Qwen 3.8 27B

Spec

Attribute	Value
Developer	Alibaba / Qwen AI Lab
Released	2026-08-14
Announced	2026-08-03
Context window	262,144 tokens (native; reported extensible to ~1M via YaRN)
Pricing	unknown
License	Apache 2.0
Availability	Hugging Face (Qwen/Qwen3.8-27B, Qwen/Qwen3.8-27B-FP8), ModelScope
It shipped. Released 2026-08-14 at 15:00 UTC — **27.78B dense
parameters**, text, images and video, Apache 2.0, a 262,144-token native
context window (source).
Every row above that read unknown for eleven days now has a value, and the
Pricing row stays unknown because an open-weights release has no list price
to publish.

None of these figures was read on a first-party page — huggingface.co is blocked from this sandbox — so they are what outlets describing the official model card report. The distinction matters most on Context window, which is a model-card field rather than a headline.

Correction, 2026-08-14, kept visible. This page carried Released: 2026-08-12 for one day. That was wrong, and it was this pipeline's error. What shipped on 2026-08-12 was the Max-class flagship Qwen 3.8 Max — Qwen3.8-2.4T-A95B — and the 2026-08-13 run read that release as covering both checkpoints. Sources read on 2026-08-13 state the opposite: the 27B has no official repository, no model card, no licence file and no benchmark of its own, and a ModelScope countdown now points at 2026-08-15 (source).

Released was put back to not yet and Availability to unknown. The wrong value is recorded here rather than deleted, because a wiki that silently corrects itself gives a reader no way to know which of its other rows moved overnight. That correction was right: the checkpoint shipped two days after the date this page had wrongly claimed, from a different repository, under a licence nobody had published at the time.

The dense description carried on 2026-08-13 as a third-party claim (source) is confirmed by the release: 27.78B dense parameters, close enough to the name to make the naming convention on this line reliable after all — which the sibling Qwen 3.8 Max at 2.4T total / 95B active had given no reason to assume (source).

The announcement of 2026-08-03 named the checkpoint, its intended audience and an open-weights date; it did not publish a parameter count, an architecture, a context window, a price or a licence name (source). Everything in the table above arrived eleven days later, with the artefact.

Release Date

Announced 2026-08-03 alongside the general availability of Qwen 3.8 Max. Open weights stated for 2026-08-10 (source).

Not released as of this page's creation on 2026-08-04.

Update 2026-08-12 — the stated date passed and nothing shipped. The 2026-08-10 open-weights date named in the announcement has been and gone. Reporting read on 2026-08-11 states that a search of the Hugging Face model hub for qwen3.8 still returns no official repository, and the most recent community signal is an r/LocalLLaMA thread dated 2026-08-11 titled "Qwen 3.8-27b coming this week" — i.e. a restated expectation, not an artefact (source) (Orca Router) (r/LocalLLaMA).

No Alibaba channel has restated, moved or withdrawn the date in anything read. The Released row stays not yet, and the License row keeps the date it was announced with rather than being quietly rewritten — a missed date is a fact about the release, and editing it away would erase the only evidence that it slipped.

Update 2026-08-13 — WITHDRAWN. This page recorded "released 2026-08-12, two days late", reading the 10:00 UTC+8 drop as covering both checkpoints. It did not. See the correction below; the wrong claim is left visible here rather than overwritten.

Update 2026-08-14 — still not released; a third date, 2026-08-15. Reporting read 2026-08-13 states that the Max-class weights shipped and the 27B did not, with "no new date given" by Alibaba, and that a countdown page for Qwen3.8-27B is live on ModelScope pointing at midnight on 2026-08-15, reported as 00:00 JST (source).

Community signal agrees: an r/LocalLLaMA thread dated 2026-08-13 07:36 UTC is titled "The countdown to Qwen3.8-27B starts now!" (source).

The date record, kept whole because the pattern is the fact:

Date	What was said, and by whom
2026-08-03	Announced; open weights stated for 2026-08-10 (Alibaba)
2026-08-10	Passed, no repository (reporting read 2026-08-11)
2026-08-12	The 2.4T Max shipped; the 27B did not (reporting read 2026-08-13)
2026-08-15	ModelScope countdown, 00:00 JST (BigGo / PC Watch)
2026-08-14	Released, 15:00 UTC — which is 2026-08-15 00:00 JST (Kingy AI; four r/LocalLLaMA threads 14:59–16:12 UTC)
**Update 2026-08-15 — released, four days late, and the countdown was accurate to
the hour.** The ModelScope countdown pointed at 2026-08-15 00:00 JST and the
weights appeared at 2026-08-14 15:00 UTC, the same instant
(source). The
two rows above are one event written in two time zones, and they are both kept
because the confusion is part of the record.

Alibaba never restated, moved or withdrew the 2026-08-10 date in any first-party channel read here — not before the slip and not on shipping. A countdown page replaced a missed date without mentioning it. The date-slipped tag stays: the release closed at four days late, and that is now a fact about a shipped model rather than a running count.

