[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-qwen38-max-top-tier-claude-comparison-en":3,"article-related-qwen38-max-top-tier-claude-comparison-en":29,"series-model-release-3531feda-a023-4b38-9f1c-69f6b1a37f8e":76},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":22,"views":26,"created_at":27,"published_at":28,"topic_cluster_id":11},"3531feda-a023-4b38-9f1c-69f6b1a37f8e","qwen38-max-top-tier-claude-comparison-en","Qwen3.8-Max pushes Alibaba into the top tier","\u003Cp data-speakable=\"summary\">Alibaba’s Qwen3.8-Max-Preview is a 2.4T-parameter model that rivals top \u003Ca href=\"\u002Ftag\u002Fclaude\">Claude\u003C\u002Fa> systems.\u003C\u002Fp>\u003Cp>Alibaba has released \u003Ca href=\"https:\u002F\u002Fwww.qianwen.com\u002F\" target=\"_blank\" rel=\"noopener\">Qwen3.8-Max-Preview\u003C\u002Fa>, and the numbers are hard to ignore: 2.4 trillion parameters, stronger results in coding and \u003Ca href=\"\u002Ftag\u002Fagent\">agent\u003C\u002Fa> tasks, and API pricing that undercuts several premium Western models. In the latest Arena rankings, it climbed into the top tier of large models and drew direct comparisons with \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fclaude\" target=\"_blank\" rel=\"noopener\">Anthropic’s Claude\u003C\u002Fa> family.\u003C\u002Fp>\u003Cp>The pitch is simple enough to understand even before you look at the demos: Qwen3.8-Max is trying to be the model people can actually afford to use every day. Alibaba says domestic pricing starts at 12 yuan per million input tokens and 36 yuan per million output tokens, with cached input at 1.5 yuan. International pricing is lower than the headline Claude Opus 5 rates cited in the source article.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Metric\u003C\u002Fth>\u003Cth>Qwen3.8-Max-Preview\u003C\u002Fth>\u003Cth>Source detail\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Parameter count\u003C\u002Ftd>\u003Ctd>2.4T\u003C\u002Ftd>\u003Ctd>Largest Qwen model to date\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Domestic input price\u003C\u002Ftd>\u003Ctd>12 yuan \u002F 1M tokens\u003C\u002Ftd>\u003Ctd>Cached input: 1.5 yuan \u002F 1M tokens\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Domestic output price\u003C\u002Ftd>\u003Ctd>36 yuan \u002F 1M tokens\u003C\u002Ftd>\u003Ctd>Positioned as lower-cost than premium rivals\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Release timing\u003C\u002Ftd>\u003Ctd>2026-07-19\u003C\u002Ftd>\u003Ctd>Preview launch date in the source\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>Benchmarks are only part of the story\u003C\u002Fh2>\u003Cp>The most interesting part of the launch is not the size of the model. It is where Alibaba says it performs well: coding, long-horizon tasks, professional work, multimodal understanding, and agentic workflows. Those are the exact categories where users feel model quality in a practical way, because they involve planning, correction, and follow-through instead of one-off text generation.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786149191019-ls21.png\" alt=\"Qwen3.8-Max pushes Alibaba into the top tier\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>On the Arena charts referenced in the source article, Qwen3.8-Max moved into the global top group for text tasks and also posted strong results in coding. The article claims it even beat \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-opus-5\" target=\"_blank\" rel=\"noopener\">Claude Opus 5\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-fable-5\" target=\"_blank\" rel=\"noopener\">Claude Fable 5\u003C\u002Fa> in some coding rankings, which is exactly the kind of result that gets developers to stop scrolling and start testing.\u003C\u002Fp>\u003Cul>\u003Cli>2.4T parameters make it the largest Qwen release so far.\u003C\u002Fli>\u003Cli>Benchmarks point to strong performance in coding and agent tasks.\u003C\u002Fli>\u003Cli>Pricing is aggressive enough to matter for real API usage.\u003C\u002Fli>\u003Cli>The model is already being positioned for office work and multimodal analysis.