parameters, QwQ Plus handles long documents, intricate prompts, and multi-turn reasoning sessions with ease. It achieves benchmark performance comparable to DeepSeek-R1 on AIME 24/25 and LiveCodeBench, making it one of the most capable open-weight reasoning models available via API.

## Key Features

- **Thinking-Only Mode**: QwQ Plus always reasons before responding, exposing its chain-of-thought in a `reasoning_content` field for full transparency.
- **131K Token Context**: Processes extensive inputs including long codebases, research papers, and detailed system prompts without truncation.
- **Reinforcement Learning Enhanced**: Post-training via RL dramatically improves accuracy on math, science, and logic tasks.
- **OpenAI-Compatible API**: Integrate directly using the OpenAI SDK â€” no custom client required.
- **DashScope Inference Backend**: Served via Alibaba's high-performance DashScope infrastructure for low-latency production use.

## Best Use Cases

QwQ Plus is purpose-built for tasks where accuracy and reasoning depth matter more than raw speed:

- **Mathematics & Science**: Solve multi-step equations, proofs, and quantitative reasoning problems with verifiable chain-of-thought.
- **Code Generation & Debugging**: Reason through algorithmic challenges, write clean production code, and diagnose complex bugs.
- **Legal & Financial Analysis**: Parse dense documents and synthesize structured conclusions from unstructured text.
- **Research Assistance**: Summarize papers, compare hypotheses, and generate well-reasoned literature reviews.
- **Technical Q&A**: Answer developer and engineering questions with detailed, step-by-step explanations.

## Prompt Tips and Output Quality

QwQ Plus works best when prompts are clear and goal-oriented. For mathematical or coding problems, include all relevant context and constraints upfront. Because the model reasons internally, you will receive both a `reasoning_content` block (the thinking trace) and a `content` block (the final answer) â€” use the reasoning trace to audit correctness or understand the model's approach.

Recommended parameters: temperature 0.6, TopP 0.95, presence penalty 0-2. Avoid greedy decoding (temperature 0) which can produce repetitive outputs.

## FAQs

**What makes QwQ Plus different from Qwen or GPT-4o?**
QwQ Plus is a dedicated reasoning model â€” it always thinks before answering, making it slower but significantly more accurate on hard problems.

**Does QwQ Plus support function calling or tool use?**
QwQ Plus is optimized for deep reasoning text generation. For agentic tool-use workflows, consider pairing it with a planning layer.

**What is the context limit?**
131,072 tokens â€” sufficient for long codebases, research papers, and multi-turn conversations.

**How is billing calculated?**
Input and output tokens are billed separately. Thinking tokens (in `reasoning_content`) are billed as output tokens.

**Is QwQ Plus open source?**
The underlying QwQ-32B weights are open-source on Hugging Face. QwQ Plus is the production-optimized API version served by Alibaba Cloud via Segmind.

**Which model should I use for speed vs. accuracy?**
For maximum accuracy on complex tasks, use QwQ Plus. For faster responses on simpler queries, consider Qwen3-Plus or a smaller Qwen3 model.2a:T96d,# How to Use QwQ Plus Effectively

QwQ Plus is Alibaba Cloud's deep reasoning model, optimized for complex math, code, and logical analysis. Unlike general-purpose LLMs, it always thinks before responding â€” giving you a transparent reasoning trace alongside the final answer. Here is how to get the most out of it.

### Getting Started

Call the model via Segmind's OpenAI-compatible endpoint:

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_SEGMIND_API_KEY",
    base_url="https://api.segmind.com/v1"
)

response = client.chat.completions.create(
    model="qwq-plus",
    messages=[{"role": "user", "content": "Solve: If 3x + 7 = 28, find x and explain your steps."}]
)

