[AINews] Qwen 3.8 Max(2.4T) and 27B, new open weights models for Coding and Cowork

After the Qwen Exodus last year and new management took over launching more closed model APIs, there was some real doubt as to whether or not this leading open models lab would continue to release relevant models. That doubt is now gone. Qwen 3.8 Max is a MONSTER 2.4T model that would have been the top open model in the world but for the Kimi K3 release we already covered. Qwen offers them on API for $2 input/$6 output per million tokens, but they have promised to open-weight both models. Key Capabilities & Breakthrough Highlights Autonomous Long-Horizon Coding: 10+ Days Unattended Coding: Built a self-evolving coding harness from scratch over a multi-week autonomous run. Autonomous AI Research: Rebuilt a complete paper’s pipeline (Unified Data Selection for LLM Reasoning) from scratch, then autonomously ran an iterative research loop over 125 hours to invent a new data selection method beating the original paper’s benchmark by +2.71 points. Competitive Data Science: Competed against 526 human teams in the WWW2025 Multimodal Dialogue Intent Recognition Challenge, placing in the top 13% (outperforming 87% of human teams) within 24 hours. Autonomous Hardware & Chip Design: Executed a complete silicon design flow (GCD/RSA cryptographic accelerator) from RTL editing to simulation, synthesis, and physical layout. Reduced gate count from 8,298 to 678 gates while achieving an 81% die area reduction and meeting physical timing closure at 500 MHz. Deep Real-World Work & Operations: Demonstrated production-grade outputs across hundreds of professional workflows (e.g., corporate legal reviews, UI/UX design, structural engineering models, and automated ETF quant research). Outperformed competing models in the E-Commerce Bench (a 365-day store operation simulation), generating a 4.16x return (¥416,252 balance) through continuous game-theoretic negotiation and inventory planning. Multimodal Agents & Visual Feedback: Integrates native visual feedback across planning, coding, and GUI interaction, enabling direct application recreation across platforms (desktop, mobile, web). Released Qwen-MM-Plugins to extend multimodal capabilities to existing agent frameworks. A very nice win for open weights! On today’s pod with Baseten we talked about what it’s like to support these massive model drops on release. AI News for 7/25/2026-7/27/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies! AI Twitter Recap Top Story: Qwen 3.8 Max open model launch What happened Alibaba Qwen announced Qwen3.8-Max as its new flagship and said open weights are coming next week. Alibaba introduced Qwen3.8-Max as its “most capable model to date,” describing it as a 2.4T-parameter model focused on coding, long-horizon agentic work, and multimodal reasoning, with the explicit claim that open weights will be released next week, alongside Qwen3.8-27B also going open-weight @Alibaba_Qwen The launch tweet also included API pricing: $2.00 / M input tokens, $6.00 / M output tokens, and $0.25 / M cached tokens @Alibaba_Qwen Alibaba framed the model around several headline capabilities: 10+ days of autonomous coding, 500+ turns of chip design optimization, 365 days of e-commerce strategy, and native multimodal intelligence where vision is part of the execution loop rather than just an input channel @Alibaba_Qwen The company simultaneously pushed availability across its own surfaces and partners: Qwen Studio, API, Command Code, and later Venice; infra and app builders quickly confirmed support plans or integrations including Baseten, Hermes Agent, and Command Code @Alibaba_Qwen @Alibaba_Qwen @baseten @Teknium The announcement landed as part of a broader pattern: multiple observers described it as evidence that the Chinese open-weight frontier is now competing directly with top Western closed models, especially in coding, agentic workflows, and multimodal tasks @kimmonismus @matvelloso Official claims and reported specs Vendor-reported model details and performance claims were unusually aggressive for an open-weight release. Alibaba’s own framing: 2.4T total parameters @Alibaba_Qwen Long-horizon agentic/cowork focus @Alibaba_Qwen Autonomous coding over 10+ days with a public GitHub trace @Alibaba_Qwen 500+ turns for chip design optimization @Alibaba_Qwen 365 days of e-commerce strategy execution @Alibaba_Qwen Native multimodal planning loop rather than vision-only input @Alibaba_Qwen Third-party summary tweet from ZhihuFrontier added more claimed or reported technical details: 95B active parameters per token, implying an MoE activation ratio of roughly 4% 1M-token context window API exposes low / medium / xhigh reasoning-effort modes Compatibility with OpenAI and Anthropic protocols Benchmark claims: PaperBench 93.0, CoWorkBench 74.8, WideSearch 81.9 @ZhihuFrontier Vals AI independently posted concrete eval/runtime settings: 1M token context 128k max output tokens Tested at temperature 0.7 with default top-p / top-k @ValsAI These numbers matter because they place Qwen3.8-Max in the same deployment class as other giant sparse open models like Kimi K3 and GLM-5.2, not the more practical 30B–70B local tier. Independent evaluations and leaderboard placements The model immediately posted strong third-party results, especially in coding-adjacent, vision, and design-heavy arenas. Frontend Code Arena: Qwen3.8-Max debuted at #4 overall with 1,668 Elo, trailing only Claude Opus 5 [Max] at 1,705 and Kimi K3 [Max] at 1,676, and roughly tied with Claude Opus 5 [High] at 1,669 @arena In Frontend Code Arena subslices, it ranked: #2 Consumer Product #3 Brand & Marketing, Reference-based design, Gaming, Content Creation Tools #4 Data & Analytics #5 Simulations @arena Vision Arena: Qwen3.8-Max ranked #2 with 1,305, only 13 points behind Claude Fable 5 [High] @arena Vals Index: