--- title: Qwen3.8-Max: what we know, what's unverified, and what... description: Alibaba has launched Qwen3.8-Max, a 2.4-trillion-parameter sparse mixture-of-experts model activating ~95B parameters per inference, with a 1-million-token... canonical: https://daily.dev/posts/qwen3-8-max-what-we-know-what-s-unverified-and-what-developers-are-actually-saying-tce0j5xea twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Qwen3.8-Max: what we know, what's unverified, and what developers are actually saying | daily.dev og:description: Alibaba has launched Qwen3.8-Max, a 2.4-trillion-parameter sparse mixture-of-experts model activating ~95B parameters per inference, with a 1-million-token... og:url: https://daily.dev/posts/qwen3-8-max-what-we-know-what-s-unverified-and-what-developers-are-actually-saying-tce0j5xea og:image: https://api.daily.dev/og/posts/tCe0J5XEa.png og:image:alt: Qwen3.8-Max: what we know, what's unverified, and what developers are actually saying og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Qwen3.8-Max: what we know, what's unverified, and what developers are actually saying **[Collections](https://daily.dev/sources/collections)** · 4 min read · 3 upvotes · 0 comments ## Summary Alibaba has launched Qwen3.8-Max, a 2.4-trillion-parameter sparse mixture-of-experts model activating ~95B parameters per inference, with a 1-million-token context window and multimodal support. Self-published benchmarks place it near Claude Opus 4.8 and GPT-5 on coding and agentic tasks, but independent verification is pending. A headline demo showed the model autonomously building a CLI tool over 16 days with a public GitHub history, though developers note the results depend heavily on controlled scaffolding rather than raw capability. Open weights for the full model and a 27B variant are promised on Hugging Face but haven't shipped yet, drawing criticism that it's currently an API product with an open-source label. Enterprise considerations around data sovereignty, indemnification, and the practical limits of long context windows in production are also flagged. ## Content Alibaba launched Qwen3.8-Max this week, and the AI community has had a lot to say about it — some impressed, some skeptical, most somewhere in between. ## What it is Qwen3.8-Max is a sparse mixture-of-experts model with 2.4 trillion total parameters, though only about 95 billion activate per token. That distinction matters: you get the stored knowledge of a 2.4T model while paying for roughly 95B worth of compute per inference. It supports text, images, and video, with a 1-million-token context window and a maximum output length of 131,072 tokens. The internal "thinking budget" before the model commits to an answer stretches to 262,000 tokens. Pricing on QwenCloud is $2 per million input tokens and $6 per million output tokens, with cached reads dropping to $0.17 per million — about 8x cheaper if you're reusing a stable prompt prefix. That's significantly cheaper than comparable closed-lab models, though at least one developer noted Kimi K3 runs agentic tasks at $0.86 per run versus Qwen3.8-Max's $1.14, because the Alibaba model takes more turns and pushes output token usage up roughly 45%. Open weights for both the full 2.4T model and a smaller 27B variant are expected on Hugging Face and ModelScope next week. The model is already available via QwenCloud, OpenRouter, and Venice. ## Benchmarks On Terminal Bench 2.1 (which tests whether a model can actually drive a real command line through a task end to end), Qwen3.8-Max scored 86.6, compared to 84.6 for Claude Opus 4.8 and 88.8 for GPT-5.6 Sol. It posted 93.0 on PaperBench and 92.6 on GPQA Diamond. On Arena's Image-to-WebDev leaderboard, it entered at #2, behind Claude Opus 5 but ahead of Fable 5. On the open-weights SWE-bench leaderboard, Kimi K3 still leads at 57 points, with Qwen3.8-Max one point behind. The honest summary: it's competitive with top-tier models on several benchmarks, but not clearly ahead of any of them. Most analysts treating Kimi K3 and Qwen3.8-Max as roughly tied on capability, with cost and deployment context being the real differentiators. ## The 16-day coding demo Alibaba's headline demo had the model autonomously build a CLI tool called "oh-my-cli" over 16 days, producing 265 commits, 127 pull requests, and 151 GitHub issues — all publicly auditable on GitHub. Other demos include reproducing a research paper's results in five days (about 7,600 lines of code, 33 training rounds, all 6 findings reproduced) and optimizing a cryptographic circuit from 8,298 logic gates down to 678 over roughly 500 iterations, cutting chip area by 81% while still meeting timing at 500 MHz. Those are genuinely interesting results. But developers have pointed out something worth sitting with: Alibaba didn't just hand the model a task and walk away. It built a tightly controlled environment — specific tools, constrained interfaces, structured feedback loops — and the model operated inside that cage. The insight isn't that the model is autonomous; it's that the scaffolding did a lot of work. Capacity is not the same as judgment. ## What developers actually think Reactions have been mixed in ways that feel pretty honest. On the open-weights promise: the weights haven't shipped yet, and until they do, this is an API product. One developer called it "an API business model wearing an open source jacket." That's a bit harsh, but the point stands — promised weights