Qwen

Meet Qwen-Image-2.1, the most balanced and cost-effective image generation model in the Qwen-Image series! Now open weights! 🎨 A unified model for both generation and editing, delivering top-tier quality in a lightweight package. Highlights: 👀 - Compact & exceptionally fast: A lightweight 7B architecture that outperforms most closed-source models, with drastically accelerated inference for multi-image inputs. - Native transparency: Natively generates and edits RGBA layers, enabling seamless compositing and text editing within transparent images. - Versatile, high-fidelity editing: Supports up to 10 reference images and precise local control while preserving strict fidelity for portraits and products. - Broad coverage & stunning aesthetics: Excels at panoramas, infographics, and virtual try-ons, delivering realistic textures and elegant typography. Start to create your next masterpiece with Qwen-Image-2.1! 🖼️ - Blog: t.co/tVntKOi7jy - GitHub: t.co/cRj66wCrWr - Model Scope: t.co/64d7Ix6YFR - Hugging Face: t.co/njHSBXUbVS

We're extending FREE Qwen 3.8 Max + Flash for 7 more days! And we've increased rate limits across all 3 models. Built something with Qwen or Wan? Show us what you've got! Tag us and @Alibaba_Qwen or @Alibaba_Wan. We'll pick 3 winners based on: 🏆 Most creative 🏆 Most challenging 🏆 Most effort put in Each winner gets $200 cash + $200 in GMI credits!

Qwen3.8-27B is now live on Nebius Token Factory. A compact 27B dense model for coding, research, and agent workflows, with a focus on planning and completing tasks across multiple steps. Start building: tokenfactory.nebius.com/endpoints?moda…

Qwen-Image-2.1 by @Alibaba_Qwen just landed as the #1 open source model in the Image Edit Arena and Text-to-Image Arena! With 1367 pts in the Image Edit Arena, Qwen-Image-2.1 took the #1 spot among open. It landed #16 overall, just 3 pts from GPT-Image-1.5-high-fidelity at #15. See the leaderboard for the Text-to-Image arena below. Congrats to the @Alibaba_Qwen team on this contribution to the open source ecosystem!

We're excited to offer Day0 OpenVINO support for Qwen-Image-2.1 Read more about what you can accomplish here: ms.spr.ly/6019a5nm5 x.com/17533392773863…

Day-0 support for @Alibaba_Qwen’s Qwen-Image 2.1 is here in SGLang-Diffusion! 🖥️ Native precision on a single RTX 4090 24GB with CPU offload - 1024×1024 generation in 18.7s and image editing in 21.7s with 22.7 GiB peak GPU memory during requests. - On an RTX PRO 6000 96GB: 8.0s generation and 9.6s editing. 🎨 Text-to-image, multi-image editing, and transparent RGBA output—all with one checkpoint. ⚡ Native inference with TP/SP, LoRA, and OpenAI-compatible APIs. 40 denoising steps, one image per request, warmed HTTP latency including PNG output. No quantization. Cookbook and GPU-specific commands below 👇

Qwen Image 2.1 is here! 🖼️ A 7B params native image generation and editing model, with up to 10 image references The model comes with it's own prompt enhancement LLMs, integrated with diffusers 🧨 and ComfyUI ▶️ on Spaces huggingface.co/spaces/hugging…

Qwen-Image-2.1 is now supported in ComfyUI! Open weights. One 7B checkpoint that generates and edits. → Image generation at native 2K → Instruction editing from up to 10 reference images in a single pass → RGBA output, alpha included

Qwen-Image-2.1 from @Alibaba_Qwen has day-0 support in vLLM-Omni. 🎉 Generate, edit, and create transparent images with one model: a 7.1B DiT paired with Qwen3-VL-8B. Setup and supported combinations 👇 recipes.vllm.ai/Qwen/Qwen-Imag… x.com/Alibaba_Qwen/s…

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.

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.

Qwen is up. Come say hi😃 qwencloud.com

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 Qwen3.8-Flash-Next (1,620 pts at $0.39/MToken). With this release, three models at the top of the overall leaderboard have moved off the Pareto frontier despite remaining #2–#4 overall: - Claude Opus 5 (Max): #2, 1,688 pts, $20/MToken - Kimi K3 (Max): #3, 1,674 pts, $12/MToken - Qwen3.8 (Max): #4, 1,669 pts, $5/MToken Congrats to the @Alibaba_Qwen team on the release!

