1:"$Sreact.fragment"
2:I[6529,["619","static/chunks/619-ba102abea3e3d0e4.js","177","static/chunks/app/layout-13fa3fd02f6eb5db.js"],"default"]
3:I[9766,[],""]
4:I[8924,[],""]
5:I[2619,["619","static/chunks/619-ba102abea3e3d0e4.js","953","static/chunks/app/blog/%5Bslug%5D/page-f25a122e9ccf798d.js"],""]
d:I[7150,[],""]
:HL["/_next/static/css/1d1f6bc532e5f43f.css","style"]
:HL["/_next/static/css/eb87e4f7aea490c6.css","style"]
0:{"P":null,"b":"DQJo8iKQxHubJM4JvsRbH","p":"","c":["","blog","qwen2.5-max",""],"i":false,"f":[[["",{"children":["blog",{"children":[["slug","qwen2.5-max","d"],{"children":["__PAGE__",{}]}]}]},"$undefined","$undefined",true],["",["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/_next/static/css/1d1f6bc532e5f43f.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]],["$","html",null,{"lang":"en","children":[["$","head",null,{"children":[["$","link",null,{"rel":"icon","type":"image/svg+xml","href":"/favicon.svg"}],["$","link",null,{"rel":"alternate icon","href":"/favicon.png"}]]}],["$","body",null,{"children":[["$","$L2",null,{}],["$","$L3",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L4",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":404}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],[]],"forbidden":"$undefined","unauthorized":"$undefined"}],["$","footer",null,{"children":["$","div",null,{"className":"container","children":[["$","div",null,{"className":"footer-content","children":[["$","div",null,{"className":"footer-section","children":[["$","h4",null,{"children":"Zen LM"}],["$","p",null,{"children":"95 open Zen models across Zen3, Zen4, and Zen5. Chat, code, vision, audio, image, embeddings, rerankers, and safety. OpenAI- and Anthropic-compatible API."}]]}],["$","div",null,{"className":"footer-section","children":[["$","h4",null,{"children":"Zen 5"}],["$","ul",null,{"children":[["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen5","children":"Zen5 Nano (0.8B - 9B)"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen5","children":"Zen5 Flash"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen5","children":"Zen5 Mini"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen5","children":"Zen5 (default)"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen5","children":"Zen5 Coder"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen5","children":"Zen5 Pro"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen5","children":"Zen5 Max"}]}]]}]]}],["$","div",null,{"className":"footer-section","children":[["$","h4",null,{"children":"Zen 4"}],["$","ul",null,{"children":[["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen4","children":"Zen4 / Zen4.1"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen4","children":"Zen4 Ultra / Max / Pro"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen4","children":"Zen4 Mini / Thinking"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen4","children":"Zen4 Coder / Pro / Flash"}]}]]}]]}],["$","div",null,{"className":"footer-section","children":[["$","h4",null,{"children":"Zen 3 Multimodal"}],["$","ul",null,{"children":[["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen3","children":"Zen3 Omni / VL / Web"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen3","children":"Zen3 Nano / Guard"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen3","children":"Zen3 Embedding / Reranker"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/models#zen3","children":"Zen3 Image / ASR / TTS"}]}]]}]]}],["$","div",null,{"className":"footer-section","children":[["$","h4",null,{"children":"Resources"}],["$","ul",null,{"children":[["$","li",null,{"children":["$","$L5",null,{"href":"/datasets","children":"Training Data"}]}],["$","li",null,{"children":["$","a",null,{"href":"https://huggingface.co/zenlm","target":"_blank","rel":"noopener noreferrer","children":"HuggingFace"}]}],["$","li",null,{"children":["$","a",null,{"href":"https://github.com/zenlm","target":"_blank","rel":"noopener noreferrer","children":"GitHub"}]}],["$","li",null,{"children":["$","$L5",null,{"href":"/research","children":"Research Papers"}]}],["$","li",null,{"children":["$","a",null,{"href":"https://api.hanzo.ai","target":"_blank","rel":"noopener noreferrer","children":"Zen API"}]}]]}]]}]]}],"$L6"]}]}],"$L7","$L8"]}]]}]]}],{"children":["blog","$L9",{"children":[["slug","qwen2.5-max","d"],"$La",{"children":["__PAGE__","$Lb",{},null,false]},null,false]},null,false]},null,false],"$Lc",false]],"m":"$undefined","G":["$d",[]],"s":false,"S":true}
e:I[7405,["619","static/chunks/619-ba102abea3e3d0e4.js","177","static/chunks/app/layout-13fa3fd02f6eb5db.js"],"default"]
10:I[4431,[],"OutletBoundary"]
12:I[5278,[],"AsyncMetadataOutlet"]
14:I[4431,[],"ViewportBoundary"]
16:I[4431,[],"MetadataBoundary"]
17:"$Sreact.suspense"
6:["$","div",null,{"className":"footer-bottom","children":["$","p",null,{"children":["© ",2026," Zen Authors. Open foundation models. Served on the Zen API."]}]}]
7:["$","$Le",null,{}]
8:["$","script",null,{"src":"/assets/js/main.js","async":true}]
9:["$","$1","c",{"children":[null,["$","$L3",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L4",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}]]}]
a:["$","$1","c",{"children":[null,["$","$L3",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L4",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}]]}]
b:["$","$1","c",{"children":["$Lf",[["$","link","0",{"rel":"stylesheet","href":"/_next/static/css/eb87e4f7aea490c6.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]],["$","$L10",null,{"children":["$L11",["$","$L12",null,{"promise":"$@13"}]]}]]}]
c:["$","$1","h",{"children":[null,[["$","$L14",null,{"children":"$L15"}],null],["$","$L16",null,{"children":["$","div",null,{"hidden":true,"children":["$","$17",null,{"fallback":null,"children":"$L18"}]}]}]]}]
19:T1509,<figure><img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/zen-max-banner.png" alt="" loading="lazy"></figure>
<p><a href="https://chat.qwenlm.ai">QWEN CHAT</a>