and available weights are different things. On the benchmarks: Alibaba published same-day results from its own evaluations. Several developers flagged this as "a lab grading its own homework." Independent verification is still pending. On the million-token context window: useful for prototyping, but developers with production experience caution that long-context performance degrades quietly. Stuffing a million tokens into a prompt is not a production architecture. On pricing: some developers find it competitive; others are sticking with GPT-5.6 Sol plus DeepSeek Flash as a cheaper combination. The 27B variant, when weights drop, may be more practically deployable for most organizations than the full 2.4T model, which is not realistically self-hostable for most teams. On the broader picture: open-model benchmark scores have gone from around 2% on Humanity's Last Exam a year ago to over 50% now. Whatever the caveats around Qwen3.8-Max specifically, the trajectory of open-weight models is real. ## The geopolitical read Alibaba released this model despite ongoing US export controls on advanced chips. Whether Qwen3.8-Max represents a genuine frontier capability or a well-optimized near-frontier model is still being debated, but the gap with Western labs has clearly narrowed. That's the part of this story that will matter longest. ## Questions this post answers ### What are the pricing and specs for Alibaba's Qwen3.8-Max API? Qwen3.8-Max is priced at $2 per million input tokens and $6 per million output tokens on QwenCloud. It is a sparse mixture-of-experts model with 2.4 trillion total parameters, activating roughly 95 billion per token at inference. It supports text, images, and video with a 1-million-token context window. A smaller 27B variant is also planned. _Teams comparing frontier model pricing track moves like this on daily.dev before committing to an API contract._ ### How does Qwen3.8-Max compare to Kimi K3 for agentic tasks in terms of cost and capability? On capability, Qwen3.8-Max and Kimi K3 are roughly tied on open leaderboards, with Kimi K3 holding a one-point lead at 57 points. Cost is the real differentiator: Kimi K3 runs at $0.86 per agentic task versus $1.14 for Qwen3.8-Max, partly because Qwen3.8-Max takes more agentic turns and pushes output token usage up about 45%. _Developers choosing between frontier open-weight models for agentic pipelines follow cost-per-task comparisons like this on daily.dev._ ### What are the practical limitations of using a 1-million-token context window in production? Large context windows are useful for prototyping but tend to degrade quietly in production: retrieval quality drops, costs spike, and failures become hard to debug. Experts recommend treating a 1M-token context window as a convenience feature rather than a production architecture decision. _Engineers designing production RAG or long-context pipelines find hard-won lessons like this on daily.dev._ --- Tags: [#open-source](https://daily.dev/tags/open-source), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#mixture-of-experts](https://daily.dev/tags/mixture-of-experts), [#qwen](https://daily.dev/tags/qwen) [View this post on daily.dev](https://daily.dev/posts/qwen3-8-max-what-we-know-what-s-unverified-and-what-developers-are-actually-saying-tce0j5xea) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Qwen3.8-Max: what we know, what's unverified, and what developers are actually saying","url":"https://daily.dev/posts/qwen3-8-max-what-we-know-what-s-unverified-and-what-developers-are-actually-saying-tce0j5xea","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/qwen3-8-max-what-we-know-what-s-unverified-and-what-developers-are-actually-saying-tce0j5xea"},"datePublished":"2026-08-04T14:50:42.852Z","dateModified":"2026-08-08T17:13:10.682Z","description":"Alibaba has launched Qwen3.8-Max, a 2.4-trillion-parameter sparse mixture-of-experts model activating ~95B parameters per inference, with a 1-million-token...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/706e61521e6cc1d77e4395be7e98cc26?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/706e61521e6cc1d77e4395be7e98cc26?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Collections","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Collections","logo":"https://media.daily.dev/image/upload/s--fk_6ycEi--/f_auto,q_auto/v1780996001/logos/collections?_a=BAMAMiWQ0","url":"https://daily.dev/sources/collections"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/qwen3-8-max-what-we-know-what-s-unverified-and-what-developers-are-actually-saying-tce0j5xea","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":3},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"open-source,llm,ai-agents,mixture-of-experts,qwen","timeRequired":"PT4M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Collections","item":"https://daily.dev/sources/collections"},{"@type":"ListItem","position":3,"name":"Qwen3.8-Max: what we know, what's unverified, and what developers are actually saying"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/qwen3-8-max-what-we-know-what-s-unverified-and-what-developers-are-actually-saying-tce0j5xea#faq","mainEntity":[{"@type":"Question","name":"What are the pricing and specs for Alibaba's Qwen3.8-Max API?","acceptedAnswer":{"@type":"Answer","text":"Qwen3.8-Max is priced at $2 per million input tokens and $6 per million output tokens on QwenCloud. 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