Big news: Qwen3.8-Max-0902 by @Alibaba_Qwen just debuted at #1 overall in the Code Arena: WebDev with 1691 pts! It scores 3 pts above Claude Opus 5 (Max), 17 pts above Kimi K3 (Max), and 22 pts above the previous Qwen3.8-Max. Priced at a blended $5/MToken, Qwen3.8-Max-0902 also claims the highest-scoring position on the Pareto frontier! Stay tuned for a closer look at its Pareto positioning, and for Agent Arena scores coming soon. Its strength carries across every Code Arena: WebDev category: - #1 in Data & Analytics and Consumer Product - #2 in Brand & Marketing, Gaming, and Simulations - #3 in Content Creation Tools and Reference-Based Design Congrats to the @Alibaba_Qwen team on this huge update!

Most AI benchmarks test what a model says. In commerce, the hard part was never the answer. It’s execution. We’ve open-sourced CommerceAgentBench: a benchmark for real commerce operations. Early results are humbling. The best overall completion rate is ~62%. Qwen @Alibaba_Qwen delivered the strongest overall performance across complex commercial workflows among the open-weight models evaluated. Explore the benchmark and full results ↓ t.co/bLM0UFQD3B

Qwen3.8-Flash is now available in OpenCode Go 125B/6B · 1M context · multimodal

Qwen3.8-Flash API is live on QwenCloud. 262K native context, extensible to 1M, and priced to scale. About Price⬇️ Input: $0.15 / 1M tokens Output: $0.47 / 1M tokens Cache hit: $0.016 / 1M tokens Get your API key and start building： qwencloud.com/models/qwen3.8… #QwenCloud #Qwen

Qwen3.8 Flash from @Alibaba_Qwen is now live on OpenRouter. A multimodal reasoning model for coding assistants, agentic workflows, visual understanding, codebase and document analysis, desktop interaction, charts, and long video. openrouter.ai/qwen/qwen3.8-f…

TokenSpeed Day 0 Support for @Alibaba_Qwen 3.8 Flash Next. 🔹GDN + Qwen Sparse Attention hybrid architecture 🔹Gated residual connections 🔹N-gram embedding For N-gram embedding, TokenSpeed also supports FP8 precision. As a preview for Qwen 4, we will continue optimizing beyond Day 0.

Congrats to @Alibaba_Qwen on releasing Qwen3.8-Flash-Next, an experimental open-weight model that previews the Qwen4 architecture. We’ve got Day 0 support to fine-tune with NVIDIA NeMo AutoModel and NeMo RL, plus recipes to run it with @sgl_project, @vllm_project and @lightseekorg TokenSpeed. Get started: t.co/qsFWpRIHEK

Qwen3.8-Flash can now be run locally! 🔥 The 125B MoE model outperforms Claude-Opus-4.6 (Max). Run on 75GB RAM via Unsloth GGUFs. Qwen3.8-Flash-Next enables CPU RAM / unified mem setups to deliver near VRAM speeds. Guide: unsloth.ai/docs/models/qw… GGUF: huggingface.co/unsloth/Qwen3.… x.com/Alibaba_Qwen/s…

⚡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 embeddings, with just 6B activated per token. Unmatched cost-efficiency. What's new: 🥳 - Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4. - Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks. - Strong performance: scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision (with CI). - 262K native context, extensible to 1M with YaRN. We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀 We can't wait to see what you build with Qwen3.8-Flash!👀👇 - Blog: t.co/M5hYypFLgJ - Technical Report: t.co/IF0gObIkQO - Hugging Face: t.co/6ow8QVAABt - ModelScope: t.co/tDOn2jNuFG

Congrats to @Alibaba_Qwen on launching Qwen3.8-Flash! SGLang is proud to be a day-0 partner supporting the new architecture preview for Qwen4. It's a 125B main model with 51B of N-gram embeddings and 6B activated per token. The 51B N-gram embeddings scale model capacity with almost no extra compute per token, and can sit in host memory with async prefetch instead of occupying GPU memory. The GDN + QSA hybrid attention gives you efficient memory and precise retrieval at the same time on long-horizon tasks, while Gated Residual gives the model 4 lanes instead of 1 to pass information between layers. And it's trained with Muon! We're excited for what's next with Qwen4, and we already have plenty of ideas for how to use the N-gram embeddings in new deployment setups. Stay tuned! Blog and cookbook in the comments👇

