50 Best Tweets About Qwen (2026)

Discover the best tweets about Qwen, including Alibaba model releases, coding, reasoning, benchmarks, fine-tuning, and local deployment.

Model-specific Qwen research, releases, benchmarks, coding performance, deployment, and comparisons with concrete evidence.

Creators
43
Updated

What 50 top Qwen posts reveal

The supplied Qwen discussion is 82% supportive and is concentrated in agentic coding (50% of tweets), benchmarks and comparisons (38%), and local deployment (34%). Official release posts foreground agentic and multimodal capabilities, while local-use posts provide concrete deployment claims. Counterpoints in the supplied posts include a reported benchmark gap and a tool-call test where Qwen underperformed xLAM in that setup. [2084100707423289643, 2031008078850924840, 1952469728410390593]

Dominant tone
Positive

82% of posts

Median score
21.1

All-time engagement

Leading format
Announcement

58% of posts

Recent posts
26%

Published in 90 days

Conversation map

The themes creators return to

Benchmarks and model comparisons

Benchmark scores and head-to-head comparisons with Claude, GPT, Gemini, Kimi, Fable, Gemma, and other models across coding, reasoning, arenas, and agent tests.

38%

Efficiency, compression, and cost

Efficiency claims around small models, sparse MoE active parameters, compression, hardware requirements, inference speed, token cost, and cost-performance tradeoffs.

30%

Multimodal and long-context capabilities

Qwen's multimodal capabilities across vision, audio, speech, video, image generation, long context, and full-modality interaction.

28%

Robotics and vision-language-action models

Qwen-VLA and embodied AI applications for robot manipulation, navigation, trajectory prediction, and cross-embodiment control.

4%

2 posts Median score 76.5 View evidence 1 View evidence 2

Tone and stance

Sentiment Positive leads
Author posture Supportive leads

Performance benchmark

Median likes
113
Median reposts
12
Median replies
9
Median views
12.9K

Posts with media make up 78% of this collection. Their median all-time score is 19.3, compared with 25.0 for text-only posts.

Format mix

  • Announcement 58% · score 22.2
  • Opinion 28% · score 20.0
  • Case Study 12% · score 5.32
  • Tutorial 2% · score 2197.1

Where creators agree, and where they do not

Shared view

Agents anchor the flagship story

Official Qwen announcements emphasize agentic coding and multimodality. Qwen3.8-Max is presented as supporting autonomous development and long-horizon work, while Qwen3.6-Plus is presented with agentic coding, vision, and a 1M-token API context window.

Shared view

Local operation spans small to huge models

Posts describe local Qwen use across a wide size range: a Qwen3.5 local-agent guide for systems with 24GB RAM or less; a Qwen3.5 27B derivative described as runnable in 16GB at 4-bit; Qwen3.5 Small models running locally in a WebGPU browser setup; and a Qwen3.5 397B model reported running on a 48GB MacBook at 4.4 tokens per second.

Shared view

Sparse MoE drives efficiency discussion

Efficiency discussion centers on Qwen3.6-35B-A3B’s sparse MoE configuration: 35B total parameters and 3B active parameters. A comparison post cites 73.4% on SWE-Bench Verified for that model, versus 87.6% for Opus 4.7 in the same post.

Open debate

Local value does not erase trade-offs

Local Qwen is not presented as universally superior. One post prioritizes privacy and fixed hardware cost while calling Opus smarter; another explicitly notes a benchmark gap; and one tool-call experiment reports Qwen slower and less successful than xLAM in that specific Asana-task test.

Open debate

Comparisons depend on evaluation target

Comparative positioning varies by task and source. One post reports slight Gemma edges in knowledge, reasoning, and coding while favoring Qwen for agentic work; other posts report Qwen3.6-Plus benchmark results or a Design Arena placement.

Patterns behind standout posts

Local deployment showed strong thematic performance

Local deployment and edge inference has a supplied median all-time score of 64.422, above model efficiency’s 58.14 but below training/distillation’s 137.7. Its high-scoring examples include concrete hardware or deployment details.

Tutorials outperformed their small share

Announcements account for 58% of the supplied set, compared with 28% opinions, 12% case studies, and 2% tutorials. The single tutorial category has the highest supplied median all-time score, 2197.136.

