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Qwen Profile picture
@Alibaba_Qwen
Nov 3 • 2 tweets • 2 min read • Read on X
We’ve released an early preview of Qwen3-Max-Thinking—an intermediate checkpoint still in training.

Even at this stage, when augmented with tool use and scaled test-time compute, it achieves 100% on challenging reasoning benchmarks like AIME 2025 and HMMT.

You can try the current version in Qwen Chat and Alibaba Cloud API—more to come as training continues.

Qwen Chat: chat.qwen.ai/?thinking=true

Alibaba Cloud API （enable_thinking=True）: modelstudio.console.alibabacloud.com/?tab=doc#/doc/…Image
API usage for Qwen3-Max-Thinking-Preview:
Model name: qwen3-max-preview
Parameter setting: enable_thinking=True Image

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More from @Alibaba_Qwen

Qwen Profile picture
Oct 4
🚀 Qwen3-VL-30B-A3B-Instruct & Thinking are here!
Smaller size, same powerhouse performance 💪—packed with all the capabilities of Qwen3-VL!

🔧 With just 3B active params, it’s rivaling GPT-5-Mini & Claude4-Sonnet — and often beating them across STEM, VQA, OCR, Video, Agent tasks, and more.

And that’s not all: we’re also releasing an FP8 version, plus the FP8 of the massive Qwen3-VL-235B-A22B!

Try it out and make your multimodal AI applications run faster!🧠🖼️

Qwen Chat:   chat.qwen.ai/?models=qwen3-…
Github&Cookbooks：   github.com/QwenLM/Qwen3-V…
API:   alibabacloud.com/help/en/model-…
Blog：  qwen.ai/blog?id=99f033…
ModelScope:   modelscope.cn/collections/Qw…
HuggingFace:   huggingface.co/collections/Qw…Image
Performance of Qwen3-VL-30B-A3B-Thinking Image
Pure text performance Image
Read 4 tweets
Qwen Profile picture
Sep 22
🚀 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-…
💻 GitHub: github.com/QwenLM/Qwen3-O…
🤗 HF Models: huggingface.co/collections/Qw…
🤖 MS Models:
modelscope.cn/collections/Qw…
🎬 Demo: huggingface.co/spaces/Qwen/Qw…Image
Use the voice chat and video chat features on Qwen Chat to experience the Qwen3-Omni model.
Performance Image
Read 4 tweets
Qwen Profile picture
Sep 11
🚀 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
Blog: qwen.ai/blog?id=4074cc…
Huggingface: huggingface.co/collections/Qw…
ModelScope: modelscope.cn/collections/Qw…
Kaggle: kaggle.com/models/qwen-lm…
Alibaba Cloud API: alibabacloud.com/help/en/model-…Image
Pretraining Efficiency & Inference Speed Image
Prefill Stage: At 4K context length, throughput is nearly 7x higher than Qwen3-32B. Beyond 32K, it’s over 10x faster. Image
Read 9 tweets
Qwen Profile picture
Aug 18
🚀 Excited to introduce Qwen-Image-Edit!
Built on 20B Qwen-Image, it brings precise bilingual text editing (Chinese & English) while preserving style, and supports both semantic and appearance-level editing.

✨ Key Features
✅ Accurate text editing with bilingual support
✅ High-level semantic editing (e.g. object rotation, IP creation)
✅ Low-level appearance editing (e.g. addition/delete/insert)

Try it now: chat.qwen.ai/?inputFeature=…
Hugging Face: huggingface.co/Qwen/Qwen-Imag…
ModelScope: modelscope.cn/models/Qwen/Qw…
Blog: qwenlm.github.io/blog/qwen-imag…
Github: github.com/QwenLM/Qwen-Im…
API: alibabacloud.com/help/en/model-…
Image
Image
Read 16 tweets
Qwen Profile picture
May 9
🚀 One line. A full webpage. No hassle.

Introducing Web Dev – the ultimate tool for building stunning frontend webpages & apps using simple prompts in Qwen Chat.

🎨 Just say, "create a twitter website" — and boom! Instant code, ready to go.

No coding required. Just your imagination.

👉🏻 chat.qwen.ai/?inputFeature=…Image
Make an interesting animate
Create a semantic "Contact Support" form with fields for the user's name, email, issue type, and message. Arrange the form elements vertically within a card. Image
Read 6 tweets
Qwen Profile picture
Apr 28
Introducing Qwen3!

We release and open-weight Qwen3, our latest large language models, including 2 MoE models and 6 dense models, ranging from 0.6B to 235B. Our flagship model, Qwen3-235B-A22B, achieves competitive results in benchmark evaluations of coding, math, general capabilities, etc., when compared to other top-tier models such as DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro. Additionally, the small MoE model, Qwen3-30B-A3B, outcompetes QwQ-32B with 10 times of activated parameters, and even a tiny model like Qwen3-4B can rival the performance of Qwen2.5-72B-Instruct.

For more information, feel free to try them out in Qwen Chat Web (chat.qwen.ai) and APP and visit our GitHub, HF, ModelScope, etc.

Blog: qwenlm.github.io/blog/qwen3/
GitHub: github.com/QwenLM/Qwen3
Hugging Face: huggingface.co/collections/Qw…
ModelScope: modelscope.cn/collections/Qw…

The post-trained models, such as Qwen3-30B-A3B, along with their pre-trained counterparts (e.g., Qwen3-30B-A3B-Base), are now available on platforms like Hugging Face, ModelScope, and Kaggle. For deployment, we recommend using frameworks like SGLang and vLLM. For local usage, tools such as Ollama, LMStudio, MLX, llama.cpp, and KTransformers are highly recommended. These options ensure that users can easily integrate Qwen3 into their workflows, whether in research, development, or production environments.

Hope you enjoy our new models!Image
Image
Qwen3 exhibits scalable and smooth performance improvements that are directly correlated with the computational reasoning budget allocated. This design enables users to configure task-specific budgets with greater ease, achieving a more optimal balance between cost efficiency and inference quality.Image
Qwen3 models are supporting 119 languages and dialects. This extensive multilingual capability opens up new possibilities for international applications, enabling users worldwide to benefit from the power of these models. Image
Read 5 tweets

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