# Access both reasoning and final answer
print(response.choices[0].message.reasoning_content)  # Chain of thought
print(response.choices[0].message.content)            # Final answer
```

### Recommended Parameter Settings

- **Temperature**: 0.6 â€” balances creativity and precision; avoid 0 (greedy) to prevent repetition.
- **TopP**: 0.95 â€” maintains diverse, high-quality output.
- **Presence Penalty**: 0.5-1.5 â€” reduces looping in long reasoning traces.
- **Max Tokens**: Set generously (8,000+) â€” the model needs room to think and respond.

### Prompting for Best Results

**For math and logic problems**: State the problem completely, include known constraints, and ask for a step-by-step solution.

**For coding tasks**: Describe the expected input/output behavior, language preference, and any edge cases to handle.

**For analysis and research**: Provide the source material or a precise question, and ask for a structured, evidence-based answer.

**For debugging**: Paste the full code block and error message â€” the model will trace through execution to find the root cause.

### Understanding the Output

QwQ Plus returns two response fields:
- `reasoning_content`: The internal chain-of-thought. Use this to verify the reasoning or audit decisions in high-stakes applications.
- `content`: The polished final answer delivered to end users.

### When to Use QwQ Plus vs. Other Models

Use QwQ Plus when accuracy is more important than speed â€” it is your best choice for competitive math, rigorous code review, complex analysis, and scientific problem-solving. For low-latency or conversational use cases, consider a lighter Qwen3 variant.1e:["$","$L28",null,{"route":"qwq-plus","modelDetails":{"title":"QwQ Plus","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwq-plus-5506.webp","type":"textToText","model_type":"SO","is_depreciated":false,"meta_description":"QwQ Plus delivers deep chain-of-thought reasoning for math, code, and logic with 131K context.","seo_description":"$29","guide":"$2a","pricing_guide":null,"artifacts":[],"default_image_output":"https://segmind-resources.s3.amazonaws.com/output/qwq-plus-example-output.txt","default_response":null,"parameters":{"prompt":{"isRequired":true,"affectsPricing":false,"type":"str","description":"Text input for model reasoning. Detailed for complex tasks.","isEnum":false,"hidden":false,"isAdvanced":false,"displayOrder":0,"component":"textarea","displayValue":"A bat and a ball together cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost? Show your step-by-step reasoning.","label":"prompt","default":"A bat and a ball together cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost? Show your step-by-step reasoning."},"response_format":{"type":"object","format":"json","component":"textarea","label":"Response format","description":"Structured output: {\"type\": \"json_object\"} returns valid JSON (the prompt must mention JSON). This model does not enforce a JSON Schema.","displayValue":{"type":"json_object"},"isRequired":false,"isAdvanced":true,"hidden":false,"affectsPricing":false,"isEnum":false,"displayOrder":1}},"endpoints":{"main":"/v1/qwq-plus"},"api_notes":[],"model_outputs":null,"llm_capabilities":null,"license":"","github_link":null,"inference_provider":"DASHSCOPE","created_ts":"2026-03-24 19:11:25.615195+00:00","slug":"qwq-plus","name":"QwQ Plus","description":"Deep chain-of-thought reasoning for math, code, and logic.","category":"textToText","modality":"llm","input":"text","output":"text","provider":"ALIBABA","thumbnail_url":"https://segmind-resources.s3.amazonaws.com/output/qwq-plus-5506.webp","is_new":false,"coming_soon":false,"popularity_score":6,"category_percentile":15,"average_cost":0.000341,"latency_seconds":3.59534,"created_at":"2026-03-24 19:11:25.615195+00:00","parameters_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"prompt":{"type":"string","title":"Prompt","description":"Text input for model reasoning. Detailed for complex tasks.","default":"A bat and a ball together cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost? Show your step-by-step reasoning.","x-ui-metadata":{"order":0,"advanced":false,"hidden":false}},"response_format":{"type":"object","format":"json","title":"Response format","description":"Structured output. {\"type\": \"json_object\"} returns any valid JSON; this model does not enforce a json_schema. Omit for plain text. The prompt must mention JSON.","examples":[{"type":"json_object"}],"x-ui-metadata":{"order":1,"advanced":true,"hidden":false}}},"required":["prompt"],"examples":[{"prompt":"A bat and a ball together cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost? Show your step-by-step reasoning."}]}},"metrics":{"total_requests":null,"latency":3.59534},"children":"$L2b"}]
23:{"metadata":[["$","title","0",{"children":"QwQ Plus API | Segmind"}],["$","meta","1",{"name":"description","content":"QwQ Plus delivers deep chain-of-thought reasoning for math, code, and logic with 131K context."}],["$","link","2",{"rel":"canonical","href":"https://www.segmind.com/models/qwq-plus"}],["$","meta","3",{"property":"og:title","content":"QwQ Plus API | Segmind"}],["$","meta","4",{"property":"og:description","content":"QwQ Plus delivers deep chain-of-thought reasoning for math, code, and logic with 131K context."}],["$","meta","5",{"property":"og:image","content":"https://segmind-resources.s3.amazonaws.com/output/qwq-plus-5506.webp"}],["$","meta","6",{"name":"twitter:card","content":"summary_large_image"}],["$","meta","7",{"name":"twitter:title","content":"QwQ Plus API | Segmind"}],["$","meta","8",{"name":"twitter:description","content":"QwQ Plus delivers deep chain-of-thought reasoning for math, code, and logic with 131K context."}],["$","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:T62c,{"@context":"https://schema.org","@type":"FAQPage","@id":"https://www.segmind.com/models/qwq-plus#faq","mainEntity":[{"@type":"Question","name":"What makes QwQ Plus different from Qwen or GPT-4o?","acceptedAnswer":{"@type":"Answer","text":"QwQ Plus is a dedicated reasoning model â€” it always thinks before answering, making it slower but significantly more accurate on hard problems."