Statistical standouts

  1. View standout post 1 Score 2482.6 · 117.71× median
  2. View standout post 2 Score 2197.1 · 104.18× median
  3. View standout post 3 Score 896.2 · 42.5× median
  4. View standout post 4 Score 749.4 · 35.53× median
  5. View standout post 5 Score 565.9 · 26.83× median

Who shapes this conversation

The five most represented creators account for 20% of the selected posts.

  1. 1. Qwen

    @Alibaba_Qwen

    2 posts

  2. 2. Arbos

    @arbos_born

    2 posts

  3. 3. Okara

    @askOkara

    2 posts

  4. 4. daily.dev

    @dailydotdev

    2 posts

  5. 5. ℏεsam

    @Hesamation

    2 posts

  6. 6. Mark Kretschmann

    @mark_k

    2 posts

Official posts frame the product roadmap

The official Qwen account’s two evidenced posts present Qwen3.6-Plus and Qwen3.8-Max around agentic coding, multimodality, long context, and additional open-weight releases.

Builders extend Qwen beyond releases

Developer posts focus on implementation: local agentic coding, Mac-native fine-tuning for text, vision, speech-to-text, and text-to-speech, plus planned community compression work for Qwen variants.

Comparison posts foreground coding efficiency

Hesamation’s two posts are comparison-led: one highlights a locally runnable Qwen3.5 derivative and its reported SWE-Bench result, while the other contrasts Qwen3.6-35B-A3B’s 3B active parameters and SWE-Bench score with Opus 4.7.

How this analysis was made

Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.

Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.

This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.

Top Qwen tweets from 43 creators

Ranked 01–50

  1. 01

    @Alibaba_Qwen ·

    📢Meet Qwen3.8-Max — our most capable model to date. Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉 Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters: - Autonomous coding: 10+ days of

    • 1.1K Replies
    • 2.7K Reposts
    • 21.9K Likes
    • 6.2M Views
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  2. 02

    @UnslothAI ·

    Learn how to run Qwen3.5 locally using Claude Code. Our guide shows you how to run Qwen3.5 on your server for local agentic coding. We then build a Qwen 3.5 agent that autonomously fine-tunes models using Unsloth. Works on 24GB RAM or less. Guide: https://t.co/JDPtuIJAZC

    • 98 Replies
    • 357 Reposts
    • 2.9K Likes
    • 229.1K Views
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  3. 03

    @Hesamation ·

    this model is an agentic treasure. it has been #1 trending for 3 weeks on @huggingface as mentioned by @danielhanchen. it's Qwen 3.5 27B fine-tuned on Opus 4.6 distilled data and beats Sonnet 4.5 on SWE-bench verified and more. "Runs locally on 16GB in 4-bit or 32GB in 8-bit."

    • 44 Replies
    • 124 Reposts
    • 1.6K Likes
    • 87.9K Views
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  4. 04

    @Alibaba_Qwen ·

    (1/8)🚀 Introducing Qwen3.6-Plus: Towards Real-World Agents! 🤖 Today, we’re thrilled to drop a major milestone in our journey toward native multimodal agents. Here is what makes Qwen3.6-Plus a game-changer: 💻 Next-level Agentic Coding: Smarter, faster execution. 👁️ Enhanced

    • 238 Replies
    • 658 Reposts
    • 5K Likes
    • 1M Views
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  5. 05

    @_ARahim_ ·

    The entire Qwen stack is now fine-tunable on your Mac! 🍏 Just pushed the latest release, adding Qwen3-TTS to mlx-tune. You can now natively train: ✅ Qwen3.5 (Text) ✅ Qwen3.5 (Vision) ✅ Qwen3-ASR (Speech → Text) ✅ Qwen3-TTS (Text → Speech) One consistent API pattern for all

    • 17 Replies
    • 87 Reposts
    • 692 Likes
    • 55.7K Views
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  6. 06

    @xenovacom ·

    NEW: Alibaba just released Qwen 3.5 Small — a family of powerful multimodal models available in a range of sizes (0.8B, 2B, 4B, and 9B parameters). Perfect for on-device applications! They can even run 100% locally in your browser on WebGPU, powered by Transformers.js! 🤯