Qwen3.8-Max ranked #2 among open-weight models, #10 overall out of 43, with a score of 66.1 @ValsAI Vals also reported: It matched Claude Opus 4.7 on the Index, 66.1 vs 66.1 At about 2.3x lower cost per test: $2.68 vs $6.17 @ValsAI Vals’ benchmark-specific numbers: SWE-bench: 87.3%, ahead of GPT-5.5 (82.6%) and GLM-5.2 (83.3%), but behind Claude Opus 4.8 (89.2%) Terminal-Bench 2.1: 67.4, up from 61.0 for Qwen 3.7 Max @ValsAI Vals also highlighted the pace of progress: Qwen 3.7 Max = 57.5 Qwen 3.8 Max = 66.1 Gain of 8.6 points in ~2.5 months Price cut from $2.50/$7.50 to $2.00/$6.00 input/output @ValsAI There were also more anecdotal but technically relevant claims: One user visualized benchmark deltas and argued “Opus 4.8 is mostly subsumed by 3.8-Max” on the chart they reconstructed @deliprao Another claimed Qwen 3.8 surpassed Fable 5 on Terminal Bench and said Anthropic was now under visible pressure @kimmonismus A separate tweet called Qwen 3.8 Max the “best object detection VLM” across satellite, infrared, documents, technical drawings, sketches, crowded scenes, and small objects, though this was based on examples rather than a cited benchmark paper @skalskip92 Facts vs. opinions Facts / directly attributable claims Alibaba announced Qwen3.8-Max and said open weights arrive next week; Qwen3.8-27B will also go open-weight @Alibaba_Qwen Alibaba disclosed API pricing of $2 input / $6 output / $0.25 cached per million tokens @Alibaba_Qwen Arena reported #4 in Frontend Code Arena at 1,668 and #2 in Vision Arena at 1,305 @arena @arena Vals reported 66.1 on Vals Index, #2 among open-weight models, 87.3% SWE-bench, 67.4 Terminal-Bench 2.1, 1M context, 128k output, and lower cost-per-test than Opus 4.7 @ValsAI @ValsAI @ValsAI ZhihuFrontier stated 95B active parameters and protocol compatibility; this appears to be a secondary summary rather than an original Alibaba spec sheet @ZhihuFrontier Opinions / extrapolations / rhetoric “China is no longer lagging behind but competing on equal footing” @kimmonismus “Open models are winning now” @JonathanRoss321 “Looks like Opus 4.8 is mostly subsumed” @deliprao “Anthropic is under pressure” and “mood shifted drastically” are ecosystem readings, not measurements @kimmonismus “Best object detection VLM” is an informed product judgment, but not one tied in-thread to a standard benchmark table @skalskip92 Claims that Qwen3.8-Max plus open agents prove open models have “caught up” are user-level interpretations rather than consensus eval conclusions @omarsar0 The central factual story is strong even after stripping out the hype: a very large sparse model, open-weight promise, lower pricing than prior Qwen Max, and high placements on multiple third-party leaderboards. The infrastructure reality: “open-weight” does not mean easy to run A major counterpoint in the discussion was that frontier open models are operationally open, but not broadly accessible in the local-inference sense. Jamin Ball argued that pricing comparisons were overstated because “vanilla” token prices ignore token efficiency and because these models are enormous: Qwen 3.8 Max >2T params Kimi K3 ~104B active per token GLM 5.2 = 744B total, 40B active For K3, loading weights alone is >1TB memory Requires at least 8 H100/B200 GPUs to run Moonshot recommends 64+ accelerators in supernode-style setups @jaminball This same critique implicitly applies to Qwen3.8-Max, even if its active-parameter count is somewhat lower than K3’s: a 2.4T-class MoE is not a commodity local model @jaminball StableQuan made the practical version of the same point more bluntly: long, RAM-heavy prompts and slow tool calls make giant models painful on consumer hardware, recommending API use instead @stablequan At the same time, the excitement around Qwen3.8-27B shows where many developers think the real adoption wave may come from: a smaller open-weight descendant in the same family, possibly inheriting some of the flagship’s post-training or distilled capabilities @kimmonismus @TheZachMueller This is the key split in the open-model story: ecosystem influence and benchmark legitimacy come from releasing the 2.4T flagship; practical deployment at scale may come from the 27B release. Licensing controversy and geographic restrictions The most concrete skeptical reaction was not about performance, but about the license. OstrisAI flagged what they read as a license prohibition covering the USA, EU, UK, and Korea, saying the terms appeared to forbid even downloading the model from the US @ostrisai That concern echoed a broader discussion happening simultaneously around another open-weight release, MiniMax H3, where users argued that geographic restrictions undercut claims of openness @kimmonismus No clarifying Qwen license tweet appears in this dataset from Alibaba itself, so the restrictive-license reading remained unresolved within these tweets For engineers, this matters more than the marketing label. “Open weights” can still mean: no OSI-style open-source rights, use-case restrictions, export/jurisdiction limits, or no legal permission for commercial deployment in key regions. That licensing ambiguity is one of the main reasons some of the reaction was more cautious than celebratory. Why the launch matters strategically This was widely read as a strategic shift by Alibaba, not just a routine product update. ZhihuFrontier explicitly framed the move as Alibaba choosing ecosystem influence over exclusivity, arguing that earlier Max models stayed closed while the open line had previously topped out around Qwen3-235B @ZhihuFrontier In that reading, DeepSeek, Kimi, and other Chinese open models weakened the premium of keeping top-tier systems API-only, pushing Alibaba to compete on ecosystem adoption as well as model quality @ZhihuFrontier Multiple observers connected Qwen3.8-Max to a broader Chinese-model s [truncated for AI cost control]