Qwen3.8-27B weights are Apache-2.0 and 56 GB — this is the 3.8 you can actually run

Alibaba uploaded Qwen3.8-27B on August 13, 2026. The official repo is Qwen/Qwen3.8-27B. It is a dense vision-language model under Apache-2.0. The BF16 checkpoint is 55.56 GB. Unsloth's Q4_K_M GGUF is 17.1 GB. Native context is 262,144 tokens. Thinking can be turned off — unlike the 2.4T Max weights.

Alibaba uploaded the Qwen3.8-27B weights on August 13, 2026. The official repo is Qwen/Qwen3.8-27B. Three days ago we wrote that this checkpoint was still missing. It is not missing anymore. (Source: Qwen model card; the Upload folder commit is 2026-08-13T08:23Z.)

Key facts:

The official name is Qwen3.8-27B. Hugging Face lists the safetensors build as 28B params, BF16.
The license is Apache-2.0. The LICENSE file is copyright 2026 Alibaba Cloud. That is not the qwen3.8-max license on the 2.4T drop.
It is a native vision-language model. Pipeline tag is image-text-to-text. Inputs include images and video.
Native context is 262,144 tokens, extensible to 1,000,000. The hosted Qwen Cloud 27B, which Qwen says is coming soon, is the build that defaults to 1M plus official tools.
The official BF16 checkpoint is 55.56 GB across 18 shards. model.safetensors.index.json reports total_size 55,562,855,904 bytes.
Thinking is on by default and can be turned off. reasoning_effort is xhigh / medium / low. preserve_thinking is on by default.

Official Qwen3.8-27B text benchmark table: Terminal Bench 2.1 73.0, SWE-bench Pro 61.7, DeepSWE 1.1 42.2, QwenSWEBench 79.0, CoWorkBench 70.7, compared with Qwen3.6-27B, Qwen3.7-Plus, Muse Glimmer-30B and Opus 4.6 Max. — Vendor table on the official card. SWE-bench Pro 61.7 beats Opus 4.6 Max 53.4. Terminal Bench 2.1 does not: 73.0 versus 78.2. (Table: Qwen/Qwen3.8-27B, 2026-08-14)

What this means if you’re building with Qwen3.8

1. This is the local 3.8. The 2.4T drop is still not a workstation model. The Qwen3.8-2.4T-A95B weights are 4,893 GB in BF16 and 397 GB at the smallest community 1-bit GGUF. Qwen3.8-27B is 55.56 GB in BF16. Unsloth’s Q4_K_M is 17.107 GB. Q8_0 is 29.047 GB. The smallest listed Unsloth quant, UD-IQ2_XXS, is 9.010 GB. Vision needs a separate mmproj (~0.93 GB). A single 24 GB card is in range for Q4. A 397 GB 1-bit Max is not.

2. The license is the other split. The 27B LICENSE is Apache-2.0. The 2.4T card uses qwen3.8-max, which adds UI-credit and MaaS revenue clauses. If you self-host or fine-tune, read the file on the repo you actually pulled.

3. Treat the headline benches as vendor numbers. Qwen evaluated SWE-bench Pro, DeepSWE 1.1, NL2Repo-Bench and the in-house QwenSWEBench with the Claude Code harness (temp 1.0, top_p 0.95, 256K). It also says it corrected “problematic tasks” and re-ran baselines. CoWorkBench and QwenSWEBench are in-house. The useful contrast is against its own 3.6-27B: DeepSWE 13.3 → 42.2, OSWorld-Verified 63.9 → 84.3. Do not read 61.7 SWE-Pro as an independent win over Opus.

4. Default thinking will eat your VRAM budget. Official sampling: thinking mode temperature=1.0, top_p=0.95, top_k=20; instruct / non-thinking temperature=0.7, top_p=0.80, presence_penalty=1.5. Disable thinking with chat_template_kwargs.enable_thinking=False. Qwen’s own note: lower reasoning_effort can produce faster turns and more retries, so it does not always reduce total tokens. The 2.4T open weights cannot turn thinking off. This one can.

5. Serve it as a model-id swap, then measure. Official recipes exist for vLLM, SGLang and TokenSpeed. The card’s example model id is Qwen/Qwen3.8-27B. Our Qwen3.6 local coding guide is still the local-loop writeup; swap the weights, keep the harness, run your own eval. Hosted Qwen3.8-Max remains the API path in the Max numbers guide.

Hugging Face file listing for unsloth/Qwen3.8-27B-GGUF showing Q4_K_M at 17.1 GB, Q4_0 at 16.1 GB, and Q6_K at 22.9 GB. — Community GGUF sizes on 2026-08-16. Q4_K_M is 17.1 GB. That is the number that makes this a local model. (Listing: unsloth/Qwen3.8-27B-GGUF)

Sources: Qwen3.8-27B model card · Apache-2.0 LICENSE · unsloth GGUF builds · Qwen3.8 blog · r/LocalLLaMA release megathread