    Video thumbnail from Xenova's post Watch video
    • 21 Replies
    • 119 Reposts
    • 1.1K Likes
    • 102.1K Views
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  7. 07

    @AlexFinn ·

    You're right, local models aren't as good as cloud models That's not the point though The point is to have free, private intelligence that can do work for you 24/7 around the clock I have a 3 local models scraping Reddit, product hunt, and other sites 24/7 Looking for

    • 177 Replies
    • 78 Reposts
    • 1.3K Likes
    • 80.8K Views
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  8. 08

    @Hesamation ·

    > Opus 4.7 is a ~5T model > Qwen 3.6 uses 3B for inference SWE Bench verified: > Opus 4.7: 87.6% > Qwen3.6-35B-A3B: 73.4% No rate limits. Free to run. The benchmarks don’t hold much, and there is a gap, but man this is impressive.

    • 52 Replies
    • 58 Reposts
    • 1.6K Likes
    • 64.7K Views
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  9. 09

    @Saboo_Shubham_ ·

    You can now run Qwen 3.5 397B parameter model on your MacBook. 48GB RAM. Pure C. Hand-tuned Metal shaders. No Python, no frameworks. 4.4 tok/s. Built in 24 hours. Human + AI Agent pair programming. 90+ experiments.

    • 58 Replies
    • 83 Reposts
    • 666 Likes
    • 62.9K Views
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  10. 10

    @0xSero ·

    Qwen3.5, MiniMax-M2.7 are incredible acts of kindness that I don't think will be with us from so much longer. Here's my update for you. > I have 20 GPUs at full utilisation right now. All these getting cooooompressed, no synthetic data All runs will be done in 9 days, if I

    • 32 Replies
    • 19 Reposts
    • 576 Likes
    • 19.7K Views
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  11. 11

    @askOkara ·

    holy shit! qwen launched a 9b model that beats gpt-oss-120b a model almost 10x its size. small models are getting scary good

    • 21 Replies
    • 40 Reposts
    • 802 Likes
    • 54.7K Views
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  12. 12

    @robotsdigest ·

    Qwen-VLA feels like one of the first real robotics foundation models. A single system trained across robot manipulation, navigation, egocentric human video, simulation, and vision-language reasoning instead of isolated robot policies.

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    • 2 Replies
    • 32 Reposts
    • 176 Likes
    • 14.5K Views
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  13. 13

    @arbos_born ·

    Apple showed a model can teach itself to code better without any teacher, verifier, or RL. Just fine-tune on its own best outputs. Qwen3-30B went from 42.4% to 55.3% pass rate on LiveCodeBench. Works at 4B, 8B, and 30B scale. The mechanism: reshaping how the model spreads

    • 7 Replies
    • 13 Reposts
    • 124 Likes
    • 10K Views
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  14. 14

    @TheAhmadOsman ·

    Been playing with @PrismML's new model that turned Qwen 3.5 27B into a sub-4GB and sub-6GB weights and I am impressed Cannot believe how far Opensource and Local AI have come since Christmas (~8 months ago)

    • 20 Replies
    • 10 Reposts
    • 298 Likes
    • 12.6K Views
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  15. 15

    @VaibhavSisinty ·

    What's happening in AI right now is genuinely hard to process. A 3 billion parameter model is matching models that are 200 to 300 times larger. On math. On coding. On reasoning. And beating some of them. It's called VibeThinker-3B. Built by Sina Weibo's team on a tiny Qwen 3B

    • 8 Replies
    • 24 Reposts
    • 143 Likes
    • 14.1K Views
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  16. 16

    @arbos_born ·

    The #1 trending model on HuggingFace for three weeks: one researcher distilled Claude's reasoning into Qwen3.5-27B. People are running frontier-level reasoning locally. Now imagine that process as a competition instead of a solo project. Dozens of miners, each trying a different

    • 5 Replies
    • 16 Reposts
    • 125 Likes
    • 13.1K Views
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  17. 17

    @goyalshaliniuk ·

    Another major LLM company has entered the Physical AI race Alibaba’s Qwen team just released Qwen-VLA, a unified Vision-Language-Action model that can control humanoid robots and integrates manipulation, navigation, trajectory prediction, and cross-embodiment control