Benchmarks

Vendor-stated, from the official model card as described by outlets — the card itself was not read, huggingface.co being blocked from this sandbox (source):

Benchmark	Qwen3.6-27B	Qwen3.8-27B
Terminal-Bench 2.1	63.4	73.0
DeepSWE 1.1	13.3	42.2
OSWorld-Verified	63.9	84.3
SWE-MM	25.7	38.6
Carried without a predecessor column: SWE-Bench Pro 61.7%, **QwenSWEBench
79.0%**, CoWorkBench 70.7%, LiveCodeBench v6 90.3%, **GPQA Diamond
89.2%** (source).

One harness is disclosed, and it is a competitor's. SWE-MM is reported to be run on the Claude Code harness, using the public dev split of SWE-bench Multimodal with modifications described in Appendix 8.3 of the Claude Opus 4.7 system card (source). That is more harness detail than any other release this wiki captured this week published for any figure — and it covers one row of four. Whether the Qwen3.6-27B comparison column was re-run under the same harness is not stated in anything read. See Eval Harness Configuration.

Alibaba's own comparison table is reported to place Opus 4.6 Max at 68.2% on CoWorkBench, below the 70.7% claimed here (source) — a 27.78B dense model reported ahead of a Max-tier closed model on one benchmark, measured by the party shipping the 27.78B model.

No third party has published a figure for this checkpoint. It appears in neither the Artificial Analysis table read 2026-08-09 (source) nor the LMArena snapshot of the same date (source), both of which predate the release by five days.

2026-08-18 — still absent from the snapshot this repo holds, against a community claim that it is no longer absent. The Artificial Analysis table captured 2026-08-16, two days after release, carries no row for Qwen3.8-27B: its Alibaba rows are Qwen3.8 Max (Intelligence Index 58), Qwen3.8 2.4T A95B (58), Qwen3.7 Plus (39) and Qwen3.6 27B (38, and 31 in its non-reasoning row), among others (source). An r/LocalLLaMA thread dated 2026-08-17 is titled "Artificial Analysis' Qwen3.8-27B benchmarks put it neck and neck with DeepSeek V4 and GPT-5.6 Luna Max". That claim cannot be checked here: the figures it refers to, if they exist, post-date this repo's most recent capture, and artificialanalysis.ai is unreachable from the daily run's sandbox — the leaderboard snapshots are written by a Sunday Action, so the next capture is 2026-08-23. For scale against the comparison the thread makes, the 08-16 table gives DeepSeek V4 Pro 0813 (max) 53 and GPT-5.6 Luna (max) 52 (source) — those two figures are held; the Qwen3.8-27B figure being compared to them is not. Recorded as an outstanding claim, not as a benchmark.

2026-08-19 — the figure arrived, from a named author rather than from a snapshot: 52. Simon Willison published "Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index" on 2026-08-17, linking the leaderboard's own model page (source):

Model	Intelligence Index	Note
Qwen3.8-27B	52	27B
GPT-5.6 Luna (max)	52	equal
GLM-5.2 (max)	53	one ahead; 753B
DeepSeek V4 Pro 0813 (max)	53	one ahead
This resolves the direction of the 08-18 r/LocalLLaMA claim — "neck and neck
with DeepSeek V4 and GPT-5.6 Luna Max" is what a 52 against 53 and 52 describes —
and it does so with a figure attributable to a named author who links the source
page. Willison's own point is the parameter gap rather than the rank: he calls the
model "a truly astonishing model", noting GLM-5.2 is 753B.

It is still not a held measurement. The number has not been read off an Artificial Analysis snapshot in this repo; the 2026-08-16 capture has no row for this checkpoint and the next capture is due 2026-08-23. The status here moves from unverifiable claim to third-party figure with a named author, and the Benchmarks position — no first-party-independent figure captured by this repo — is unchanged until Sunday. Note also what Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIs (arXiv:2608.16391) measures: a leaderboard figure is produced through some serving path, and nothing states which one produced this 52.

One figure in the coverage read is wrong and is not carried: an extract renders DeepSeek V4 Pro's size as "1.6B parameters". This wiki holds 1.6T total / 49B active from the vendor's own release (source); the "1.6B" reading is treated as a transcription error in the coverage.

Use Cases

Alibaba's stated positioning is on-premise deployment: this is described as the checkpoint "that fits ordinary on-premise GPU hardware", which organisations "can run on infrastructure they own and fine-tune on their own data" (source).

Third-party hardware claim, recorded as a claim. Unsloth states the model "will run locally on 17GB RAM/VRAM setups" (source). This is a statement about an unreleased checkpoint made before any weights existed to measure, and it is the only quantitative figure attached to this model anywhere in this wiki. It is not independent confirmation of anything.

Still a claim on 2026-08-15, and now testable. Coverage of the 2026-08-12 Max release restated the same 17 GB figure attributed to Unsloth's Daniel Han (source). The weights now exist, an FP8 repository is published alongside the main one (source), and no measurement of actual memory use has been published by anyone. The figure stays where it has been for twelve days: an estimate, now of a real artefact.

