Alibaba Qwen Releases Qwen3.8-Max: A 2.4 Trillion Parameter MoE Model and the Most Capable One in the Qwen Family to Date

Alibaba’s Qwen team has made Qwen3.8-Max broadly available and confirmed that its open weights ship next week. A second checkpoint, Qwen3.8-27B, is also going open-weights. Qwen3.8-Max is a 2.4-trillion-parameter mixture-of-experts model. It accepts text, image and video as input and returns text.

Is it deployable

Yes, but the deployable surface depends on which artifact you are applying.

The hosted API is deployable today by any company size. It is OpenAI- and DashScope-compatible, so integration is a base-URL and model-ID change. The open weights are a different matter. At 2.4T total parameters, the checkpoint is a multi-node datacenter artifact. Alibaba has not disclosed the activated-parameter count. Serving cost therefore cannot yet be modeled. Qwen3.8-27B is the checkpoint that fits ordinary on-premise GPU hardware.

The published feature set maps cleanly onto four industries. Those are software engineering, legal and financial document review, media and e-commerce operations, and design.

Applications include repository-scale coding agents and long-document knowledge bases. Long-video indexing, structured data extraction and multi-step research assistants also fit.

Interactive explainer

<![CDATA[<![CDATA[<![CDATA[vals[fi])win++} var el=d.createElement('div');el.className="bm-r"; var h=" <div class="bm-n"><span>“+row[0]+’</span><em>‘+(vals[qi]!==null&&vals[fi]!==null?(vals[qi]>vals[fi]?’above’:vals[qi]<vals[fi]?'below':'ties')+' Fable5':'')+'</em></div> ‘; C.m.forEach(function(mn,j){ var v=vals[j],raw=disp(row[j+1]); var w=(v===null||!mx)?0:(v/mx*100); h+=’ <div class="bm-l'+(j===qi?' q':'')+(j===best&&v!==null?' w':'')+'">‘+ ‘<span class="m">‘+mn+’</span><span class="t"><span class="f" data-w="'+w.toFixed(1)+'"></span></span>‘+ ‘<span class="v">‘+raw+’</span></div> ‘}); el.innerHTML=h;L.appendChild(el)}); q(‘#bmw’).textContent=”Qwen3.8-Max above Fable5 on “+win+’ of ‘+cmp+’ comparable rows’; setTimeout(function(){qa(‘#bml .f’).forEach(function(f){f.style.width=f.dataset.w+’%’})},40); setTimeout(rs,140);} /* RL curve */ var RL=[[0,.474],[500,.469],[1000,.487],[1500,.586],[2000,.606],[2500,.622],[3000,.616],[3500,.647],[4000,.725],[4500,.719],[5000,.689]]; var rlDone=false; function paintRl(){ if(rlDone)return;rlDone=true; var g=q(‘#rlg’),X=function(v){return 44+v/5000*570},Y=function(v){return 210-(v-.44)/(.76-.44)*180}; function mk(t,a){var e=d.createElementNS(‘http://www.w3.org/2000/svg’,t); for(var k in a)e.setAttribute(k,a[k]);g.appendChild(e);return e} [.45,.5,.55,.6,.65,.7,.75].forEach(function(v){ mk(‘line’,{x1:44,y1:Y(v),x2:614,y2:Y(v),stroke:’#1d1830′,’stroke-width’:1}); var t=mk(‘text’,{x:6,y:Y(v)+3,class:’lbl’});t.textContent=v.toFixed(2)}); mk(‘line’,{x1:44,y1:Y(.474),x2:614,y2:Y(.474),stroke:’#4a4170′,’stroke-width’:1,’stroke-dasharray’:’4 4′}); var sb=mk(‘text’,{x:470,y:Y(.474)+14,class:’lbl’});sb.textContent=”SFT baseline 0.474″; var pts=RL.map(function(p){return X(p[0])+’,’+Y(p[1])}).join(‘ ‘); var pl=mk(‘polyline’,{points:pts,fill:’none’,stroke:’#7C5CFF’,’stroke-width’:2.6,’stroke-linejoin’:’round’,’stroke-linecap’:’round’}); var len=pl.getTotalLength?pl.getTotalLength():2000; pl.setAttribute(‘stroke-dasharray’,len);pl.setAttribute(‘stroke-dashoffset’,len); var an=d.createElementNS(‘http://www.w3.org/2000/svg’,’animate’); an.setAttribute(‘attributeName’,’stroke-dashoffset’);an.setAttribute(‘from’,len);an.setAttribute(‘to’,0); an.setAttribute(‘dur’,’1.5s’);an.setAttribute(‘fill’,’freeze’);pl.appendChild(an); RL.forEach(function(p,i){ var pk=p[1]===.725; var c=mk(‘circle’,{cx:X(p[0]),cy:Y(p[1]),r:pk?6:3.6,fill:pk?’#B49CFF’:’#7C5CFF’,opacity:0}); var a2=d.createElementNS(‘http://www.w3.org/2000/svg’,’animate’); a2.setAttribute(‘attributeName’,’opacity’);a2.setAttribute(‘from’,0);a2.setAttribute(‘to’,1); a2.setAttribute(‘dur’,’.3s’);a2.setAttribute(‘begin’,(i*0.13)+’s’);a2.setAttribute(‘fill’,’freeze’);c.appendChild(a2); var t=mk(‘text’,{x:X(p[0]),y:Y(p[1])-12,’text-anchor’:’middle’,class:’lbl’,fill:pk?’