Qwen 3.8 Max 发布：2.4T 参数，开放权重下周上线

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.

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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