Qwen ✓
@Alibaba_Qwen · joined 02 Feb 2024
Open foundation models for AGI.
286 227Followers
3Following
1 052Posts total
3.5MViews on collected posts
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A great example of practical AI solving real-world problems! Faster than ever. Thanks for building with Qwen. @cerebras Try it out for yourself! 🏠
16.6K views · 136 likes
· 7 reposts · 13 replies
Open on X →
Meet Qwen3.8-LiveTranslate, Qwen's next-generation real-time simultaneous interpretation model! 📢
Built on an Interleave architecture, it improves faithfulness, fluency, and conciseness while reducing average lagging (LAAL) from 2.8s to 2.3s across 60 languages.
New https://t.c
46.4K views · 758 likes
· 76 reposts · 49 replies
Open on X →
We built Money Agent, a personal finance assistant powered by @Alibaba_Qwen 3.8 27B on Cerebras.
Now you can turn a home-buying question into a real-time conversation, then into a financial goal, faster. https://t.co/VxsTgc9a6I
37.1K views · 194 likes
· 19 reposts · 14 replies
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0:49
🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities!
Native audio-video understanding, reasoning, and tool use come together in one model: understand the content, plan the task, execute with tools, and deliver the result.
Highlights: 🥳
- h
241.3K views · 3.3K likes
· 329 reposts · 142 replies
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Fast meets open. 🚀 Qwen3.8-27B is now running on @cerebras with rapid inference. Try it now!
202.4K views · 1.3K likes
· 60 reposts · 72 replies
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Qwen3.8-27B is now live at Cerebras speed.
The dense, open-weight model from @Alibaba_Qwen scores 34 on the Artificial Analysis Intelligence Index—making it comparable to models such as GPT-5.6 Luna, DeepseekV4 Pro, and Claude Sonnet 4.6. https://t.co/0tkRRn49Ft
284.7K views · 1.4K likes
· 82 reposts · 88 replies
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0:48
🏆 #1 overall on Code Arena, and top of the Pareto frontier at $5/MToken. Thanks! @arena
Give Qwen3.8-Max-0902 a spin on QwenCloud.🥳
54.3K views · 660 likes
· 48 reposts · 43 replies
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@Alibaba_Qwen AI moving so fast that 0902 was released on 0901
18.7K views · 186 likes
· 0 reposts · 12 replies
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Qwen3.8-Max-0902 by @Alibaba_Qwen just debuted at #1 overall in Code Arena: WebDev with 1,691 pts, and became the highest-scoring model on the Pareto frontier at a blended $5/MToken!
On the updated frontier, Qwen3.8-Max-0902 sits above HY4 Preview (1,629 pts at $2.08/MToken) and
112.7K views · 573 likes
· 42 reposts · 24 replies
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🏆 #1 on CodeArena: WebDev leaderboard.
Qwen3.8-Max-0902 jumps from 1669 to 1691, setting a new record for agentic coding (WebDev) workflows, with standout strength in multistep reasoning, tool use, and full app generation.
Thanks for the recognition! @arena
94.4K views · 1.1K likes
· 98 reposts · 67 replies
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CommerceAgentBench starts with real commercial demand, and Qwen3.8-Max delivers the strongest overall performance among open-weight models. Let's test Qwen on your real-world workflows! 🔥
65.3K views · 334 likes
· 23 reposts · 28 replies
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@Alibaba_Qwen Congratulations Qwen team on another amazing open release! 🥳
We are working hard on allowing you all to run Qwen3.8-Flash locally!
https://t.co/vXkjO3W0fj
40K views · 629 likes
· 49 reposts · 32 replies
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With only 6B active parameters, Qwen3.8-Flash-Next-Base tops 8 of 14 benchmarks, including MMLU-Pro, SuperGPQA, BBH and GSM8K. And it remains competitive with Qwen3.7-Plus-Base on the rest. Its 51B N-gram embedding parameters use deterministic lookups, adding no per-token https:/
69.3K views · 320 likes
· 21 reposts · 7 replies
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At a 1M-token context length, QSA’s attention kernel is up to 7.6× faster in prefill and 4.9× faster in decode. With a 90% prefix-cache hit rate, Qwen3.8-Flash-Next delivers 8.6× the prefill throughput of Qwen3.7-Plus. https://t.co/QZ0koCWvQU
38.9K views · 303 likes
· 14 reposts · 2 replies
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Model Architecture
Four core upgrades for maximum capability, efficiency, capacity, and stability:
- Attention: GDN + QSA Hybrid. Gated DeltaNet (GDN) compresses history. Qwen Sparse Attention (QSA) uses a lightweight indexer for micro-block context selection. Lower the cost of
88.9K views · 461 likes
· 43 reposts · 6 replies
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Language performance & Vision Language performance: https://t.co/PkhyKwFmP3
166K views · 620 likes
· 45 reposts · 14 replies
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⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight!
The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens.
125B parameters + 51B N-gram https://
1.9M views · 7.9K likes
· 1K reposts · 448 replies
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เทียบกับบัญชีขนาดเดียวกัน
19 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 100K–1M ผู้ติดตาม แสดงต่อคนมากกว่าบัญชีขนาดเดียวกัน.
ยอดดูมัธยฐาน65 286บัญชีนี้5 983ค่ามัธยฐานของ 100K–1M
การเข้าถึง, %22.81%บัญชีนี้1.49%ค่ามัธยฐานของ 100K–1M
การมีส่วนร่วม, %0.82%บัญชีนี้0.93%ค่ามัธยฐานของ 100K–1M
| ตัวชี้วัด | บัญชีนี้ | ค่ามัธยฐานของ 100K–1M | อัตราส่วน |
|---|---|---|---|
| ยอดดูมัธยฐานต่อโพสต์ | 65 286 | 5 983 | 10.9× |
| การเข้าถึง (ยอดดู ÷ ผู้ติดตาม) | 22.81% | 1.49% | 15.3× |
| อัตราการมีส่วนร่วม | 0.82% | 0.93% | 0.89× |
บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
40K26 Aug
65.3K1 Sep
94.4K2 Sep
112.7K
18.2K
18.7K
7.3K
54.3K
284.7K11 Sep
202.4K12 Sep
241.3K18 Sep
37.1K
46.4K19 Sep
16.6K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
1.80%26 Aug
0.60%1 Sep
1.41%2 Sep
0.58%
1.72%
1.06%
1.61%
1.40%
0.55%11 Sep
0.70%12 Sep
1.62%18 Sep
0.64%
1.95%19 Sep
0.95%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.
What the audience does
Likes71.4%20 550 in total
Reposts7.0%2 001 in total
Replies3.7%1 062 in total
Quotes3.2%916 in total
Bookmarks14.7%4 236 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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