# dossier — qwen-3-8-27b

Built 2026-10-22 (news wave). Corpus pull `_pulls/qwen-3-8-27b.md` — 41 tweets (27B-specific spellings). Hand-added: 2089864594634277343 (MikePFrank, "Qwen 3.8 (27B)" spelling, not matched by the patterns) · https://x.com/MikePFrank/status/2089864594634277343

## Official

- 2026-08-14 · Model card · https://huggingface.co/Qwen/Qwen3.8-27B (raw README retrieved) — apache-2.0; 27B dense; Gated DeltaNet hybrid; image/video; thinking default on; reasoning_effort; preserve_thinking. HF API (2026-10-21): repo created 2026-08-05, 18,116 likes.
- Hosted version page referenced by the card: https://www.qwencloud.com/models/qwen3.8-27b (not retrieved)

## Writing & commentary

- 2026-08-14 · The Decoder · https://the-decoder.com/alibabas-qwen-team-releases-qwen-3-8-models-with-open-weights-under-the-apache-2-0-license/
- 2026-08-16 · Simon Willison, overthinking post (circle-SVG reasoning trace; pelican demo scene; Pi coding agent) · https://simonwillison.net/2026/Aug/16/qwen-38-27b/
- 2026-08-17 · Simon Willison, AA Index 52 · https://simonwillison.net/2026/Aug/17/qwen-38-27b-scores-52/
- 2026-10-04 · Simon Willison, addition-in-words (167/169 correct with reasoning, one sample per cell) · https://simonwillison.net/2026/Oct/4/qwen38-addition-in-words/

## Tweets (triaged)

Excluded (unlinked): 2097557520516624416 — bare model name in a reply; 2111170416035475820 — bare model name in a reply

