A title card for the Qwen3.8-27B and Artificial Analysis article: a magnifying glass hovering over a chip with an empty 'awaiting data' scorecard, titled 'Qwen3.8-27B on Artificial Analysis' with the subtitle 'No independent score yet — here's the data that stands in'.
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Qwen3.8-27B on Artificial Analysis: No Index Yet, and the Data That Stands In

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Magnus Corvin

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Artificial Analysis has not indexed Qwen3.8 27B. Checked 2026-08-15, the model's page on artificialanalysis.ai returns a 404 — no Intelligence Index, no output speed, no AA price, no independent benchmark. The Apache 2.0 weights shipped on August 13–14, so that will probably change within days or weeks; it has not changed yet. Until it does, every Qwen3.8 27B number you can find is Ali​baba's own model card or a news outlet repeating it. Here is the one-line answer, the AA data that does exist for the Qwen3.8 family, the official specs, and an honest read of what the "it beats everything" headlines actually are.

Sourcing note: everything below is labeled. Official means taken from Ali​baba's Qwen/Qwen3.8-27B model card on Hugging Face (August 2026). Independent means measured and reproduced by an outside party — nothing for the 27B qualifies yet. Relayed means a news outlet copying vendor numbers, which is not independent.

The one-line answer, dated

Qwen3.8 27B on Artificial Analysis: no page, no score. The URL artificialanalysis.ai/models/qwen-3-8-27b returned "404 Page not found" when checked 2026-08-15.

• The "Qwe​n 3.8" AA scores circulating online — Intelligence 58, Agentic Index #1 — belong to Qwen3.8-Max, the 2.4T-parameter API flagship. Not the 27B.

• The most recent 27B AA does track is the predecessor Qwen3.6-27B: Intelligence Index ~37–38 in reasoning mode, ~55–59 tokens/sec output. That is your baseline for what an indexed 27B looks like.

A status card for Qwen3.8-27B on Artificial Analysis, checked 2026-08-15: the model is marked NOT INDEXED (no page, no score, no price; the model URL returns 404), while Qwen3.8-Max 2.4T A95B is indexed at Intelligence 58 and Agentic Index #1, and the predecessor Qwen3.6-27B is indexed at Intelligence ~37–38 and ~55–59 tokens/sec.

What Artificial Analysis actually shows for the Qwen3.8 family

AA's leaderboard lists "Qwen3.8 2.4T A95B" — that is Qwen3.8-Max — at Intelligence Index 58, the second-best open-weights model on the index (Artificial Analysis models page, accessed 2026-08-15). On August 6, 2026, AA's Agentic Index ranked Qwen3.8-Max #1 globally — the first time a Chinese lab topped it, ahead of Claude Opus 5 and GPT-5.6 — as widely reported from AA's leaderboard that day.

None of those numbers transfer to the 27B. Qwen3.8-Max is a 2.4T-parameter mixture-of-experts API model with roughly 95B active parameters; the 27B is a dense, self-hostable download. They share a generation label, not an evaluation.

The predecessor is your baseline: Qwen3.6-27B on AA

AA has tracked Qwen3.6-27B for months. Depending on snapshot it scores ~37–38 on the Intelligence Index in reasoning mode (around 31 in non-reasoning mode) at ~55–59 tokens/sec — a single-GPU model holding its own against mid-tier frontier models from a year ago. That is the shape of data to expect when AA indexes the 3.8-27B: an index in the same ballpark, a speed on consumer hardware, and a price that mostly reflects the hosting choice. Treat it as a baseline, not a prediction — the 3.8-27B adds hybrid attention and native video input, either of which could move the numbers.

The official data: Ali​baba's model card, not independent

A data card for Qwen3.8-27B from Alibaba's official model card: specifications (27B dense, 28B with vision encoder, 64 layers, hidden size 5,120, vocabulary 248,320, 48 linear + 16 full attention heads, 262K native to 1M context via YaRN, text+image+video input, ~55.6 GB BF16) and vendor-reported benchmarks (Terminal-Bench 2.1 73.0, SWE-bench Pro 61.7, LiveCodeBench v6 90.3, GPQA Diamond 89.2, OSWorld-Verified 84.3), labeled Alibaba-reported, unreproduced.