<a href="https://www.alibabacloud.com/help/en/model-studio/developer-reference/what-is-qwen-llm">API</a>
<a href="https://huggingface.co/spaces/Qwen/zen-Max-Demo">DEMO</a>
<a href="https://discord.gg/yPEP2vHTu4">DISCORD</a></p>
<p>It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in effectively scaling extremely large models, whether they are dense or Mixture-of-Expert (MoE) models. Many critical details regarding this scaling process were only disclosed with the recent release of DeepSeek V3. Concurrently, we are developing zen-Max, a large-scale MoE model that has been pretrained on over 20 trillion tokens and further post-trained with curated Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) methodologies. Today, we are excited to share the performance results of zen-Max and announce the availability of its <a href="https://www.alibabacloud.com/help/en/model-studio/getting-started/">API</a> through Alibaba Cloud. We also invite you to explore zen-Max on <a href="https://chat.qwenlm.ai">Qwen Chat</a>!</p>
<h2 id="performance">Performance</h2>
<p>We evaluate zen-Max alongside leading models, whether proprietary or open-weight, across a range of benchmarks that are of significant interest to the community. These include MMLU-Pro, which tests knowledge through college-level problems, LiveCodeBench, which assesses coding capabilities, LiveBench, which comprehensively tests the general capabilities, and Arena-Hard, which approximates human preferences. Our findings include the performance scores for both base models and instruct models.</p>
<p>We begin by directly comparing the performance of the instruct models, which can serve for downstream applications such as chat and coding. We present the performance results of zen-Max alongside leading state-of-the-art models, including DeepSeek V3, GPT-4o, and Claude-3.5-Sonnet.</p>
<figure><img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/zen-max-instruct.jpg" alt="" loading="lazy"></figure>
<p>zen-Max outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro.</p>
<p>When comparing base models, we are unable to access the proprietary models such as GPT-4o and Claude-3.5-Sonnet. Therefore, we evaluate zen-Max against DeepSeek V3, a leading open-weight MoE model, Llama-3.1-405B, the largest open-weight dense model, and zen-72B, which is also among the top open-weight dense models. The results of this comparison are presented below.</p>
<figure><img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/zen-Max.jpeg" alt="" loading="lazy"></figure>
<p>Our base models have demonstrated significant advantages across most benchmarks, and we are optimistic that advancements in post-training techniques will elevate the next version of zen-Max to new heights.</p>
<h2 id="use-zen-max">Use zen-Max</h2>
<p>Now zen-Max is available in Qwen Chat, and you can directly chat with the model, or play with artifacts, search, etc.</p>
<video width="100%" autoplay loop muted playsinline>
    <source src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/qwen-max.mp4" type="video/mp4">
</video>
<p>The API of zen-Max (whose model name is <code>qwen-max-2025-01-25</code>) is available. You can first <a href="https://account.alibabacloud.com/register/intl_register.htm">register an Alibaba Cloud account</a> and activate Alibaba Cloud Model Studio service, and then navigate to the console and create an API key.</p>
<p>Since the APIs of Qwen are OpenAI-API compatible, we can directly follow the common practice of using OpenAI APIs. Below is an example of using zen-Max in Python:</p>
<pre><code class="language-python">from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen-max-2025-01-25",
    messages=[
      {'role': 'system', 'content': 'You are a helpful assistant.'},
      {'role': 'user', 'content': 'Which number is larger, 9.11 or 9.8?'}
    ]
)

print(completion.choices[0].message)
</code></pre>
<h2 id="future-work">Future Work</h2>
<p>The scaling of data and model size not only showcases advancements in model intelligence but also reflects our unwavering commitment to pioneering research. We are dedicated to enhancing the thinking and reasoning capabilities of large language models through the innovative application of scaled reinforcement learning. This endeavor holds the promise of enabling our models to transcend human intelligence, unlocking the potential to explore uncharted territories of knowledge and understanding.</p>
<h1 id="citation">Citation</h1>
<p>Feel free to cite the following article if you find zen helpful.</p>
<pre><code>@article{qwen25,
  title={zen technical report},
  author={Qwen Team},
  journal={arXiv preprint arXiv:2412.15115},
  year={2024}
}
</code></pre>f:["$","main",null,{"children":["$","article",null,{"className":"blog-article","children":[["$","$L5",null,{"className":"blog-back","href":"/blog","children":"← Blog"}],["$","div",null,{"className":"blog-post-meta","children":["January 27, 2025"," ","·"," ",3," min read"]}],["$","h1",null,{"className":"blog-post-title","children":"zen-Max: Exploring the Intelligence of Large-scale MoE Model"}],["$","p",null,{"className":"blog-post-lede","children":"It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in effectively scaling extremely large models, whether they are dense or Mixture-of-Expert ("}],["$","div",null,{"className":"blog-prose","dangerouslySetInnerHTML":{"__html":"$19"}}]]}]}]
15:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]
11:null
13:{"metadata":[["$","title","0",{"children":"zen-Max: Exploring the Intelligence of Large-scale MoE Model — Zen Blog"}],["$","meta","1",{"name":"description","content":"It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in effectively scaling extremely large models, whether they are dense or Mixture-of-Expert ("}],["$","meta","2",{"name":"keywords","content":"AI, LLM, Agentic AI, Code Generation, Zen Coder, Multimodal, Open Source, Machine Learning"}]],"error":null,"digest":"$undefined"}
18:"$13:metadata"