#B49CFF’:’#8b81ab’,opacity:0}); t.textContent=p[1].toFixed(3); var a3=d.createElementNS(‘http://www.w3.org/2000/svg’,’animate’); a3.setAttribute(‘attributeName’,’opacity’);a3.setAttribute(‘from’,0);a3.setAttribute(‘to’,1); a3.setAttribute(‘dur’,’.3s’);a3.setAttribute(‘begin’,(i*0.13)+’s’);a3.setAttribute(‘fill’,’freeze’);t.appendChild(a3); if(i%2===0){var xl=mk(‘text’,{x:X(p[0]),y:228,’text-anchor’:’middle’,class:’lbl’});xl.textContent=p[0]}}); var pk=mk(‘text’,{x:X(4000),y:Y(.725)-26,’text-anchor’:’middle’,class:’lbl-b’,fill:’#B49CFF’}); pk.textContent=”peak”; setTimeout(rs,200)} /* cross-harness */ var HZ=[ {n:’CoWorkBench’,b:[[‘Fable5 (OpenClaw)’,75.9,0],[‘Opus4.8 (OpenClaw)’,72.3,0],[‘Qwen3.7-Max (OpenClaw)’,64.6,0],[‘QwenWork’,73.2,1],[‘Claude Code’,74.6,1],[‘Codex’,75.8,1],[‘OpenClaw’,74.8,1],[‘Hermes’,73.3,1]]}, {n:’WorkspaceBench’,b:[[‘Fable5 (OpenClaw)’,68.7,0],[‘Opus4.8 (OpenClaw)’,66.8,0],[‘Qwen3.7-Max (OpenClaw)’,61.4,0],[‘QwenWork’,67.0,1],[‘Claude Code’,67.3,1],[‘Codex’,67.6,1],[‘OpenClaw’,67.7,1],[‘Hermes’,71.2,1]]}, {n:’JobBench’,b:[[‘Fable5 (OpenCode)’,57.4,0],[‘Opus4.8 (OpenCode)’,48.4,0],[‘Qwen3.7-Max (OpenCode)’,31.3,0],[‘QwenWork’,58.0,1],[‘Claude Code’,59.0,1],[‘Codex’,57.5,1],[‘OpenClaw’,59.8,1],[‘Hermes’,59.6,1],[‘OpenCode’,53.4,1]]}]; HZ.forEach(function(h){ var c=d.createElement(‘div’);c.className=”hc”; var s=” <h4>“+h.n+’</h4> ‘; var mx=Math.max.apply(null,h.b.map(function(x){return x[1]})); h.b.forEach(function(b){s+=’ <div class="bm-l'+(b[2]?' q':'')+'">‘+ ‘<span class="m">‘+b[0]+’</span><span class="t"><span class="f" style="width:'+(b[1]/mx*100)+'%"></span></span>‘+ ‘<span class="v">‘+b[1].toFixed(1)+’</span></div> ‘}); c.innerHTML=s;q(‘#hzl’).appendChild(c)}); /* deploy */ var DEP=[ ‘<b>Path: hosted API only.</b> Call model ID <code>qwen3.8-max</code> through the DashScope or OpenAI-compatible endpoint. Self-hosting 2.4T weights is out of reach. Best fits: coding agents, document and video ingestion, research assistants. Owner: the founding engineer or a solo AI engineer.’, ‘<b>Path: API first, 27B for anything sensitive.</b> Use the hosted flagship for long-horizon agent work and keep <b>Qwen3.8-27B</b> on your own GPUs for PII-bearing or high-volume routing. Best fits: support automation, analytics copilots, internal tooling. Owner: an ML platform team.’, ‘<b>Path: both, with an evaluation gate.</b> Multi-node clusters can host the open weights once activated parameters and license are published. Until then run the API behind your own harness. The cross-harness numbers suggest portability across Claude Code, Codex and OpenClaw. Owner: an applied AI group reporting to the CTO.’, ‘<b>Path: wait, then take 27B.</b> No license file has been published for Qwen3.8-Max, so procurement cannot clear it yet. Air-gapped deployment realistically means <b>Qwen3.8-27B</b>. Best fits: healthcare, defense, public sector. Owner: security architecture plus a data science lead.’]; qa(‘#who button’).forEach(function(b){b.onclick=function(){ qa(‘#who button’).forEach(function(x){x.classList.remove(‘on’)}); b.classList.add(‘on’);q(‘#dep’).innerHTML=DEP[+b.dataset.w];setTimeout(rs,60)}}); q(‘#dep’).innerHTML=DEP[0]; /* evidence */ var EV=[[ ‘Full benchmark table across coding, agent, reasoning, multimodal, document, video’, ‘2.4 trillion total parameters, mixture-of-experts’, ‘1M context; 991K max input, 131K max output, 262K max reasoning’, ‘Text, image and video input; text output’, ‘$2.00 input, $6.00 output, $0.25 implicit cache read per 1M tokens’, ‘Built-in code_interpreter, web_search, web_extractor, t2i_search, i2i_search’, ‘RL scaling curve, including the decline past the 4,000-environment peak’, ‘Cross-harness results on QwenWork, Claude Code, Codex, OpenClaw, Hermes’, ‘Open weights for Qwen3.8-Max and Qwen3.8-27B announced for next week’ ],[ ‘Model card’, ‘License file’, ‘Activated parameters per token’, ‘Independent evaluation from Artificial Analysis or LMArena’, ‘Harness, attempt count and reasoning settings behind each score’, ‘Note: the multimodal table compares against Qwen3.7-Plus, not Qwen3.7-Max’ ]]; var evMode=0; function paintEv(){var L=q(‘#evl’);L.innerHTML=”; EV[evMode].forEach(function(t,i){var e=d.createElement(‘div’);e.className=”q-ev”; e.style.animationDelay=(i*55)+’ms’; e.innerHTML=’<span class="pip '+(evMode?'n':'y')+'">‘+(evMode?’?’:’✓’)+’</span><span>‘+t+’</span>‘; L.appendChild(e)});setTimeout(rs,120)} qa(‘#evp button’).forEach(function(b){b.onclick=function(){ qa(‘#evp button’).forEach(function(x){x.classList.remove(‘on’)}); b.classList.add(‘on’);evMode=+b.dataset.e;paintEv()}}); paintEv();paintBm(); window.addEventListener(‘load’,rs);setTimeout(rs,300);setTimeout(rs,900); })(); </script> ” style=”width:100%;height:600px;border:0;overflow:hidden;display:block” scrolling=”no” loading=”lazy” title=”Qwen3.8-Max Interactive Explainer”>]]]]]]><![CDATA[><![CDATA[>]]]]><![CDATA[>]]>

