Qwen3.8 model releases

An updated snapshot of Alibaba's flagship Qwen3.8-Max, released Sep 2 2026 (model id qwen3.8-max-0902; alias qwen3.8-max-2026-09-02). A post-training upgrade on the unchanged 2.4-trillion-parameter Mixture-of-Experts base (~95B active per token), it sharpens coding for engineering-scale projects and long-horizon autonomous development, multi-tool agent orchestration, and native vision (chart reasoning, document parsing, multimodal perception). Accepts text, image, and video input and returns text, with a 1M-token context, up to ~131K output tokens, and an optional thinking mode carrying up to 256K chain-of-thought tokens. Pricing is unchanged from Qwen3.8-Max at $2.00 / $6.00 per 1M input/output tokens. Vendor-reported gains include a +22-point CodeArena jump to 1,691 (first on that leaderboard at launch); figures are unverified. Served API-first (Alibaba, OpenRouter); the -Max flagship tier is closed-weight, so treat this snapshot as proprietary despite the open-weight base family. Distinct from the base Qwen3.8-Max (2026-08-03).

The productionized, managed Qwen Cloud API model announced Aug 26 2026 alongside the open-weight Qwen3.8-Flash-Next, with official QwenCloud pricing confirmed Aug 27-28. It runs the same Qwen4-preview architecture as Flash-Next — a 125B-total sparse Mixture-of-Experts that activates ~6B parameters per token (~180B stored once a 51B n-gram embedding table and a multi-token-prediction module are counted; roughly 95% sparsity), built on Gated-DeltaNet + Qwen Sparse Attention with a gated residual stream and Muon-trained large linear layers — but ships as the hosted service rather than the self-host weights. On QwenCloud it defaults to a 1M-token context with built-in tools and accepts text/image/video in, returning text out. List pricing is $0.15 input / $0.47 output / $0.016 cache-hit per Mtok (domestic China Y0.8/Y2.7/Y0.1), roughly a third of DeepSeek-V4-Flash and about one-thirteenth of the Qwen3.8-Max flagship. Also reachable through OpenCode Go's flat-rate subscription. Distinct catalog row from the open-weight Qwen3.8-Flash-Next (self-host, Qwen Community License 1.0); this managed API is recorded proprietary/API-only. Self-reported benchmarks carry over from Flash-Next (SWE-bench Pro 62.5, DeepSWE 58.7) — all vendor numbers, unverified by independent labs at launch. Thinking on by default.

An open-weight, experimental preview of the architecture that will underpin Qwen4, released Aug 26 2026 (Qwen/Qwen3.8-Flash-Next). A sparse MoE with ~6B active parameters (headline 125B-with-6B-activated; ~180B stored once a 51B n-gram embedding table and 4B multi-token-prediction module are counted), 512 experts (10 routed + 1 shared), and a hybrid Gated-DeltaNet + Qwen Sparse Attention design. Native 262,144-token context, extensible to 1M via YaRN. Accepts text, image, and video in and returns text out. Distinct from the managed Qwen Cloud 'Qwen3.8-Flash' API (which defaults to 1M context and bundled tools); this Next build is catalog/self-host only with no hosted list price at launch, served via Transformers, vLLM, SGLang, and TokenSpeed. Weights under the Qwen Community License 1.0. Self-reported vs DeepSeek-V4-Flash-0731: DeepSWE 58.7 vs 54.4, SWE-bench Pro 62.5 vs 56.0, LiveCodeBench v6 91.9, GPQA Diamond 91.7, though NL2Repo 48.1 vs 54.2 is a regression; vision self-reports include AndroidWorld 84.5 and RealWorldQA 88.5 — all vendor numbers, unverified at launch. Thinking on by default.

The open-weight, single-GPU sibling of Qwen3.8-Max, published by Alibaba on Hugging Face on Aug 14 2026 under Apache 2.0 — the smaller open release Alibaba had promised alongside the closed Qwen3.8-Max flagship. A 27B dense model (~28B counting the ~1B vision encoder) with 64 layers, hidden size 5,120, and a 248,320-token vocabulary. Uses a hybrid attention stack — 48 Gated DeltaNet linear-attention layers to 16 full Gated Attention layers (a 3:1 split) — for a native 262,144-token context, extendable to 1M via YaRN. Natively multimodal (text, image, and video input; text output) and ships with Multi-Token Prediction for speculative decoding. Quantized (Unsloth dynamic GGUFs) it runs in ~16-17GB of VRAM, fitting a single consumer GPU such as a 3090 or 4090 — positioned as one of the most capable local models of 2026.

Alibaba's largest model to date and the flagship of the Qwen3.8 line — a 2.4-trillion-parameter sparse Mixture-of-Experts (~95B active per query) that Alibaba positions just behind Anthropic's Fable 5 on overall performance. Previewed 2026-07-19 at the World AI Conference in Shanghai, it went generally available on 2026-08-03 with a published benchmark table, standard API access, and firm per-token pricing ($2 / $6 per 1M input/output tokens). Fully multimodal (text, image, video input) over a 1M-token context. Alibaba also committed to shipping open weights for both Qwen3.8-Max and a smaller Qwen3.8-27B checkpoint.