Qwen 3.8 Max 2.4T open weights ship as text-only, users say

• Qwen3.8-2.4T-A95B open weights landed as a text-only Max-class checkpoint, not feature-equivalent to the hosted model: entsnack's LocalLLaMA post says vision was absent, and the Qwen Hugging Face card says Qwen3.8-Max adds vision input, non-thinking support, 1M context by default, and built-in tools.
• The model is huge by open-weight standards: NVIDIA's GB300 serving guide lists 2.4T total parameters, 95B active per token, hybrid full and linear attention, up to 1M context, and 128K output, while segmond's LocalLLaMA thread turned that into a local-running problem.
• The hosted Qwen3.8-Max showed a real agentic jump: Vals AI's benchmark thread says Legal Research Bench all-pass rose from 25.5% to 47.6%, and Vals AI's follow-up ties the gain to longer runs, more primary-law calls, and 128K max output.
• Qwen3.8-27B stayed in rollout limbo: Alibaba_Qwen's reply said open weights were landing this week, while a LocalLLaMA post captured a ModelScope page returning 404 before release.

The model card buries the useful split: Max gets the creature comforts, the checkpoint gets the weights. The official Hugging Face card lists vision input, non-thinking support, 1M context by default, and built-in tools as Max-side additions; Modal's day-zero endpoint post says it used SGLang plus a custom DFlash speculator; NVIDIA's serving writeup says inference wants data-center-scale accelerated compute. Vals AI found Qwen3.8-Max did better legal research partly by refusing to quit: more turns, more tool calls, more sources, and far longer wall-clock time in its benchmark thread.

LocalLLaMA's first complaint was literal: the Qwen3.8 Max 2.4T open-weight release had no vision, while the API kept vision. Hannibalj2ca's thread framed the same pattern more broadly: strong Chinese open weights, but flagship open releases still arriving as text-first packages.

The Qwen Hugging Face card backs up the split in one sentence: Qwen3.8-Max is the official version based on Qwen3.8-2.4T-A95B, with extra features such as vision input, non-thinking support, 1M context by default, and official built-in tools.

The license complaint was separate. The Hugging Face license file is titled Qwen3.8-Max License, although it grants broad rights to use, modify, distribute, host, fine-tune, and create derivative works.

Vision was already part of the Max-side pitch. skalskip92 linked a Qwen3.8-Max blogpost around object detection, object counting, image reasoning, data extraction, cost, and speed.

The attached object-detection example reports 85.6% mAP@50 on a solar-panel hotspot image. That made the open-weight omission sharper than a missing feature flag.

NVIDIA's GB300 guide gives the serving-shape facts: 2.4T total parameters, 95B active per token, fine-grained MoE, hybrid full and linear attention, up to 1M context, and 128K output. It also says inference at this scale depends on data-center-scale accelerated compute.

Hosted surfaces expose smaller practical envelopes. OpenRouter's model page lists Qwen3.8 2.4T A95B at 262K context and $2/$6 per million input/output tokens, while Modal's launch post says its endpoint is backed by SGLang and a custom DFlash speculator tuned to Qwen3.8's shape.

Vals AI's read was persistence, not pure intelligence. From its benchmark thread:

• Legal Research Bench rank: #22 to #4.
• All-pass: 25.5% to 47.6%, +22.1 points.
• Per-requirement accuracy: 78.4% to 86.1%, +7.7 points.
• Turns per task: 19.7 to 35.5.
• Tool calls: 38 to 60.
• Sources: 7.1 to 12.2.
• Time per task: 809s to 3,678s.
• Cost per task: $2.49, versus $6.76 for Opus 5, $9.79 for Fable 5, and $21.61 for GPT-5.6 Sol.

The mechanism shifted toward deeper legal retrieval. Vals AI said CourtListener calls rose from 6.8 to 22.9 per session, generic web search dropped, reasoning increased 6x, and final answers became 1.7x longer.

Alibaba_Qwen amplified the result in its reply, thanking Vals AI for highlighting the move from #22 to #4.

Qwen3.8-27B was a separate promise from the 2.4T A95B drop. Alibaba_Qwen said the 27B open weights were landing this week, and another Alibaba_Qwen reply said the model would improve on Qwen3.6-27B for agentic coding.

The rollout still looked messy at the edge. EveningIncrease7579 said a ModelScope link for Qwen3.8-27B returned 404 after a release-date page had circulated.

Community questions had already dropped to serving internals. mailto_devnull's thread asked whether the 27B model would ship with a DFlash or MTP head, noting Qwen3.6-27B with MTP ran around 8 tok/s on a 32GB unified-memory machine.