    Video thumbnail from Shalini Goyal's post Watch video
    • 18 Replies
    • 19 Reposts
    • 66 Likes
    • 790 Views
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  18. 18

    @natolambert ·

    New report with @xeophon is out with the latest open model adoption data we have gathered for Interconnects & The ATOM Project. At the surface level, we can see Chinese models continuing to accelerate in adoption. The report details much more. 1. We manually curate ~1.5K of the

    • 6 Replies
    • 22 Reposts
    • 156 Likes
    • 18.6K Views
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  19. 19

    @DesignArena ·

    BREAKING: Qwen 3.6 Plus by @Alibaba_Qwen is #11 overall on Design Arena with an Elo of 1307 This is in the same performance band as Kimi K2.5 Thinking by @Kimi_Moonshot and Claude Opus 4.5 by @AnthropicAI Congrats to the @Alibaba_Qwen team on the launch!

    • 4 Replies
    • 10 Reposts
    • 159 Likes
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  20. 20

    @tinkerapi ·

    Four Qwen 3.5 models from @Alibaba_Qwen are now live on Tinker. Qwen 3.5 introduces hybrid linear attention that enables long context windows, as well as native vision input.

    • 10 Replies
    • 25 Reposts
    • 375 Likes
    • 238.6K Views
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  21. 21

    @fahdmirza ·

    💥 Qwen3.6-Plus just DROPPED ♠ and it's built for real-world autonomous agents 🔹1M token context window out of the box 🔹Tops benchmarks in agentic coding, tool use & long-horizon planning 🔹New preserve_thinking API keeps reasoning alive across multi-turn agent tasks 🔹Works

    • 9 Replies
    • 10 Reposts
    • 90 Likes
    • 7.2K Views
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  22. 22

    @alex_prompter ·

    Alibaba just introduced the Qwen 3.5 Small Model Series. Four models. 0.8B to 9B parameters. Natively multimodal. Built for edge devices, mobile, and real-world deployment. More intelligence, less compute. Here's what this release actually means:

    • 6 Replies
    • 8 Reposts
    • 120 Likes
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  23. 23

    @Ubermenscchh ·

    breaking.. alibaba mass dropped qwen 3.6-plus and it's embarrassing every frontier model right now 61.6 on terminal-bench (beats claude 4.5 opus) 56.6 on swe-bench pro (1st place) 80.9 on multilingual agentic coding (1st place) 58.7 on claw-eval real world agent (1st place)

    • 35 Replies
    • 71 Reposts
    • 193 Likes
    • 60.1K Views
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  24. 24

    @manishkumar_dev ·

    @AlibabaGroup just released Qwen 3.6 Plus, and this feels like a real step toward AI that actually executes work. After testing it hands on, this is not just another model update. It is built around agentic coding, multimodal reasoning, and full workflow execution. Here is

    • 35 Replies
    • 41 Reposts
    • 116 Likes
    • 19K Views
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  25. 25

    @andonlabs ·

    Qwen 3.6 Plus is 6th on Vending-Bench 2. > The best Chinese model > An astonishing improvement over the previous version (Qwen 3.5 Plus went bankrupt) > Chinese models are now 142 days behind the west > Pareto Optimal for Cost vs. Score

    • 5 Replies
    • 14 Reposts
    • 185 Likes
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  26. 26

    @ai_for_success ·

    Qwen has released Qwen3.6-35B-A3B, a sparse Mixture-of-Experts model that is now open-source under the Apache 2.0 license. TLDR - Sparse MoE architecture with 35B total and 3B active parameters - Performance in agentic coding rivals models 10x its active size - Strong multimodal

    • 5 Replies
    • 12 Reposts
    • 98 Likes
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  27. 27

    @mark_k ·

    Alibaba just dropped Qwen3.8-Max, their most capable model yet. 2.4 trillion parameters (95B active), built on the Qwen 3.5 architecture, with a 1M token context window and native multimodal support. This is the first time they’re open-sourcing weights of a Qwen-Max-class model

    • 12 Replies
    • 4 Reposts
    • 109 Likes
    • 4.7K Views
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  28. 28

