Qwen3.7 Preview lands on Arena (1 minute read)

# Thread by @Alibaba_Qwen on Thread Reader App Source: [https://threadreaderapp.com/thread/2056403591464984753.html](https://threadreaderapp.com/thread/2056403591464984753.html) ## More from @Alibaba\_Qwen [](https://threadreaderapp.com/user/Alibaba_Qwen) Sep 22, 2025 🚀 Introducing Qwen3\-Omni — the first natively end\-to\-end omni\-modal AI unifying text, image, audio & video in one model — no modality trade\-offs\! 🏆 SOTA on 22/36 audio & AV benchmarks 🌍 119L text / 19L speech in / 10L speech out ⚡ 211ms latency \| 🎧 30\-min audio understanding 🎨 Fully customizable via system prompts 🔗 Built\-in tool calling 🎤 Open\-source Captioner model \(low\-hallucination\!\) 🌟 What’s Open\-Sourced? We’ve open\-sourced Qwen3\-Omni\-30B\-A3B\-Instruct, Qwen3\-Omni\-30B\-A3B\-Thinking, and Qwen3\-Omni\-30B\-A3B\-Captioner, to empower developers to explore a variety of applications from instruction\-following to creative tasks\. Try it now 👇 💬 Qwen Chat:[chat\.qwen\.ai/?models=qwen3\-…](https://chat.qwen.ai/?models=qwen3-omni-flash) 💻 GitHub:[github\.com/QwenLM/Qwen3\-O…](https://github.com/QwenLM/Qwen3-Omni) 🤗 HF Models:[huggingface\.co/collections/Qw…](https://huggingface.co/collections/Qwen/qwen3-omni-68d100a86cd0906843ceccbe) 🤖 MS Models: [modelscope\.cn/collections/Qw…](https://modelscope.cn/collections/Qwen3-Omni-867aef131e7d4f) 🎬 Demo:[huggingface\.co/spaces/Qwen/Qw…](https://huggingface.co/spaces/Qwen/Qwen3-Omni-Demo)[](https://pbs.twimg.com/media/G1d9f1ZawAA0DRH.jpg) Use the voice chat and video chat features on Qwen Chat to experience the Qwen3\-Omni model\. Performance[](https://pbs.twimg.com/media/G1eAq2KaoAI2beT.jpg) Read 4 tweets [](https://threadreaderapp.com/user/Alibaba_Qwen) Sep 11, 2025 🚀 Introducing Qwen3\-Next\-80B\-A3B — the FUTURE of efficient LLMs is here\! 🔹 80B params, but only 3B activated per token → 10x cheaper training, 10x faster inference than Qwen3\-32B\.\(esp\. @ 32K\+ context\!\) 🔹Hybrid Architecture: Gated DeltaNet \+ Gated Attention → best of speed & recall 🔹 Ultra\-sparse MoE: 512 experts, 10 routed \+ 1 shared 🔹 Multi\-Token Prediction → turbo\-charged speculative decoding 🔹 Beats Qwen3\-32B in perf, rivals Qwen3\-235B in reasoning & long\-context 🧠 Qwen3\-Next\-80B\-A3B\-Instruct approaches our 235B flagship\. 🧠 Qwen3\-Next\-80B\-A3B\-Thinking outperforms Gemini\-2\.5\-Flash\-Thinking\. Try it now:[chat\.qwen\.ai](https://chat.qwen.ai/) Blog:[qwen\.ai/blog?id=4074cc…](https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list) Huggingface:[huggingface\.co/collections/Qw…](https://huggingface.co/collections/Qwen/qwen3-next-68c25fd6838e585db8eeea9d) ModelScope:[modelscope\.cn/collections/Qw…](https://modelscope.cn/collections/Qwen3-Next-c314f23bd0264a) Kaggle:[kaggle\.com/models/qwen\-lm…](https://www.kaggle.com/models/qwen-lm/qwen3-next-80b) Alibaba Cloud API:[alibabacloud\.com/help/en/model\-…](https://www.alibabacloud.com/help/en/model-studio/models#c5414da58bjgj)[](https://pbs.twimg.com/media/G0lVrz1aMAMqnVP.jpg) Pretraining Efficiency & Inference Speed[](https://pbs.twimg.com/media/G0ldAWZakAAGBdx.jpg) Prefill Stage: At 4K context length, throughput is nearly 7x higher than Qwen3\-32B\. Beyond 32K, it’s over 10x faster\.[](https://pbs.twimg.com/media/G0ldN9sbAAAwSft.jpg) Read 9 tweets