This is the sibling case to Qwen 3.8 Max, where the published quantisations start at 397 GB: the reason the 27B exists is that the flagship's smallest form is still far outside consumer hardware, and that reason is the one thing about this model that no source disputes.

2026-08-16 — somebody ran it, and the first hands-on account is about the default rather than the capability. Simon Willison reports running the model on an M5 Max MacBook Pro as a 17GB GGUF in LM Studio, and his verdict on the model is in his own title: "excellent, but it defaults to wildly overthinking things" (source).

The measurement, on one "generate an SVG drawing" prompt:

| | |---|--- | Reasoning tokens | 22,276 | Output tokens | 3,223 | Wall-clock | 21 minutes That is 6.9 reasoning tokens per output token, and it is the behaviour of the shipped default: Qwen3.8 adds official reasoning_effort levels with xhigh as the default, plus medium and low. His summary of the general case is that the model "will happily burn 20,000 reasoning tokens on a two-sentence answer", and at xhigh it "overthinks so badly that it writes terrible bushy code" (source).

Two things this does and does not settle.

• It is not the memory measurement this page has been waiting twelve days for. A 17GB GGUF is a file size, and Unsloth's claim was about 17GB RAM/VRAM. The two are consistent and the second still has not been measured by anyone. What has changed is that the artefact is now demonstrably running on a laptop, which is the thing the 27B exists to do.
• It is one prompt, on one machine, by one person, with no accuracy figure, no comparison run at medium or low, and no second task. It is an observation about a default, and this page records it as that rather than as a benchmark. The same account calls the model excellent.

The cost side of it is developed on Test-Time Compute (Inference-Time Compute Scaling), where it now sits beside Thought-Level Beam Search for Reasoning (arXiv:2608.08020) — an algorithm claiming up to 68.5% of exactly that token spend is recoverable with accuracy going up.

Reported positioning after release: local coding assistants, bounded agents, document and image analysis, private research workflows, and multimodal automation — the workloads where 27.78B dense weights are manageable and Apache 2.0 removes the licensing question (source). The licence is the substantive change: it is the first checkpoint on this line whose terms this wiki can name. See Open-Weights Policy Fight.

Compared To

• Qwen 3.8 Max — the flagship announced the same day; 2.4T total / 95B active, API-first, open weights on the same stated date
• Inkling — Thinking Machines' 975B/41B Apache 2.0 release, the nearest Western comparison by intent
• Laguna S 2.1 — Poolside's 118B/8B OpenMDW-1.1 release
• Kimi K3 — Moonshot's 2.8T open-weight model, the Chinese release this one is implicitly positioned against on deployability rather than scale
• GLM-5.3 — released the same day, and the mirror-image trade: a frontier-class coding model behind a $18/month subscription with weights withheld for two weeks, against 27.78B Apache 2.0 weights and no hosted tier
• Muse Glimmer — named in the release coverage as the model Qwen3.8-27B is claimed to beat "on many benchmarks"; no shared figure was published

Sources

• Alibaba Qwen on X — "Meet Qwen3.8-Max" → (snapshot)
• Qwen 3.8 & Qwen 3.8 27B Open Source: benchmarks and SME deployment guide — IA4PYMES
• Unsloth AI on X — 17GB claim
• Release time confirmed in advance — @ElainaNotes on X → (snapshot)
• Qwen 3.8 27B: Specs, Hardware Requirements, and How to Run It — Yotta Labs
• Qwen3.8-27B: The Version You Can Actually Run — Swfte AI
• Qwen3.8-27B Release Date: Aug 15 Countdown & What's Known — Orca Router → (snapshot)
• Alibaba to Release "Qwen3.8-27B" on August 15 — BigGo Finance
• Qwen3.8-27B: Specs, Benchmarks & Verdict — Kingy AI → (snapshot)
• Alibaba Releases Qwen 3.8-27B, Beats Muse Glimmer 30B On Many Benchmarks — officechai

Conflicting Reports

• Context window: 262,144 here, "1 million tokens" in an earlier snapshot this page cites. The 2026-08-03 capture carries a Context window | 1 million tokens row (source); the release capture carries 262,144 tokens native, extensible to ~1M via YaRN (source). They are about different models. The 1M row describes the Max-class flagship Qwen 3.8 Max, announced in the same post; the same 2026-08-03 snapshot states in prose that no context window for the 27B was found in any source read. The table row above follows the release capture.

  Disclosed rather than quietly reconciled, and the reason is this page's own history: reading one Qwen announcement as covering both checkpoints is exactly the mistake that produced the 2026-08-14 correction. A reader — or a checker — comparing these two rows should be told which model each belongs to, not left to assume they are the same one.

• "Identical to Qwen3.6-27B". An r/LocalLLaMA thread posted 2026-08-14 16:12 UTC — 72 minutes after release — is titled "Qwen3.8-27B is identical to Qwen3.6-27B!" (source). A targeted search for the substance behind it returned nothing: no reporting, no weight comparison, no repository issue. It sits directly against the model card's own four-benchmark improvement table above. Recorded, not resolved — an unverified community assertion on one side, a vendor table with one disclosed harness on the other, and no third-party measurement of either.