$ cat wiki/models/qwen-3-8.md

Qwen 3.8 Max

modelupdated 2026-08-16created 2026-07-20

chinese-lab open-weight frontier-model moe

Compared with

Spec

Attribute	Value
Developer	Alibaba / Qwen AI Lab
Released	2026-08-03
Announced	2026-07-19
Context window	1 million tokens
Pricing	$2 / M input · $6 / M output
License	Open weights shipped 2026-08-12; licence name unknown
Availability	Alibaba Cloud Model Studio (global), QwenWork, Hugging Face, ModelScope
Four rows on this page read `unknown` from 2026-07-20 until 2026-08-03. They were
filled by Alibaba's general-availability announcement, not by a later reading of the
preview (source).

License is still not a licence name, and after 2026-08-12 that is a different gap than it was before. Weights now exist and are downloadable; what no source read carries is the name of the licence they ship under. No LICENSE file was opened — this sandbox cannot reach huggingface.co — and pre-release coverage said plainly that Alibaba had "not yet disclosed the licence" (source). Earlier Qwen open lines carrying Apache-2.0 is precedent, not a licence, and is recorded as such rather than filled in.

Release Date

Preview announced 2026-07-19. Generally available 2026-08-03 (source).

Access is via Alibaba Cloud Model Studio APIs, offered globally, and through QwenWork, Alibaba's workplace agent platform (source).

Open weights are stated for the week of 2026-08-10, alongside a second checkpoint, Qwen 3.8 27B (source). This wiki recorded that as an announced intent rather than a completed release — the treatment it carried since 2026-07-19, and the same one MiniMax H3 was given until its weights actually shipped on 2026-08-03.

Open weights shipped 2026-08-12, two days after the stated week opened and two days after this page recorded the date as missed. The release was scheduled in advance for 10:00 UTC+8 on 2026-08-12 — 02:00 UTC — and the open-weight checkpoint is named Qwen3.8-2.4T-A95B (source).

This is the first time Alibaba has opened a Max-class model (source).

The wiki keeps the missed-date record above rather than rewriting it. A release that arrived two days late is a different fact from one that arrived on time, and both belong on the page.

Repositories reported live (none opened — huggingface.co is unreachable from this sandbox, so these are search-returned repository identifiers, not read model cards):

Repository	What it is
`Qwen/Qwen3.8-2.4T-A95B`	first-party BF16 weights
`Qwen/Qwen3.8-2.4T-A95B-FP8`	first-party FP8, "fine-grained fp8 quantization with block size of 128"
`unsloth/Qwen3.8-2.4T-A95B-GGUF`	third-party GGUF
`RadixArk/Qwen3.8-2.4T-A95B-NVFP4`	third-party NVFP4
(source)

Local Deployment

The figure that makes this release readable as an engineering event rather than a licensing one is the size. Unsloth's published quantisations (source):

Format	Size	Reduction as stated
Full precision	4.9 TB	—
`Q8_0` (8-bit)	2.6 TB	—
Dynamic `IQ1_S`	508 GB	89% smaller
`UD-IQ1_XXXS` (1-bit)	397 GB	91% smaller
Unsloth also reports new sub-`IQ1_S` data types — `Q1_0` (`IQ1_XXXXS`) — released
for this model, and that it runs via Unsloth Desktop or llama.cpp
(source).

"Open" and "runnable" are not the same claim here. At full precision the weights are 4.9 TB; the smallest quantisation that exists is 397 GB, which is still beyond any single consumer GPU. The checkpoint sized for that audience is Qwen 3.8 27B, released the same day. No measurement of quality loss at any of these quantisation levels appears in anything read — the only such statement is Alibaba's own, about its FP8 build being "nearly identical to the original model".

Benchmarks

First independent measurement, 2026-08-03: Qwen3.8-Max debuts on Arena.AI, placing second globally on multimodal tasks, behind only Claude Fable 5 (source).

That is the first third-party figure this page has carried. Alibaba's own July claim was "second only to Anthropic Fable 5", resting entirely on internal evaluations (source) — the independent multimodal result agrees with it, on one benchmark and one modality. It is not a general confirmation of the July claim, which was not scoped to multimodal.

As of 2026-08-03 the model was listed on neither Artificial Analysis nor Hugging Face (source), so the Intelligence Index figure this wiki uses to compare frontier models does not exist for it. The most recent Artificial Analysis read this repo holds is 2026-08-02 (source).

That gap closed on 2026-08-16, and both forms are listed — the API flagship and the open checkpoint released on 08-12 appear as separate rows and score identically (source):

Model	Artificial Analysis Intelligence Index	Context Window	Cost per Task USD	Median Tokens/s
Qwen3.8 Max	58	1M	$1.13	47
Qwen3.8 2.4T A95B	58	984k	$1.09	49
`Artificial Analysis Intelligence Index` is that publisher's composite of their own
benchmark suite, not an accuracy, and `Cost per Task USD` is their measured cost of
one task, not a per-token price.