These are the verified specs from the Qwen/Qwen3.8-27B model card (Apache 2.0, August 2026):

• 27B dense parameters (28B with the vision encoder), 64 layers, hidden size 5,120, vocabulary 248,320.

• Hybrid attention: 48 linear-attention heads (Gated DeltaNet) plus 16 full Gated Attention heads — roughly a 3:1 linear-to-full split.

• Context: 262,144 tokens native, extensible to 1,000,000 via YaRN.

• Input: text, image, and video natively; thinking mode on by default.

• Size: ~55.6 GB in BF16; ~16–17 GB of weights at Q4 — a 24 GB GPU is the practical single-card target.

Vendor-reported benchmarks from the same card (Ali​baba's word, unreproduced): Terminal-Bench 2.1 73.0; SWE-bench Pro 61.7; LiveCodeBench v6 90.3; GPQA Diamond 89.2; OSWorld-Verified 84.3.

What the "it beats everything" headlines actually are

A Chinese tech-news relay (Gate.it, mid-August 2026) leads with "Qwen3.8 27B beats Meta's Muse Glimmer across all 8 benchmarks" — Terminal-Bench 2.1 73.0 vs 51.7, SWE-bench Pro 61.7 vs 51.2 — and claims wins over Claude Opus 4.6 in 15 of 19 overlapping tests. Those figures are Ali​baba's model-card numbers copied into a news summary. No outside lab has run the 27B, and the relay does not claim one did. "Third-party coverage" here means third-party repetition, not third-party verification.

A screenshot of the Qwen/Qwen3.8-27B model card on Hugging Face, showing the Apache 2.0 license, the model type 'Causal Language Model with Vision Encoder', BF16 tensor type, the note that the Qwen Cloud hosted version is coming soon with 1M context, and 4 adapters, 46 finetunes and 304 quantizations.

Why there is no AA price for the 27B — and what that tells you

AA prices models by API endpoint. The 27B has no official hosted API yet: Ali​baba's Qwe​n Cloud hosting is "coming soon" with a 1M-token context. There is nothing to price. That is the real contrast at the center of this model. Qwen3.8 27B is an open-weights download; its price is your electricity bill. At Q4 on a 24 GB card it costs $0 per token. The API-only flagship Qwen3.8-Max is $2 per million input / $6 per million output. A self-hosted model does not appear in an API-pricing index precisely because it is free per token — the opposite situation from every API model AA does price.

What to use until AA indexes it

You want an independently-scored Qwe​n 27B today: run or call Qwen3.6-27B. It is AA-indexed (Intelligence ~37–38 in reasoning mode), Apache 2.0, self-hostable, and it is the model the 3.8-27B has to beat. If you would rather call it over an API than run it, it is in the OrcaRouter catalog at provider list price with 0% markup — one key, no second contract.

You want the API flagship: Qwen3.8-Max is AA-indexed at 58 and Agentic #1, at $2/$6 per 1M tokens. You are paying for a 2.4T MoE, not a local model.

You want the 3.8-27B specifically: download the Apache-2.0 weights and self-host. Treat every benchmark as Ali​baba's word until an independent harness publishes. Watch the AA model page (artificialanalysis.ai/models/qwen-3-8-27b) and the open harnesses — when a real number lands, it will be obvious.

The bottom line

Qwen3.8 27B has no Artificial Analysis score because Artificial Analysis has not measured it — and no AA price because it is a self-host model, not an API. Every "Qwe​n 3.8 beats X" headline you can find today is Ali​baba's model card in a news costume. The numbers that are genuinely independent in this story belong to the siblings: Qwen3.8-Max at Intelligence 58 with the #1 Agentic Index, and Qwen3.6-27B at ~37–38. Anchor on those; treat the 27B's own figures as vendor claims until someone independent runs it. Check back — this page will change the day AA publishes.

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