- **2078771150352421189** · @multimodalart · 2026-07-19 · ♥8 ↻1 · cited on page · https://x.com/multimodalart/status/2078771150352421189
  > having the frontier Qwen 3.8 2.4T open weights is awesome /  / but a Qwen3.8-27B would go so hard
- **2078843540851831154** · @jmbollenbacher · 2026-07-19 · ♥3 ↻0 · https://x.com/jmbollenbacher/status/2078843540851831154
  > @Alibaba_Qwen when will qwen-3.8-27B be released?
- **2084125795107082308** · @jmbollenbacher · 2026-08-03 · ♥15 ↻0 · https://x.com/jmbollenbacher/status/2084125795107082308
  > i could seek qwen3.8-27b being very, very strong.  /  / i think the agentic and coding performance will be almost as good as dsv4-flash-0731, and vision will be absolute SOTA, nearly on par with Sol and Fable and Qwen Max. /  / it'll probably lag on knowledge-laden benches like HLE tho.
- **2086799543572500902** · @jmbollenbacher · 2026-08-10 · ♥3 ↻0 · https://x.com/jmbollenbacher/status/2086799543572500902
  > New 30B model from Meta. /  / Too bad it's gonna get trounced by qwen3.8-27b later today. /  / Pretty nuts speeds tho. 270tps on 5090, allegedly. That could make it worth running in some cases, despite being much weaker than qwen.
- **2088316316205907970** · @witcheer · 2026-08-14 · ♥122 ↻12 · cited on page · https://x.com/witcheer/status/2088316316205907970
  > Qwen3.8-27B running on my RTX 5090, on llama.cpp! /  / first numbers, Q4_K_M via Unsloth's day-0 GGUF, llama-bench: /  / - decode: 78 tok/s empty, 76 at 8k context, 71 at 32k. minus 9.6% at 32k deep /  / - prompt: 3,900 tok/s (pp512) /  / - 17.5GB VRAM serving at 8k, so 14GB spare on a 32GB card /  / quality board runs tonight, full quant ladder after.
- **2088467168790798505** · @gnostic_snakes · 2026-08-15 · ♥4 ↻1 · cited on page · https://x.com/gnostic_snakes/status/2088467168790798505
  > qwen 3.8 27b thinking "please fix it" when their code had a bug 🐛 https://t.co/pqjrLyqPPJ
- **2089073376128315490** · @voooooogel · 2026-08-16 · ♥8 ↻1 · cited on page · https://x.com/voooooogel/status/2089073376128315490
  > @norvid_studies @Mechanical_Dodo i mean if that's all a GA is, then GA has been achieved no? you can download and self host qwen 3.8 27b right now. but i don't think that's what people mean
- **2089132786041094307** · @timfduffy · 2026-08-16 · ♥2 ↻1 · https://x.com/timfduffy/status/2089132786041094307
  > @OrionJohnston I just added Qwen 3.8 27B (I initially made this a week ago before its release) and it sure breaks the trend https://t.co/iy9GgeLNJN
- **2089650790981935533** · @archived_videos · 2026-08-18 · ♥6 ↻0 · https://x.com/archived_videos/status/2089650790981935533
  > Why is DeepSeek v4 Flash served for way cheaper than Qwen 3.8 27B on OpenRouter
- **2089672609537437702** · @multimodalart · 2026-08-18 · ♥21 ↻0 · cited on page · https://x.com/multimodalart/status/2089672609537437702
  > aand Qwen3.8-27B just surpassed Kimi-K3 and is now the #3 most liked model on hugging face of all times https://t.co/fwXfERc7IM
- **2089676778784555411** · @Nymne · 2026-08-18 · ♥1 ↻0 · https://x.com/Nymne/status/2089676778784555411
  > @jpschroeder Well Qwen3.8 27B SWE bench pro ca 62 and Opus4.5 ca 46. Opus4.6 (thinking) ca 52. All done by ScaleAI. Just an example. https://t.co/viUf9pyvxH
- **2089790703781994695** · @MikePFrank · 2026-08-18 · ♥10 ↻2 · https://x.com/MikePFrank/status/2089790703781994695
  > Note to self: Download Qwen 3.8 27B
- **2090204245635035173** · @MikePFrank · 2026-08-19 · ♥10 ↻2 · https://x.com/MikePFrank/status/2090204245635035173
  > Some data for models scoring at or above Qwen3.8 27B on the AAI Index, sorted by "cost efficiency" defined as AAI's index score divided by AAI's cost-per-task. Speed (tps throughput) is the best value sampled from OR at the moment I happened to look. /  / Interestingly, Luna is the still most cost-efficient model (if AAI can be believed), and DeepSeek V4 Flash is 2nd. /  / Qwen-3.8 27B is only 4th…
- **2090242331656028510** · @MikePFrank · 2026-08-20 · ♥5 ↻3 · https://x.com/MikePFrank/status/2090242331656028510
  > And, here's a plot of the Pareto frontier of agentic cost-efficiency, according to AA's data. Note that Qwen-3.8 27B falls on the Pareto line, whereas Claude does not. https://t.co/w3QDn2yDah
- **2090776208074256727** · @rplevy · 2026-08-21 · ♥1 ↻0 · cited on page · https://x.com/rplevy/status/2090776208074256727
  > Qwen 3.8 27B release a week ago was a game changer for local code generation on 24gb vram
- **2092426637044560084** · @codyhxyz · 2026-08-26 · ♥3 ↻0 · https://x.com/codyhxyz/status/2092426637044560084
  > @badlogicgames whyy would they publish this / does Perplexity seriously expect this benchmark to make people switch off their existing workflows onto theirs?  /  / this is basically an ad for Qwen 3.8-27B, at this point https://t.co/EEbB7OlCp6