Two readings, and the second is the useful one. 58 places it below every model this wiki calls frontier on the same table — Claude Opus 5 (max) 63, GPT-5.6 Sol (max) 61, Grok 4.6 (high) 61 — which does not agree with the July "second only to Fable 5" claim on a general measure, though it is not the multimodal task the Arena.AI result was about. And the open checkpoint measures the same as the paid one, which is the first evidence held here that the artefact Alibaba published is the artefact it was selling. The 984k against 1M context difference is a property of the serving deployment, not of the weights — see Open-Weights Policy Fight.

2026-08-06 — now listed, and the headline about it is wrong. Figures reported for Artificial Analysis' agentic measurements (source):

Measure	Qwen3.8-Max	Comparison as reported
Agentic Index	58	tied with Claude Opus 5 at xhigh effort; Opus 5 at max effort leads at 59
GDPval-AA (44 occupations, shell + browsing)	1,739 Elo	GPT-5.6 Sol Max 1,730 · Kimi K3 1,685 · Claude Opus 5 1,852
Intelligence Index	56	reported ahead of all US companies except Anthropic and OpenAI
The models in that second row are GPT-5.6 Sol (and Terra, Luna) and Kimi K3;
the leader is Claude Opus 5.

The claim that circulated with these numbers — an r/LocalLLaMA post titled "Qwen 3.8 Max now ranked as best overall model ahead of Opus 5 by Artificial Analysis agentic index", carried onto Hacker News — is not what the figures say. Opus 5 at max effort is reported one point ahead on the Agentic Index, the tie is against Opus 5 at a lower effort setting, and on GDPval-AA the reported gap is 113 Elo the other way.

What the figures do support is narrower and still notable: on agentic work the top of the leaderboard is separated by about one index point, and an open-weight-announced Chinese model is inside that margin.

Recorded as reported. No Artificial Analysis page was read — this sandbox cannot reach the site, the sources/evals/ snapshots come from a GitHub Action, and no snapshot this repo holds carries an Agentic Index column to check these against (source).

2026-08-12 — SWE-bench Pro, reported as a position rather than a score. Coverage of the open-weights release places Qwen3.8-Max on SWE-bench Pro as "mid-pack: ahead of GPT-5.6 Sol (64.6), behind Opus 4.8 (69.2), and 12 points below Fable 5 (80.0)", with a "15-point gap to Fable 5 on FrontierSWE" (source).

Qwen3.8-Max's own score is not stated in anything read — only its position against three named models — so no figure for it is recorded here. The models named are GPT-5.6 Sol (and Terra, Luna), Claude Opus 4.8 and Claude Fable 5.

The Fable 5 figure quoted in that coverage is 80.0. This wiki holds a disclosed conflict on that number, recorded on Claude Fable 5, between 80.0 and 80.3 from two cited sources. The coverage above is a third party quoting one of the two; it is not a resolution of the conflict and is not treated as one.

Harness note, as stated: FrontierSWE figures were "evaluated with the Claude Code harness", MEAN@5, from the official leaderboard as of 2026-08-03 (source) — a harness and a sampling budget, which Eval Harness Configuration records as the two things a bare benchmark number usually omits.

No benchmark score exists for the released open-weight checkpoint itself. Every figure on this page measures the API-served Qwen3.8-Max. Whether the downloadable Qwen3.8-2.4T-A95B reproduces them is unmeasured in anything read — the gap MiniMax H3 was recorded for on 2026-08-03, when shipped weights did not reproduce the specification the model had been priced on.

Architecture

2.4 trillion total parameters (MoE) (source)
95 billion active parameters at inference (source) — this page recorded the active count as "not disclosed — a critical gap for assessing true inference cost" from 2026-07-20; it was disclosed on 2026-08-03, 15 days later
Further architecture details not published

Context

Announced a few days after Kimi K3 (2.8T, July 16)
Coverage of the GA places Qwen3.8-Max "close to the size of" Kimi K3 (source)
The smaller Qwen 3.8 27B was announced the same day and, per one write-up, "almost entirely ignored in coverage" (source)
Open-weight timing now matters to Open-Weights Policy Fight: two of the largest announced open releases of 2026 are Chinese, and both attached their weights to a date only after shipping an API first
2026-08-12 — that pattern completed. Coverage names it directly: Chinese labs "have spent 2026 converging on the same play: launch closed with a benchmark case, monetize the API window, then open the weights once the news cycle has done its work" (source). This model ran that sequence over 24 days: closed preview 2026-07-19, paid API 2026-08-03, open weights 2026-08-12

Sources

Referenced by

Alibaba / Qwen AI Lab DeepSeek V4-Pro-0813 June 2026 — Monthly Digest Kimi K3 MiniMax H3 Open-Weights Policy Fight Qwen 3.8 27B Weekly Synthesis — W32 (2026-08-03 → 2026-08-09)Weekly Synthesis — W33 (2026-08-10 → 2026-08-16)