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>That combination matters because model buyers do not live on \u003Ca href=\"\u002Ftag\u002Fbenchmark\">benchmark\u003C\u002Fa> leaderboards. They care about whether a model can finish a coding task without drifting, summarize a long document without losing the thread, and process a video or spreadsheet without falling apart halfway through. Qwen3.8-Max is clearly aimed at that buyer.\u003C\u002Fp>\u003Ch2>Real-world tests tell a more useful story\u003C\u002Fh2>\u003Cp>The source article includes hands-on tests that are more convincing than a single score. In one coding demo, the model took a large product brief and produced a working AI news website with structure, features, and a cleanup log. In another, it handled a long paper analysis by comparing the ILSVRC and PASCAL VOC datasets across the main text, tables, and appendix material.\u003C\u002Fp>\u003Cp>For the long-document test, the model reportedly answered in about 33 seconds and correctly concluded that PASCAL VOC looks harder by average metrics, while ILSVRC becomes tougher when you focus on comparable classes and difficult subsets. That is the kind of cross-section reasoning many models still struggle with, because the answer is spread across text, tables, and figures.\u003C\u002Fp>\u003Cblockquote>“The most important thing is that it can actually do the work,” said Sam Altman in a 2023 interview with \u003Ca href=\"https:\u002F\u002Fwww.theverge.com\u002F2023\u002F11\u002F6\u002F23949591\u002Fopenai-sam-altman-interview-gpt-4-turbo\" target=\"_blank\" rel=\"noopener\">The Verge\u003C\u002Fa> about AI systems that need to be useful in practice.\u003C\u002Fblockquote>\u003Cp>The article also describes a multimodal test using a nearly 70-minute podcast featuring \u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fsam-altman\u002F\" target=\"_blank\" rel=\"noopener\">Sam Altman\u003C\u002Fa>. Qwen3.8-Max generated a topic timeline and mapped claims back to timestamps, which is the sort of output that saves hours when you need to find one segment inside a long recording.\u003C\u002Fp>\u003Cul>\u003Cli>Coding demo: a full web app generated from a detailed product brief.\u003C\u002Fli>\u003Cli>Document demo: cross-chapter reasoning across a research paper.\u003C\u002Fli>\u003Cli>Podcast demo: topic extraction plus timestamped retrieval from a long recording.\u003C\u002Fli>\u003Cli>Reported turnaround on the paper task: about 33 seconds.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>These tests are useful because they show a model doing work that looks a lot like junior-to-mid-level knowledge work. It is one thing to summarize a paragraph. It is another to inspect requirements, debug issues, verify outputs, and keep track of context across a long run.\u003C\u002Fp>\u003Ch2>Alibaba is pricing for adoption, not prestige\u003C\u002Fh2>\u003Cp>The pricing strategy is the part that may matter most for developers and product teams. The source article says Qwen3.8-Max costs 12 yuan per million input tokens and 36 yuan per million output tokens in China, with cached input at 1.5 yuan. It also says the international input and output prices come in at 40% and 24% of Claude Opus 5, respectively.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786149193132-ro8d.png\" alt=\"Qwen3.8-Max pushes Alibaba into the top tier\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That puts Qwen3.8-Max in a very specific position. It is not trying to win by being the most expensive and most exclusive model on the market. It is trying to be a model that teams can put into production without watching the \u003Ca href=\"\u002Ftag\u002Ftoken\">token\u003C\u002Fa> bill explode. For startups, internal tools, and high-volume workflows, that matters as much as benchmark bragging rights.\u003C\u002Fp>\u003Cul>\u003Cli>Domestic input: 12 yuan per million tokens.\u003C\u002Fli>\u003Cli>Domestic output: 36 yuan per million tokens.\u003C\u002Fli>\u003Cli>Cached input: 1.5 yuan per million tokens.