Alibaba releases Qwen3.8 open-weight models, gaining traction on leaderboards
ByPulseAugur Editorial·[32 sources]·
Alibaba's Qwen has released its Qwen3.8 series of open-weight models, including Qwen3.8-27B and Qwen3.8-2.4T-A95B. The Qwen3.8-27B model boasts a 262K native context window, extendable to 1M tokens via YaRN, and has achieved significant traction on platforms like Hugging Face, with millions of downloads for its quantized versions. This release positions Qwen as a competitive option for both local deployments and agent development, with Qwen3.8-Max notably outperforming Claude Fable-5 on a frontend code leaderboard.
AI
IMPACT
Accelerates availability of capable open-weight models for developers, potentially driving innovation in local deployments and agent development.
RANK_REASON
Cluster contains announcements of new open-weight models from Alibaba's Qwen lab, including performance claims and release details.
A local 27B model scoring frontier performance! Huge thanks to @cline for the shoutout.🥳 This is just the beginning — Qwen3.8-27B will keep finding its way into more fields.🌱
X — Qwen (Alibaba)
TIER_1English(EN)·Alibaba_Qwen·
We promised open weights for Qwen3.8. Now, time to meet them! 🎉
⚡ Qwen3.8-27B:
- A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows.
- 262K native context, easily extendable to 1M …
🤗 Qwen/Qwen3.8-27B is climbing on Hugging Face with 267.7k downloads in 30 days and 10.4k likes. Open-weight, Apache-2.0, and image-text-to-text—real-world usage shows it's breaking out. https:// olud.ai/#leaderboard # OpenSource # AI # HuggingFace
🤗 Qwen/Qwen3.8-27B has 9.3k likes but only 2 downloads in 30 days. That gap suggests the community is watching closely—what’s unique about this open-weight image-text model? https:// olud.ai/#leaderboard # OpenSource # AI # HuggingFace
<!-- SC_OFF --><div class="md"><p><a href="https://huggingface.co/jojohai/Qwen3.8-27B-MTP-graft">https://huggingface.co/jojohai/Qwen3.8-27B-MTP-graft</a></p> <p>Tested on Vulkan, the grafting saves RAM compared to using an external file. </p> <p>What I don't guarantee however is …
🤗 Qwen/Qwen3.8-27B: open-weight, 1M downloads in 30 days, Apache-2.0. Real usage beats hype. See the daily ranking. https:// olud.ai/#leaderboard # OpenSource # AI # HuggingFace
unsloth/Qwen3.8-27B-GGUF just hit 3.6M downloads in 30 days. That’s real pull for a GGUF quant—not just a base release. https:// olud.ai/#leaderboard # OpenSource # AI # HuggingFace
RT @bnjmn_marie: Mein erster vollständiger Durchlauf von Qwen3.8 27B auf DeepSWE 1.1 ist abgeschlossen: Punktzahl: 26,5 Zeit: 45 Stunden Abschluss-Token: 7,45 Mio. Gesamtinput- und Output-Token: 820 Mio. Modellaufrufe: 9.591 Die Punktzahl liegt 16 Punkte unter dem Ergebnis, das v…
Qwen/Qwen3.8-27B is climbing on Hugging Face with 415k downloads in 30 days and 10.9k likes. Open-weight, Apache-2.0 licensed — and ranked by real-world usage, not hype. See whose weight actually moves. https:// olud.ai/#leaderboard # OpenSource # AI # HuggingFace
Gave it another try today. Tried a few different versions. Tinkered with the settings. Qwen 3.8 is unusable for me. It couldn’t even summarize and commit my work tree changes. It took 30 minutes generating tens of thousands of tokens but never got to a result. The frontier model …
<!-- SC_OFF --><div class="md"><p>The dense Qwen release is back!</p> <p><strong>Qwen3.8-27B Uncensored Aggressive is out with the complete K_P quant range, Vision, native NextN, and HauhauCS FastMTP.</strong></p> <p>Aggressive here means no refusals, no personality alterations, …
I’ve tried Qwen 3.8 today. Spent about 4 hours and wasn’t able to get anything meaningful out of it. It does generate a whole lot of more tokens compared to 3.6 but it is mostly gibberish. It never seems to get to the end of reasoning, it just continues. Well, 3.6 was working wel…
Not a fan of Qwen 3.8 27B. Overthinks too much => wasted tokens. Has someone ran it without too much thinking? Does it perform well with that knob down, or letting it think all the way is required to be good? # AI # ArtificialIntelligence # LLM # Qwen # Qwen38 # Qwen3 # Ollama # …
RT @analogalok: Qwen 3.8 35B A3B fast bestätigt! Auf GitHub gesichtet! Während alle damit beschäftigt sind, Qwen3.6 27B gegen das neue Qwen3.8 27B Dense Drop auf ihren einzelnen RTX 4090s und 3090s auszutauschen, verbirgt sich das eigentliche stille Update in diesem GitHub-Commit…
Chubby (@kimmonismus) 작성자는 Qwen 3.8 27B를 이전 세대 대비 큰 도약으로 평가하며 성공적인 모델이라고 언급했다. 다만 학습 방식, 벤치마크, 추론 비용 등 개발자가 검증할 수 있는 기술 세부 정보는 제공되지 않았다. https:// x.com/kimmonismus/status/20885 93968233615608 # qwen # llm # openmodel # ai
🔓 Qwen3.8-27B just dropped as open weights (Apache 2.0). Runs on a single RTX 4090. Your data never leaves your machine. No token meter. Perfect for SMBs tired of API bills and privacy risks. https:// signaldigital.net/2026/08/14/q wen3-8-27b-just-dropped-as-open-weights-heres-wh…
Qwen released v3.8 of their 27B parameter model today, as open weights. This is how the AI bubble pops, when you're able to run extremely competent models on regular "gaming" GPUs. 16GB VRAM is enough to run it as your main architect for coding, with other models for implementati…
FWIW, After trying # Qwen 3.8 Q4_K_M for real work, I'm going back to Qwen 3.6 MTP. Inference is just WAY too slow. I got the same work done with 3.6 MTP in less than 10% of the time. Probably its the config, but the recommended configs on # unsloth are very wrong and because of …
VentureBeat: Qwen3.8-27B runs frontier-class coding agents and reasoning locally, no cloud API required. “The biggest AI model release of the past few days, at least among the developers and AI power users on social media, wasn’t a frontier cloud model from OpenAI, Anthropic or G…
Barely 30 days live and Qwen/Qwen3.8-27B already has 91.9k downloads — that puts it ahead of almost every other open-weight model released this year. https:// olud.ai/#leaderboard # OpenSource # AI # HuggingFace
Qwen3.8-27B release day demos are out. Worth watching not for the hype, but for what the benchmark surface actually reveals — capability claims vs. real-world task performance are two different conversations. Open-weight models at this scale shift the threat model for local infer…