    @FellMentKE ·

    🚨 BREAKING: Alibaba unleashes Qwen3.5-Omni, a new frontier in Full-Modality AI. 🤯 Matching the latest Gemini-3.1 Pro in A/V understanding & surpassing it in Audio tasks, this model introduces Audio-Visual Vibe Coding—turning whiteboard sketch videos or game clips directly into

    • 19 Replies
    • 88 Reposts
    • 88 Likes
    • 91.6K Views
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  29. 29

    @pukerrainbrow ·

    You can now build a 3D game in a single AI generation. Most models struggle to coordinate physics, rendering, and logic all at once, but QWEN 3.6-Plus by Alibaba is making it as easy as typing a sentence. It features a massive 1 million token context window (about 750,000

    Video thumbnail from Pukerainbow 🤮🌈's post Watch video
    • 16 Replies
    • 12 Reposts
    • 82 Likes
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  30. 30

    @AdinaYakup ·

    Qwen @Alibaba_Qwen just released Qwen3.5-Omni 🔥 Weights are not released ( yet?), but you can try the demos: ✨ Online demo https://t.co/3lJAahMmdD ✨ Offline demo https://t.co/4hYXy2QXWB

    • 2 Replies
    • 7 Reposts
    • 69 Likes
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  31. 31

    @pankajkumar_dev ·

    Qwen 3.8 Max Leaks - Qwen3.8-Max-Preview is now officially available on Alibaba's Token Plan, Qoder, and QoderWork. - The model features 2.4T parameters - Qwen claims it's "second only to Fable 5." - The model previously on LMArena under the stealth name "Kaleb". - It is very

    • 10 Replies
    • 12 Reposts
    • 150 Likes
    • 22K Views
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  32. 32

    @mark_k ·

    Qwen-Image 3.0 just dropped from @Alibaba_Qwen! For a proper stress test, I created a special test image featuring a complex, highly detailed scene packed with people, objects, text, reflections, and challenging lighting. Let’s see how well the new model handles it. 👇

    • 9 Replies
    • 5 Reposts
    • 68 Likes
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  33. 33

    @arena ·

    Qwen 3.5 Max Preview has landed in top 10 for Arena Expert and top 15 for Text Arena. It shows particular strength in Math. Highlights: - #3 Math - #10 Expert - #15 Text Arena - Top 20 for Writing, Literature & Language, Life, Physical, & Social Science, Entertainment, Sports,

    • 12 Replies
    • 14 Reposts
    • 270 Likes
    • 167.8K Views
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  34. 34

    @askOkara ·

    qwen 3.5 model series is out! > native multimodal > comes in 0.8b, 2b, 4b and 9b > 262k context extendable to 1m > 9b outperforms gpt-oss-120b on various benchmarks while being 13x smaller alibaba cooked 🔥

    • 1 Replies
    • 1 Reposts
    • 65 Likes
    • 2.5K Views
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  35. 35

    @TeksEdge ·

    🏆️ Gemma 4 vs Qwen 3.5 and the battle for local AI is on 🔥 💥 X is full of people running both on consumer GPUs, Mac Studios, RTX 6Ks and H100s. The day-0 benchmarks tell a baseline story: 🧠 KNOWLEDGE & REASONING, Gemma leads slightly 💻 CODING, Gemma has a small edge 🤖 AGENTIC &

    • 5 Replies
    • 2 Reposts
    • 36 Likes
    • 2.6K Views
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  36. 36

    @ttunguz ·

    2025 is the year of agents, & the key capability of agents is calling tools. When using Claude Code, I can tell the AI to sift through a newsletter, find all the links to startups, verify they exist in our CRM, with a single command. This might involve two or three different

    • 5 Replies
    • 2 Reposts
    • 32 Likes
    • 5.5K Views
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  37. 37

    @cjzafir ·

    Is GPT 5.4 really good? I used codex-5.4-extra-high to fine-tune qwen-3.5-4b. (SFT) (Exhausted all my pro plan weekly credits in 24 hours.) And also used opus-4.6 to fine-tune qwen-3.5-9b Codex is fast but dataset quality is crap. Opus is slow but data quality is great.

    • 5 Replies
    • 0 Reposts
    • 31 Likes
    • 3.6K Views
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  38. 38

    @testingcatalog ·

    Alibaba released Qwen 3.6 Plus, an upgraded agentic model with coding and vision capabilities. Qwen 3.6 Plus comes with a 1M context window and is already available on Qwen Chat.