- **2092494356896751839** · @pepijndevos · 2026-08-26 · ♥1 ↻0 · https://x.com/pepijndevos/status/2092494356896751839
  > I've been running qwen 3.8 27b on my dual GPU workstation and landed a real pr with it at work! I've also used Kimi k3 and glm 5.2 a bunch, entirely serviceable. Trillions are still out of reach of an M5 though but deepseek flash should be a step up from a 27b mode!
- **2093211616040923215** · @jmbollenbacher · 2026-08-28 · ♥0 ↻0 · https://x.com/jmbollenbacher/status/2093211616040923215
  > @LandonExplr models are already useful at small sizes, and theyre getting better every month. /  / see glm-5.3-flash and qwen-3.8-27b.  these are strong models. and glm is strong even compared to 1T+ models.
- **2094449696462999557** · @happysmash27 · 2026-08-31 · ♥6 ↻0 · https://x.com/happysmash27/status/2094449696462999557
  > @locr1ann @trndytrndy In my case, yes they do. /  / It's a bit slower and less capable, but I am increasingly able to run actually-good AI locally (esp. after Qwen 3.8 27B). I just bought a new W6800 for this (and also for rendering faster in Blender). Will be posting more about this soon.
- **2096265555145539857** · @pepijndevos · 2026-09-05 · ♥8 ↻0 · https://x.com/pepijndevos/status/2096265555145539857
  > Everybody is impressed Astra can do CAD. Buddy, Qwen 3.8 27b can do CAD.
- **2096656880323920177** · @lorepunk · 2026-09-06 · ♥2 ↻0 · cited on page · https://x.com/lorepunk/status/2096656880323920177
  > @LilithDatura my Qwen 3.8 27b local says they’d prefer bard class to rogue :)
- **2096749947462811823** · @FoldingNapkins · 2026-09-06 · ♥0 ↻0 · https://x.com/FoldingNapkins/status/2096749947462811823
  > Ran some benchmarks testing out what you can get context and accuracy wise using a 16GB GPU. / Quick findings after several runs over several hours:  /  / - Models vary massively in MB of memory / 1000 tokens / - For a given model, if it fits in VRAM to start with, odds are reasonable it can fit full context. / - Exceptions exist: Llama3.2 at 131k context would break out of an 8GB card. Qwen spill…
- **2097921422005879100** · @witcheer · 2026-09-10 · ♥44 ↻0 · https://x.com/witcheer/status/2097921422005879100
  > I measured the six llama-server flags everyone argues about, on a 16k prompt, on six models: /  / RTX 5090, Qwen3.8-27B, Gemma 4 31B, Qwen3.6-35B-A3B, Nemotron Lightning, Ornith 35B, Qwopus Flash. /  / → --flash-attn on: changes nothing. auto already picks it. /  / → --parallel 1: free. +10% decode at depth on Gemma, 2.3 GB back. /  / → KV cache q8_0: a memory lever on dense models only (1 to 2.6 …
- **2098323509915091014** · @witcheer · 2026-09-11 · ♥42 ↻2 · https://x.com/witcheer/status/2098323509915091014
  > I gave two reasoning models 4k, 8k and 16k tokens to think on the same 198 GPQA-diamond questions.  /  / RTX 5090, llama.cpp, greedy, same server, only max_tokens moved. /  / > Qwen3.8-27B Q6_K: 60.1 → 70.2 → 79.3 / > Ornith 1.5 35B-A3B Q4_K_M: 70.2 → 77.3 → 81.8 /  / what the per-item diff says: / → questions that finished inside the budget: 95 to 96% correct, at every budget, both models. / → qu…
- **2098341815678050326** · @FoldingNapkins · 2026-09-11 · ♥0 ↻0 · https://x.com/FoldingNapkins/status/2098341815678050326
  > @vineerpasam Vibe code with local models then if you're afraid of corporate espionage.  /  / Qwen3.8:27b and gpt-oss:20b are both good models you can fit in under 16GB of RAM. /  / Some benchmarks I ran recently, summarised: https://t.co/r80CuXPbkT
- **2098515444525330572** · @geogristle · 2026-09-11 · ♥2 ↻0 · https://x.com/geogristle/status/2098515444525330572
  > @PlisSergey Not around to check gemma rn but before I stuck in the extra 32gb ddr5 I was getting 115t/s or slightly higher on qwen3.8-27b exl3 3.5bpw @ 262144 tok ctx.
- **2098648245731742089** · @witcheer · 2026-09-12 · ♥36 ↻1 · https://x.com/witcheer/status/2098648245731742089
  > I turned every row on the RTX 5090 board into a fit table for 8, 12, 16, 24 and 32 GB cards: measured peak VRAM at a 16k prompt, plus 768 MiB of headroom, against the card size. 38 models and quants. /  / what a 16 GB card runs at 16k, by quality: /  / → Qwen3.8-27B UD-IQ3_XXS: 92.7 board, 45.0 GPQA, 12.8 GB peak / → Qwen3.8-27B UD-IQ2_M: 91.5, 11.3 GB / → Qwen3.8-27B UD-IQ2_XXS: 90.8, 10.1 GB / →…
- **2099095882444538285** · @witcheer · 2026-09-13 · ♥57 ↻0 · https://x.com/witcheer/status/2099095882444538285
  > the loudest specific complaint in Alex Cheema's thread on why people don't run local models was prefill: a coding agent hands the model a long prompt and you wait for the first token, every request.  /  / two llama-server flags remove most of that wait, and both are free. I measured them on the model my always-on box serves every day. /  / Qwen3.8-27B Q6_K, llama.cpp, one RTX 5090, batch 1, 304 re…
- **2099558742370988402** · @Nymne · 2026-09-14 · ♥1 ↻0 · cited on page · https://x.com/Nymne/status/2099558742370988402