}},{"@type":"Question","name":"Does QwQ Plus support function calling or tool use?","acceptedAnswer":{"@type":"Answer","text":"QwQ Plus is optimized for deep reasoning text generation. For agentic tool-use workflows, consider pairing it with a planning layer."}},{"@type":"Question","name":"What is the context limit?","acceptedAnswer":{"@type":"Answer","text":"131,072 tokens â€” sufficient for long codebases, research papers, and multi-turn conversations."}},{"@type":"Question","name":"How is billing calculated?","acceptedAnswer":{"@type":"Answer","text":"Input and output tokens are billed separately. Thinking tokens (in reasoning_content) are billed as output tokens."}},{"@type":"Question","name":"Is QwQ Plus open source?","acceptedAnswer":{"@type":"Answer","text":"The underlying QwQ-32B weights are open-source on Hugging Face. QwQ Plus is the production-optimized API version served by Alibaba Cloud via Segmind."}},{"@type":"Question","name":"Which model should I use for speed vs. accuracy?","acceptedAnswer":{"@type":"Answer","text":"For maximum accuracy on complex tasks, use QwQ Plus. For faster responses on simpler queries, consider Qwen3-Plus or a smaller Qwen3 model."}}]}2f:Tf7b,# QwQ Plus â€” Advanced Reasoning Language Model

## What is QwQ Plus?

QwQ Plus is Alibaba Cloud's flagship reasoning language model, built on the QwQ-32B architecture and significantly enhanced through reinforcement learning. Unlike standard language models that respond immediately, QwQ Plus operates in thinking-only mode â€” it always deliberates internally before generating a final answer. This deep reasoning process makes it exceptionally capable on tasks that require multi-step logic, mathematical derivation, and complex problem decomposition.

With a 131,072-token context window and 32.5 billion parameters, QwQ Plus handles long documents, intricate prompts, and multi-turn reasoning sessions with ease. It achieves benchmark performance comparable to DeepSeek-R1 on AIME 24/25 and LiveCodeBench, making it one of the most capable open-weight reasoning models available via API.

## Key Features

- **Thinking-Only Mode**: QwQ Plus always reasons before responding, exposing its chain-of-thought in a `reasoning_content` field for full transparency.
- **131K Token Context**: Processes extensive inputs including long codebases, research papers, and detailed system prompts without truncation.
- **Reinforcement Learning Enhanced**: Post-training via RL dramatically improves accuracy on math, science, and logic tasks.
- **OpenAI-Compatible API**: Integrate directly using the OpenAI SDK â€” no custom client required.
- **DashScope Inference Backend**: Served via Alibaba's high-performance DashScope infrastructure for low-latency production use.

## Best Use Cases

QwQ Plus is purpose-built for tasks where accuracy and reasoning depth matter more than raw speed:

- **Mathematics & Science**: Solve multi-step equations, proofs, and quantitative reasoning problems with verifiable chain-of-thought.
- **Code Generation & Debugging**: Reason through algorithmic challenges, write clean production code, and diagnose complex bugs.
- **Legal & Financial Analysis**: Parse dense documents and synthesize structured conclusions from unstructured text.
- **Research Assistance**: Summarize papers, compare hypotheses, and generate well-reasoned literature reviews.
- **Technical Q&A**: Answer developer and engineering questions with detailed, step-by-step explanations.

## Prompt Tips and Output Quality

QwQ Plus works best when prompts are clear and goal-oriented. For mathematical or coding problems, include all relevant context and constraints upfront. Because the model reasons internally, you will receive both a `reasoning_content` block (the thinking trace) and a `content` block (the final answer) â€” use the reasoning trace to audit correctness or understand the model's approach.

Recommended parameters: temperature 0.6, TopP 0.95, presence penalty 0-2. Avoid greedy decoding (temperature 0) which can produce repetitive outputs.

## FAQs

**What makes QwQ Plus different from Qwen or GPT-4o?**
QwQ Plus is a dedicated reasoning model â€” it always thinks before answering, making it slower but significantly more accurate on hard problems.

**Does QwQ Plus support function calling or tool use?**
QwQ Plus is optimized for deep reasoning text generation. For agentic tool-use workflows, consider pairing it with a planning layer.

**What is the context limit?**
131,072 tokens â€” sufficient for long codebases, research papers, and multi-turn conversations.

**How is billing calculated?**
Input and output tokens are billed separately. Thinking tokens (in `reasoning_content`) are billed as output tokens.

**Is QwQ Plus open source?**
The underlying QwQ-32B weights are open-source on Hugging Face. QwQ Plus is the production-optimized API version served by Alibaba Cloud via Segmind.

**Which model should I use for speed vs. accuracy?**
For maximum accuracy on complex tasks, use QwQ Plus. For faster responses on simpler queries, consider Qwen3-Plus or a smaller Qwen3 model.30:T96d,# How to Use QwQ Plus Effectively

QwQ Plus is Alibaba Cloud's deep reasoning model, optimized for complex math, code, and logical analysis. Unlike general-purpose LLMs, it always thinks before responding â€” giving you a transparent reasoning trace alongside the final answer. Here is how to get the most out of it.

### Getting Started

Call the model via Segmind's OpenAI-compatible endpoint:

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_SEGMIND_API_KEY",
    base_url="https://api.segmind.com/v1"
)

response = client.chat.completions.create(
    model="qwq-plus",
    messages=[{"role": "user", "content": "Solve: If 3x + 7 = 28, find x and explain your steps."}]
)

# Access both reasoning and final answer
print(response.choices[0].message.reasoning_content)  # Chain of thought
print(response.choices[0].message.content)            # Final answer
```