Qwen3.8-Flash-Next from @Alibaba_Qwen has day-0 support in vLLM, verified on NVIDIA and AMD GPUs. 🎉 Ultra-sparse multimodal MoE: 125B params, 6B active, 262K native, 1M via YaRN. On top of those sits a separate 51B N-gram table you can offload. Most of it will look familiar. The Gated DeltaNet layers reuse the KV path vLLM has had since Qwen3-Next: only a quarter of the layers hold a growing KV cache. Keep the 51B table in host RAM instead of HBM with VLLM_PLE_CPU_OFFLOAD=1. Qwen Sparse Attention is where the new engine work went. For now the model runs from vllm/vllm-openai:qwen38-flash-next. Thanks to @Alibaba_Qwen for the weights, and for opening them this early! 🙌 🔗 t.co/NPnI6LNrGA

@Alibaba_Qwen Congrats on launching Qwen3.8-Flash! SGLang is proud to be a day-0 partner supporting the new architecture preview for Qwen4. docs.sglang.io/cookbook/autor…

Big news: Qwen3.8-27B by @Alibaba_Qwen is now #1 among open models in the Image-to-WebDev Arena (#7 overall)! Priced at $0.40/$3 per M input/output tokens, it has shifted the Pareto frontier (more info below). Qwen3.8-27B has 1574 pts with 27B parameters, achieving performance on par with Kimi K3 (Max) at 2.8T parameters! Congrats to @Alibaba_Qwen on this contribution to the open model ecosystem!

Over the weekend I had Codex parse 500K arXiv AI/ML papers since ChatGPT to understand which open models are used for research. In 2024, ~30% of papers mentioned an American open model and only 10% a Chinese model. Today, ~40% of papers mention a Chinese (open) LLM, and only 25-30% an American one. Chinese models are the default for research. Chinese mentions are still growing while American open models are stagnating. When looking at this data it's important to remember that papers substantially lag model releases, as research takes a long time. Qwen's steady growth is reflective of this, but so is Llama's lasting power. Some more observations: 1. Qwen has been steadily growing, and today 1/3 of papers which mention any LLM mention qwen. OpenAI's closed models are the highest overall, at ~37%. 2. Llama peaked around April of 2025 at 30% of papers which mention any LLM (including ChatGPT etc). Llama 4 was released at about the same time, and Llama has been declining since. 3. Gemini and Claude are less common than the leading open models, mentioned in 10-15% of papers puts them behind all of Qwen, Llama, and DeepSeek. Open models should be and are the foundations of open research. The % of papers mentioning any LLM have been steadily climbing since 2023. | Year | January | April | July | October | | 2023 | 10.43% | 15.39% | 18.69% | 32.18% | | 2024 | 29.70% | 33.93% | 35.70% | 44.25% | | 2025 | 39.23% | 45.28% | 44.94% | 53.52% | | 2026 | 55.49% | 57.26% | 53.14% | TBD Now over 50% of AI papers, from 10% in 2023. Other notes: - Gemma and Mistral hover around 5-10%. - Our beloved fully-open Olmo models have been ~1% since the first release in Jan. 2024. - DeepSeek has a clear jump after R1 in Jan. 2025 - Data derived from the most popular ML arXiv categories: cs. AI, cs. CL, cs. CV, cs. LG, stat. ML Just like our downloads and derivative model data, this is updated daily on the Interconnects Open Model Dashboard.

Exciting news: Qwen3.8-27B by @Alibaba_Qwen just landed in Code Arena: WebDev at #9 overall with 1595 pts. It is the only model in its size class in the top 10, and also reshapes the Pareto Frontier! It is only 6 ranks behind the much larger Qwen3.8-Max. For scale: Gemma 4-31B which was released back in April sits at #80. Congrats to @Alibaba_Qwen on the release! See thread for more highlights on Qwen3.8-27B.

Just pushed DFlash2 (@inco_ai) recipes to the Qwen3.8 27B cookbook⚡️ docs.sglang.io/cookbook/autor… The community has been seeing great results with NVFP4 + DFlash2, and these recipes should be some very good starting points to play with. More Qwen3.8 27B updates on the way 🫡 x.com/sgl_project/st…

We’re releasing new Qwen3.8-27B GGUFs with 10% higher accuracy. Unsloth Dynamic V3 outperforms others by >10% on Div-300, KLD & more benchmarks. We also release 1-bit quants that retain 77% accuracy. Run on 8GB RAM. Blog: unsloth.ai/docs/basics/dy… GGUF: huggingface.co/unsloth/Qwen3.… x.com/UnslothAI/stat…

Qwen3.8-Max landed #4 on Arena's Frontend Code leaderboard — one spot above Claude Fable 5. We ran both on the same 10 UI design prompts, one shot each, to see how close that really is.

On @harvey's Legal Agent, it is tied with Fable 5 at 11.3pts and is ahead of Kimi K3, Qwen 3.8 Max, and DeepSeek V4.