\u003C\u002Fli>\u003Cli>International pricing: 40% of Opus 5 input cost and 24% of Opus 5 output cost, per the source.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>There is also a broader strategic point here. Since 2023, Alibaba says the \u003Ca href=\"\u002Ftag\u002Fqwen\">Qwen\u003C\u002Fa> family has produced more than 400 \u003Ca href=\"\u002Fnews\u002Fseeds-anti-distillation-rule-open-model-policy-en\">open models\u003C\u002Fa>, spawned over 200,000 derivatives, and crossed 1 billion downloads. That scale gives the company a distribution advantage that pure model quality cannot buy on its own.\u003C\u002Fp>\u003Ch2>What this launch means for developers\u003C\u002Fh2>\u003Cp>Qwen3.8-Max matters because it hits a sweet spot that is rare in model releases: strong enough for serious work, cheap enough for repeated use, and flexible enough to cover coding, long documents, office tasks, and multimodal analysis. If the benchmark claims hold up in wider use, it will pressure other vendors to justify premium pricing more carefully.\u003C\u002Fp>\u003Cp>For developers, the immediate takeaway is practical. Test it on the tasks that cost your team time today: code generation, bug fixing, document review, and long-context extraction. If it saves even a few hours per week at the token prices Alibaba is quoting, it becomes easy to justify in a real workflow.\u003C\u002Fp>\u003Cp>The bigger question is whether this release marks a wider shift in how teams buy AI. The best models are no longer winning only by being smarter on paper; they are winning when they are good enough, affordable enough, and dependable enough to sit inside daily work. Qwen3.8-Max is Alibaba’s clearest attempt yet to own that middle ground.\u003C\u002Fp>","Alibaba’s Qwen3.8-Max-Preview arrives with 2.4T parameters, cheaper API pricing, and benchmark results that pressure Claude.","zhuanlan.zhihu.com","https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F2067604315905306959",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1786149191019-ls21.png","model-release","en","1299c9a0-be8c-4b31-a0dc-0c9164cf1099",[17,18,19,20,21],"Qwen3.8-Max","Alibaba","Claude","large language model","AI coding",[23,24,25],"Qwen3.8-Max-Preview has 2.4T parameters and strong benchmark results.","Alibaba is pricing the model aggressively for real-world adoption.","Hands-on demos suggest it can handle coding, long documents, and multimodal tasks.",1,"2026-08-08T00:32:53.612458+00:00","2026-08-08T00:32:53.61+00:00",{"tags":30,"relatedLang":35,"relatedPosts":39},[31,33],{"name":19,"slug":32},"claude",{"name":21,"slug":34},"ai-coding",{"id":15,"slug":36,"title":37,"language":38},"qwen38-max-alibaba-pricing-and-performance-zh","Qwen3.8-Max 把價格壓下來了","zh",[40,46,52,58,64,70],{"id":41,"slug":42,"title":43,"cover_image":44,"image_url":44,"created_at":45,"category":13},"ff8312eb-e6ac-4bbc-85d5-38844a1c1964","qwen38-max-agentic-work-real-frontier-en","Qwen3.8-Max proves that agentic work is the real frontier","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785844979892-cdc1.png","2026-08-04T12:02:32.157065+00:00",{"id":47,"slug":48,"title":49,"cover_image":50,"image_url":50,"created_at":51,"category":13},"ccdf0c22-70d3-474f-9fa1-592694c564b1","google-earth-should-not-ship-ai-image-generation-en","Google Earth Should Not Ship AI Image 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quality","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785607382907-76ym.png","2026-08-01T18:02:40.854976+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":13},"b3fd7185-d626-4e48-ae4d-d38170255e54","openai-cuts-gpt-56-prices-ai-bills-en","OpenAI Cuts GPT-5.6 Prices as AI Bills Climb","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1785542568883-mf9l.png","2026-08-01T00:02:28.341618+00:00",{"id":71,"slug":72,"title":73,"cover_image":74,"image_url":74,"created_at":75,"category":13},"2fee41e9-10d2-4755-9777-081159b6e609","opus-5-premium-ai-becoming-commodity-en","Opus 5 proves premium AI is becoming a 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