### Recommended Parameter Settings

- **Temperature**: 0.6 â€” balances creativity and precision; avoid 0 (greedy) to prevent repetition.
- **TopP**: 0.95 â€” maintains diverse, high-quality output.
- **Presence Penalty**: 0.5-1.5 â€” reduces looping in long reasoning traces.
- **Max Tokens**: Set generously (8,000+) â€” the model needs room to think and respond.

### Prompting for Best Results

**For math and logic problems**: State the problem completely, include known constraints, and ask for a step-by-step solution.

**For coding tasks**: Describe the expected input/output behavior, language preference, and any edge cases to handle.

**For analysis and research**: Provide the source material or a precise question, and ask for a structured, evidence-based answer.

**For debugging**: Paste the full code block and error message â€” the model will trace through execution to find the root cause.

### Understanding the Output

QwQ Plus returns two response fields:
- `reasoning_content`: The internal chain-of-thought. Use this to verify the reasoning or audit decisions in high-stakes applications.
- `content`: The polished final answer delivered to end users.

### When to Use QwQ Plus vs. Other Models

Use QwQ Plus when accuracy is more important than speed â€” it is your best choice for competitive math, rigorous code review, complex analysis, and scientific problem-solving. For low-latency or conversational use cases, consider a lighter Qwen3 variant.1f:[["$","script",null,{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"SoftwareApplication\",\"@id\":\"https://www.segmind.com/models/qwq-plus\",\"name\":\"QwQ Plus\",\"description\":\"QwQ Plus delivers deep chain-of-thought reasoning for math, code, and logic with 131K context.\",\"url\":\"https://www.segmind.com/models/qwq-plus\",\"applicationCategory\":\"AI Model\",\"applicationSubCategory\":\"textToText\",\"operatingSystem\":\"Any\",\"image\":\"https://segmind-resources.s3.amazonaws.com/output/qwq-plus-5506.webp\",\"offers\":{\"@type\":\"Offer\",\"availability\":\"https://schema.org/InStock\",\"priceCurrency\":\"USD\",\"price\":\"0.000341\"},\"provider\":{\"@type\":\"Organization\",\"@id\":\"https://www.segmind.com/#organization\",\"name\":\"Segmind\",\"url\":\"https://www.segmind.com\",\"logo\":\"https://www.segmind.com/favicon.svg\"}}"}}],["$","script",null,{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"$2d"}}],null,["$","$L2e",null,{"route":"qwq-plus","modelDetails":{"title":"QwQ Plus","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwq-plus-5506.webp","type":"textToText","model_type":"SO","is_depreciated":false,"meta_description":"QwQ Plus delivers deep chain-of-thought reasoning for math, code, and logic with 131K context.","seo_description":"$2f","guide":"$30","pricing_guide":null,"artifacts":"$1e:props:modelDetails:artifacts","default_image_output":"https://segmind-resources.s3.amazonaws.com/output/qwq-plus-example-output.txt","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":"DASHSCOPE","created_ts":"2026-03-24 19:11:25.615195+00:00","slug":"qwq-plus","name":"QwQ Plus","description":"Deep chain-of-thought reasoning for math, code, and logic.","category":"textToText","modality":"llm","input":"text","output":"text","provider":"ALIBABA","thumbnail_url":"https://segmind-resources.s3.amazonaws.com/output/qwq-plus-5506.webp","is_new":false,"coming_soon":false,"popularity_score":6,"category_percentile":15,"average_cost":0.000341,"latency_seconds":3.59534,"created_at":"2026-03-24 19:11:25.615195+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}]}]]