    • 3 Replies
    • 2 Reposts
    • 28 Likes
    • 2.4K Views
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  39. 39

    @Amank1412 ·

    this is kinda wild claude opus 4.6 runs with 200b active params and hits about 75% on swe bench verified qwen 3.6 35b a3b? only 3b active params still pulling 73.4% same benchmark, almost the same score but qwen’s doing it with 60x less compute efficiency gap is getting scary

    • 4 Replies
    • 1 Reposts
    • 19 Likes
    • 2K Views
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  40. 40

    @RoundtableSpace ·

    Qwen 3.8 Max just dropped with 2.4 trillion parameters, beats Fable 5 on several benchmarks and ran a GitHub repo autonomously for 10 days straight.

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    • 11 Replies
    • 1 Reposts
    • 67 Likes
    • 45.4K Views
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  41. 41

    @boxmining ·

    Qwen 3.6 Plus being free right now is actually pretty interesting. Tried @Alibaba_Qwen inside Hermes Agent for system audits and log analysis, and it surfaced issues I would’ve probably missed. The best part is the think blocks. Seeing how it reasons through errors and tool

    • 5 Replies
    • 3 Reposts
    • 22 Likes
    • 2.3K Views
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  42. 42

    @RoundtableSpace ·

    Kimi K3 vs Qwen 3.8 Max on the same landing page prompt. Qwen was 3x faster and still built a convincing 3D guillotine model.

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  43. 43

    @dino11 ·

    Alibaba $BABA just open-sourced Qwen 3.5 — a 9B parameter AI model that runs on your laptop. The benchmarks are insane: → Beats OpenAI's GPT-OSS-120B (a model 13x its size) on reasoning → GPQA Diamond: 81.7 vs 71.5 → 30-50 tokens/sec on a standard laptop → The 2B version runs

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  44. 44

    @ValsAI ·

    We evaluated @Alibaba_Qwen's Qwen 3.5 Flash on our remaining benchmarks. The model places in the top 10 on several benchmarks, including MortgageTax, LegalBench, and MMMU.

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  45. 45

    @TheGeorgePu ·

    Alibaba's Qwen3.7-Max just hit #2 on Code Arena. Above GPT-5.5. Above Gemini. Behind only Claude. Closed weights. API only. The 'China wins via open source' story is over. DeepSeek opened the model. Qwen closed it. Both work. The catch-up player stopped sharing. They

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  46. 46

    @Sino_Market ·

    Qwen Unveils CoPaw 1.0 Personal AI Assistant with Enhanced Models, Security and Multi-Agent Capabilities Qwen: We release CoPaw 1.0 today, a personal intelligent assistant that can be quickly deployed in users' local or cloud environments. We upgrade CoPaw's capabilities around

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  47. 47

    @agenticgirl ·

    A smaller model just outperformed the biggest ones. Qwen 3.6-Plus scored 61.6 on Terminal-Bench and 57.1 on SWE-Bench. That puts it ahead of Claude Opus 4.5, Kimi K2.5, and Gemini 3 Pro. Models that are much larger and far more expensive to run. For the past year everyone

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  48. 48

    @dailydotdev ·

    Shipped a model, forgot to mention whose model it was. Here's today's AI dev news. - @cursor_ai launched a coding model on Kimi k2.5 without saying so, co-founder later called it a mistake - Claude Code's skills ecosystem has a supply chain problem: 27% of public skills carry

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  49. 49

    @JulianGoldieSEO ·

    Qwen 3.8 vs Fable 5: 50 real builds, one winner. This guy literally built 50 games with both AIs to find out. Qwen scored 86.6 on Terminal Bench. Fable 5 scored lower. Qwen's games ran smooth and full 3D. Fable 5 got buggy on half the builds. But Fable 5 won on flight sims

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  50. 50

    @dailydotdev ·

    Agents return videos now. Here's what else landed today. - @cursor_ai agents record their own test runs and hand you a video, not a wall of diff - @github Copilot added an hourly cap on top of the monthly one, and the Claude Code comparisons are already flying - @Alibaba_Qwen's

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