What is Technically Available

The model page lists a 1M-token context window. Maximum input is 991K tokens, dropping to 983K when thinking is enabled. Maximum output is 131K tokens in both modes, and the maximum reasoning budget is 262K tokens. Rate limits are 2M tokens per minute and 15K requests per minute.

Pricing is $2.00 per 1M input tokens and $6.00 per 1M output tokens. Implicit cache reads cost $0.25 per 1M tokens. Explicit cache creation is $2.50 and explicit cache reads are $0.17 per 1M tokens. Cached input is eight times cheaper than fresh input. Prefix stability therefore drives cost more than prompt length does.

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Supported capabilities include function calling, structured outputs, batches, prefix completion and fine-tuning. Five built-in tools ship on the Responses API: code_interpreter, web_search, web_extractor, t2i_search and i2i_search.

Performance

Alibaba published a full benchmark table with this release. Qwen3.8-Max scores 86.6 on Terminal-Bench 2.1, ahead of Claude Opus 4.8 and Claude Fable 5 at 84.6, behind GPT-5.6 Sol (max) at 88.8. It reports 67.7 on SWE-bench Pro against Fable 5’s 80.0, and 73.5 on FrontierSWE against Fable 5’s 88.8. It leads PaperBench at 93.0 and IFBench at 82.8. GPQA Diamond lands at 92.6, up marginally from Qwen3.7-Max’s 92.4. The clearest gains are multimodal and agentic, not reasoning. It tops most vision rows, including OSWorld-Verified 86.1, Parametric CAD Bench 91.5, and OmniDocBench 1.5 at 92.1. Against its own predecessor the jump is large: DeepSWE 1.1 moves from 21.6 to 56.6, FrontierSWE from 40.7 to 73.5, JobBench from 31.3 to 53.4. Two caveats belong in any honest read. The multimodal table benchmarks against Qwen3.7-Plus, not Qwen3.7-Max, which flatters the generational delta. And Alibaba’s own RL scaling curve peaks at 0.725 near 4,000 training environments, then declines to 0.719 and 0.689.

Key Takeaways

Qwen3.8-Max is a 2.4T-parameter MoE model with 1M context, now generally available.
Pricing is $2 input, $6 output and $0.25 cached input per 1M tokens.
Open weights for Qwen3.8-Max and Qwen3.8-27B are promised next week.
No benchmark table, license, or activated-parameter count has been published.
The 27B checkpoint, not the flagship, is the realistic on-premise deployment path.

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