Qwen3.8 Max

Aivancity's technical write-up confirms that Alibaba Cloud officially unveiled Qwen3.8-Max on August 3, 2026, describing it as the largest and most capable Qwen model to date, with 2,400 billion total parameters (95 billion active), a Mixture-of-Experts architecture, and a reported context window of one million tokens. For practitioners, the practical signal is that long-running, agent-style coding tasks and large-context workflows are the use cases Alibaba is targeting, with clear improvement on coding and certain professional benchmarks but no claim of overall superiority. The open-weight distribution under Qwen3.8-2.4T-A95B makes

Alibaba's Qwen3.8-Max debuted as a sparse Mixture-of-Experts model with 2.4 trillion total parameters and 95 billion active parameters, first going live through Alibaba's QwenCloud API on August 2, 2026. The open-weight variant, packaged as Qwen3.8-2.4T-A95B, was published on Hugging Face and ModelScope on August 13, w For developers evaluating coding agents and large-context workloads, the technical takeaway is the combination of a 1M-token context window, MoE efficiency that keeps active parameters manageable, and Apache 2.0 weights that make self-hosting feasible. The release lands amid a crowded September 2026 frontier cycle in w

On September 2, 2026, Alibaba released Qwen3.8-Max-0902, a post-training upgrade to Qwen3.8-Max that keeps the same 2.4 trillion parameter architecture and 1 million token context while focusing on coding and long-horizon agent performance. The new variant ranks first overall on Code Arena WebDev with 1,691 points, edg The Qwen3.8-Max-0902 update leads Claude Opus 5 on three coding benchmarks (MLS-Bench-Lite, SWE-Atlas QnA, QwenSWEBench V2) and wins on WorkArena and both published multimodal evaluations, though Claude retains leads on TerminalBench 3.0, DeepSWE 1.1, and several agent coordination benchmarks. Additional strong gains a

A third-party benchmark breakdown of Qwen3.8-Max published August 14, 2026 details the model's published scores: PaperBench 93.0, IFBench 82.8, Terminal Bench 2.1 at 86.6, MathVision 95.2, and OSWorld-Verified 86.1, alongside reported losses at HLE 43.6 (last among the four flagships compared) and SWE-bench Pro 67.7 (r The same piece provides context on reported pricing of $2 input and $6 output per million tokens (roughly a third of Claude Opus 5 and a quarter of GPT-5.6 Sol) and comparative framing against Kimi K3, Fable 5, and GPT-5.6 Sol, recommending the benchmark table be treated as a strong vendor claim rather than an independ

Roboflow's evaluation of Qwen3.8-Max for vision tasks confirms the model accepts text, images, and video as input and returns text, available through the Alibaba Cloud API and testable in Roboflow Playground. The model delivered the strongest object detection results in Roboflow's upcoming VLM benchmark, performing wel The vision workflow requires prompting the model to return a JSON list containing a label and four coordinates per detected object, which Roboflow then parses and converts into bounding boxes, with Qwen models working best on XYXY coordinates normalized to a range of 0 to 1000. Open weights were scheduled for release o

Alibaba officially announced Qwen3.8-Max on August 3, 2026, describing it as the most powerful model in its Qwen series to date. The model is a multimodal foundation built on a Qwen 3.5 base with a Sparse Mixture-of-Experts architecture and hybrid attention mechanism, totaling 2.4 trillion parameters with 95 billion ac The creator-attributed announcement confirms Qwen3.8-Max natively supports visual intelligence and is positioned as Alibaba's flagship, with availability also through QwenWork, Alibaba's all-in-one workplace AI agent platform. Ranked fourth on Fronted Code Arena, the model is designed for autonomous coding and long-hor

Qwen officially released Qwen3.8-Max, a 2.4-trillion-parameter Mixture-of-Experts model with 95 billion active parameters built on the architectural foundation of Qwen 3.5, marking the first time weights of a Qwen-Max-class model will be open-sourced (releasing the week following the announcement). The post, republishe The announcement positions Qwen3.8-Max against Opus 4.8, Fable 5, GPT-5.6 Sol, and Gemini 3.1 Pro with full benchmark tables, covering coding, RL infrastructure gains, and multimodal agent capabilities. The release emphasizes end-to-end, dependable delivery of complex tasks rather than single-turn response quality, and