Qwen3.8-27B is now the #1 local model in Cline after just 4 days. This ends a *4 month* streak by the previous winner Qwen2.5-Coder-7B which has been the top local model since April. x.com/cline/status/2…

Here's a comparison i never thought i would make Qwen-3.8-27b vs Gemini-3.7-flash Yes, you read it right! I don't know what to say, this is soo damn impressive. the difference is hugeee🤯 Take a look at this result 😍 (watch it in highest quality) Same prompt, both one shot on web-chat ui Qwen-3.8-27b produces consistent beautiful, visually stunning results. This is one of the best water surface simulations i have seen across every local models i have run and at the same time beating Gemini-3.7-flash result. And this is running on my 3090. And yes its Q5, not even f8.

It's official: Qwen3.8-27B just scored 52 on the @ArtificialAnlys Intelligence Index. We now have an open-weight model that matches GPT-5.6 Luna (max) AND can run locally... even in your browser with custom WebGPU kernels! What a time to be alive! 🤯

Artificial Analysis Intelligence Index puts Qwen3.8-27B at DeepSeek V4-Pro and GPT 5.6 Luna performance. This is the first time a local model has scored frontier model capability. We weren’t expecting this pace of local progress anywhere near this soon.

Run Qwen3.8 27B locally via Atomic Chat💥 We released Atomic Dynamic GGUF quants, from 8-bit (28.9 GB) down to 1-bit (8.5 GB), and measured all other Qwen3.8 GGUFs in the community AD-IQ3_S runs on a 16GB MacBook Air and picks the same next token as the BF16 original 92.4% of the time

Alibaba's open-weight models have accumulated more than 3 billion global downloads in the past six months, eclipsing Meta, Alphabet and domestic peers to become the world’s No. 1 artificial-intelligence model bloomberg.com/news/articles/…

soon 10k

Qwen3.8-27B is here! 🚀 It's a leap in capabilities for a laptop size model. Requires ~17GB to run locally. Model page: lmstudio.ai/qwen/qwen3.8-2… x.com/Alibaba_Qwen/s…

Congratulations to the @Alibaba_Qwen on the launch of Qwen 3.8! MediaTek continues our long-standing collaboration with Qwen, with Day-0 support now available on Dimensity Auto Cockpit C-X1 and the latest Dimensity flagship mobile SoC, bringing smarter and more intuitive on-device AI experiences to more people, from smartphones to vehicles, and helping make agentic AI a seamless part of everyday life.

Ready to run Qwen3.8 locally? 👀 Qwen3.8-27B packs powerful AI into an open model developers can download, serve locally, and build with on their own terms. x.com/17533392773863…

Qwen 3.8 27B is now available on Ollama. It's one of the best open models at this size, and made for agentic tasks and professional work. Try it directly with the apps & harnesses you use: Claude Code: ollama launch claude --model qwen3.8 OpenCode: ollama launch opencode --model qwen3.8 Hermes Agent: ollama launch hermes --model qwen3.8 Pi: ollama launch pi --model qwen3.8 We have also optimizations for Apple Silicon! Try it with the model name: qwen3.8:27b-mlx

The State of Open Models, Summer 2026 ☀️ frontier models are getting larger, but small models still dominate real-world usage. Qwen leads local inference, followed by Gemma. AI agents are becoming a major force on the Hub Full picture on the blog 🤗 huggingface.co/blog/state-of-…

We’re proud to be the Day 0 open-source inference engine partner for @Alibaba_Qwen 3.8. To serve this 2.4T-parameter model across multi-node @NVIDIAAI Blackwell inference, we optimized DP/EP scaling across nodes, delivering 30%+ faster performance than TP16, plus DSpark speculative decoding with single CUDA graph optimization👇 t.co/Ec62BNvDyT

🚀 Day-0 Support! @Alibaba_Qwen has open-sourced Qwen3.8-2.4T-A95B — and it’s now live on SiliconFlow. ⚡ With 2.4T parameters and 95B active, Qwen3.8 is built to take a goal and come back with finished work — coding, researching, planning, and executing along the way. 💸 Per 1M tokens: • Input: $2.00 • Output: $6.00 • Cached input: $0.25 💻 Autonomous coding. 🔬 Deep research. 🤖 End-to-end agent execution. Built for serious workloads — from idea to implementation in one go. Time to cook → t.co/P1QJ3fZQsY 🍳