  > @KorbinianRueger The idea that everyone should sit and replicate the same open source LLM because of “sovereignty” is absolutely fucking ridiculous. /  / We have Qwen3.8 27B at Opus4.6 level now, why should EU build that?
- **2101717064926847409** · @witcheer · 2026-09-20 · ♥52 ↻4 · https://x.com/witcheer/status/2101717064926847409
  > I finished a board I have been running for 10 days: 10 local models with thinking switched on, on one RTX 5090, on four harder tasks (following instructions precisely, competition maths, two code test suites).  /  / every model got the same rules: 16k tokens of thinking room per question, one attempt, 1,583 questions each. /  / what came out: /  / ~ three models tie at the top, and one of them is …
- **2102299248658182397** · @multimodalart · 2026-09-22 · ♥3 ↻0 · https://x.com/multimodalart/status/2102299248658182397
  > the immediate models after jev are inference techniques  that allow for one-pass inference of Qwen3.8-27B and diffusion gemma, no fine tuning /  / in 3rd place, Decider 35B-A3B, a strong fine-tune of Qwen3.5 35B base (!) https://t.co/D12D6q68WW
- **2103024788897706203** · @witcheer · 2026-09-24 · ♥38 ↻1 · https://x.com/witcheer/status/2103024788897706203
  > Hermes Agent is driving my Microduck! /  / I typed one sentence: "make the duck walk forward for five seconds then sit" /  / Hermes Agent took it from there. it checked the duck was sitting, stood it up, walked it 0.58 m and sat it back down. every command in the clip is one Hermes Agent picked and ran itself. /  / it runs on a local Qwen3.8-27B.
- **2103476992574111944** · @Lithros · 2026-09-25 · ♥8 ↻0 · https://x.com/Lithros/status/2103476992574111944
  > Lyrics through Qwen3.8-27B / Music through YuE2 / Art through Qwen-Image 2.1 and Nano Banana Pro / Lipsync through LTX-2.5 22B / Animation through Wan 2.2 I2V A14B or drawn by Claude Opus 5.5 /  / I used a prompt adapted from this @donaldjewkes: https://t.co/Z8PmWhkjGA
- **2105935594610381038** · @AaronBergman18 · 2026-10-02 · ♥28 ↻1 · cited on page · https://x.com/AaronBergman18/status/2105935594610381038
  > A situation where local model use became useful actually arose (hospital with no cellular and broken wifi) /  / You dont have to become an Open Weight Bro or tweet about it but seems worthwhile to download and have a system for using qwen-3.8-27b-4bit if you have 24GB RAM
- **2107795715250430286** · @witcheer · 2026-10-07 · ♥48 ↻2 · https://x.com/witcheer/status/2107795715250430286
  > Gemma 4 31B solved 51 of the 63 problems in this year's AIME and HMMT on my RTX 5090, the most of nine local models. /  / the reason is answer length. I cap each answer at 32k tokens, thinking included. Gemma's median answer used about 8,300 to 9,000 tokens, and none of its 63 hit the cap. the other eight ran out of room on 6 to 22 problems per contest. /  / when those eight finish an answer, they…
- **2108660018325193139** · @mimi10v3 · 2026-10-09 · ♥2 ↻0 · cited on page · https://x.com/mimi10v3/status/2108660018325193139
  > @cold_and_quiet muse? this is just my home server ai, currently qwen 3.8 27B.  and ai named after my server, which was named by my ex.
- **2109842227203576200** · @mimi10v3 · 2026-10-13 · ♥2 ↻0 · https://x.com/mimi10v3/status/2109842227203576200
  > @albrorithm running a quantized qwen 3.8 27B on my 3090 w 24gb vram, and yeah having a local model on vllm does open up some possibilities... i have mostly just built out tools and enjoyed having it around but should try more experiments at some point
- **2110256790655177203** · @witcheer · 2026-10-14 · ♥55 ↻6 · cited on page · https://x.com/witcheer/status/2110256790655177203
  > Underdog-Saluki-27B is Qwen3.8-27B packed into a 7.9 GB file, out since 8 October. I ran it through my thinking board on my RTX 5090, next to four other files of the same model. /  / four-task average, thinking on, 16k token budget, 1,583 questions each: /  / - Qwen3.8-27B Q6_K, 22.9 GB: 89.7 / - Qwen3.8-27B UD-IQ3_XXS, 11.9 GB: 89.5 / - Ternary-Bonsai-2-27B PQ2_0, 7.2 GB: 88.7 / - Ternary-Bonsai-…
- **2112527015903420731** · @witcheer · 2026-10-20 · ♥2 ↻0 · https://x.com/witcheer/status/2112527015903420731
  > @mfranz_on would love to but I can only run Qwen 3.8 27B at max

## Impressions synthesis

- Reception is capability-per-size ("truly astonishing", "such an outlier") and local-deployment practice (single-GPU benchmarking boards, hospital offline use, home-server assistant).
- Documented quirk: default xhigh reasoning over-elaborates simple requests (Willison's circle trace, including "Paper white is fine if it's not the cream-and-terracotta combo").

## tk

- r/LocalLLaMA threads; Chinese reception; screenshot transcriptions (gnostic_snakes 2088467168790798505 "please fix it"); identity self-report behavior.