New on DeepInfra: Qwen3.8-2.4T-A95B 🚀 @Alibaba_Qwen's latest sparse MoE — 2.4T total params, 95B active, 512 experts. Built for coding, agentic workflows, and complex reasoning, with native 262K context. Live now at $2.00/M in · $6.00/M out · $0.20/M cached @Alibaba_Qwen @alibaba_cloud

Qwen3.8 27B brings a new state-of-the-art dense model for local AI development. ⚡ Run it on AMD Ryzen™ AI Max+ processors or single Radeon™ AI PRO R9700 card ⚡ Experience it with @LMStudio ⚡ Turn it into an app with @lemonade_server Start building with AMD Day 0 support for @Alibaba_Qwen now: t.co/EQKk02BmbY

Qwen3.8-27B can now be run locally! ✨ Run on 17GB RAM via Unsloth Dynamic GGUFs. Qwen3.8-27B is by far the strongest model for its size. We also uploaded NVFP4 quants. GGUF: huggingface.co/unsloth/Qwen3.… Guide: unsloth.ai/docs/models/qw… x.com/Alibaba_Qwen/s…

The king of small models is back! Qwen3.8-27B from @Alibaba_Qwen is open source, and Day-0 support is live in SGLang: - 206.1 tok/s decode on a single RTX 5090, with our NVFP4 plus DSpark - 38.28 tok/s decode on DGX Spark Qwen3.8-27B raises the bar again for what a small model can do on agentic planning and long-horizon tasks. Long live the (small model) king! Run it locally with SGLang 👇

🎉 Qwen3.8-27B is here from @Alibaba_Qwen, and the whole thing fits on a single GPU. Same hybrid backbone as the 2.4T flagship, dense instead of MoE. Day-0 support in vLLM. 🚀 What is in it for serving ✨ - Fits one Blackwell GPU in every precision. Qwen ships BF16 and FP8, the NVFP4 build from @inferact - 262K native context, stretching to 1M. At that length one GB300 still has room for roughly 6.6M KV tokens. Six full-length sequences in flight, on one GPU - An MTP draft head rides inside the checkpoint, so speculative decoding needs no separate speculator repo. Acceptance on short prompts measured 92.2% in BF16 and 84.8% in FP8 Verified end-to-end on @NVIDIA GB300: BF16 and FP8 at TP4, NVFP4 at TP1, tool calls working, correct generations at 1M context. Two prerequisites, vLLM nightly and transformers 5.8.0+. 🔗 t.co/gSHtqxi93x

Now available: @Alibaba_Qwen 3.8-2.4T-A95B from @alibaba_cloud on DigitalOcean Serverless Inference via NVIDIA HGX™ B300 GPUs. 🤖 1M context, built for long-horizon coding. 🔗 do.co/45VBAyZ One API, usage-based pricing, no infra to manage.

Qwen3.8-2.4T-A95B by @Alibaba_Qwen and @alibaba_cloud is now available on Modal. Served with a custom DFlash speculator trained on tool-call-heavy data. Full 1M context window.

Qwen3.8-2.4T-A95B is going open weight: 2.4T parameters, with 95B active, and Nebius Token Factory is joining as a Day 0 launch partner. Qwen3.8-2.4T-A95B is coming to Nebius Token Factory Managed Services for dedicated inference. @Alibaba_Qwen @alibaba_cloud

Qwen3.8-2.4T-A95B is now live on Fireworks with Day-0 support! This 2.4T parameter MoE model is built for autonomous agents, heavy coding, large context windows, and is ideal for coding and agentic performance. Start building today: fireworks.ai/models/firewor…

Qwen3.8-2.4T-A95B is live on Together AI. The Qwen team’s new open-weight MoE packs 2.4T total parameters with 95B active, a 1M context window, and strong coding & agent capabilities. Start building: together.ai/models/qwen3-8…

Qwen3.8-2.4T-A95B is now live on Fireworks with Day-0 support! This 2.4T parameter MoE model is built for autonomous agents, heavy coding, large context windows, and is ideal for coding and agentic performance. Start building today: fireworks.ai/models/firewor…

Ready for Qwen Live EP2？We will start at 10:00AM！ UTC+8 x.com/i/broadcasts/1… Qwen Cloud：qwencloud.com/?utm_content=g… #QwenCloud

Qwen Image 3.0 is now on OpenArt ✨ The most Real Qwen image model yet. Native text across 12 languages, precise 10px type, and full interfaces like web pages, games, and livestreams rendered with real world knowledge. Available now on OpenArt: openart.ai/suite/create-i…

Qwen 3.8 Max nearly doubled its score on Legal Research Bench in under three months, climbing from #22 to #4.