Alibaba releases open-source reasoning model QwQ-32B on Hugging Face and ModelScope, claiming comparable performance to DeepSeek-R1 but with lower compute needs
Introduction QwQ is the reasoning model of the Qwen series. Paul Barker / InfoWorld : Alibaba says its new AI model rivals DeepSeeks's R-1, OpenAI's o1 Jose Antonio Lanz / Decrypt : Alibaba's Latest AI Model Beats OpenAI's o1-mini, On Par With DeepSeek R1 Ben Jiang / South China Morning Post : ‘Better than DeepSeek and OpenAI’: Alibaba touts open-source AI model that beats rivals Qwen : QwQ-32B: Embracing the Power of Reinforcement Learning Bloomberg : Alibaba Leads Competitors Playing Catchup With China's DeepSeek Bloomberg : Alibaba Shares Jump After Release of DeepSeek Rival X: @alibaba_qwen : Today, we release QwQ-32B, our new reasoning model with only 32 billion parameters that rivals cutting-edge reasoning model, e.g., DeepSeek-R1. This time, we investigate recipes for scaling RL and have achieved some impressive results based on our Qwen2.5-32B. We find that RL training con continuously improve the performance especially in math and coding, and we observe that the continous scaling of RL can help a medium-size model achieve competitieve performance against gigantic MoE model. Feel free to chat with our new models and provide us feedback! @tomaarsen : It's wild to think that this is their MEDIUM model! Their max model is still in development. Qwen is always on the money, and again here. I'm excited to see if the vibes match the evaluation results, but with Qwen's track record, I'm not very concerned. Tommy / @shaughnessy119 : A new open source reasoning model thats 5% the size of, and rivals, DeepSeek 671B China is home to Open Source AI America is losing Nathan Lambert / @natolambert : Before you get excited like me saying “QwQ 32B is for the RL purists.” Cold start with RL. In DeepSeek R1 lingo this means SFT on distill -> RL Then shift to general RL. We need more papers! [image] @tomaarsen : The fine folks at Qwen released QwQ-32B 30 minutes ago, rivaling DeepSeek-R1 671B on various benchmarks, and outperforming OpenAI o1-mini on several as well! Vibe checks still in the works. Details & Links in 🧵 [image] Norm Matloff / @matloff : Qwen continues to be my first choice. Equal to or better than the others in accuracy, and it doesn't flinch when I ask it politically sensitive questions about China. @alibaba_qwen : 🥝 Yesterday we opensourced QwQ-32B, and we put the model on Qwen2.5-Plus + Thinking in Qwen Chat. Based on your feedback, we make a change and put QwQ-32B on the model list of Qwen Chat, and thus you can directly access it by choosing this model. Enjoy and feel free to give us [image] Awni Hannun / @awnihannun : QwQ-32B evals on par with Deep Seek R1 680B but runs fast on a laptop. Delivery accepted. Here it is running nicely on a M4 Max with MLX. A snippet of its 8k token long thought process: [video] Jasper / @zjasper666 : We now have a 32B model that can run on consumer-grade devices and rivals cutting-edge reasoning models like @deepseek_ai R1 and @OpenAI o1. We're entering the stage of democratization of AI models. @hyperbolic_labs served it on the first day, and you can try it now on our dashboard and @huggingface ! Venice / @askvenice : The new Qwen QwQ-32B reasoning model is now live for Venice Pro & API users QwQ-32B rivals leading models like DeepSeek-R1 across multiple benchmarks Try it now [image] @alibabagroup : Exciting news! Today, we release QwQ-32B, our new reasoning model with only 32 billion parameters, delivering performance comparable to other larger cutting edge models. Explore it and share your thoughts with us! Let's drive innovation together! 🚀 #OpenSource #Qwen Binyuan Hui / @huybery : 🚀 Recently, I've been focusing on RL for LLM, and I'm excited to introduce QwQ-32B—the best open-source reasoning model under 100B scale. RL indeed holds some fascinating yet unexplored mysteries. You're all welcome to continue building more interesting things based on Qwen! [image] LinkedIn: Ranjan Rajagopalan : Reinforcement Learning (RL) continues to surprise us! — Now Qwen-32B manages to perform nearly at par with Deepseek-r1 (671B, 37B activated) … Ram Senthamarai : Another major step forward with reasoning models. Alibaba was able to use just multi-stage RL (thanks to DeepSeek's innovation) … Alex Dimakis : Pretty wild: Qwen just released QwQ-32B. A model that is 20 times smaller but still better at reasoning and function calling compared to DeepSeek-R1 (671B). … Forums: r/DeepSeek : Deepseek R1 Killer is here!? r/baba : New Qwen Model Matches DeepSeek R1 with a Much Smaller Memory Footprint
Context & Ripple Effects
Alibaba had already positioned QwQ as a reasoning-model line with its Apache-licensed QwQ-32B Preview, while its Qwen team was broadening the surrounding model family into multimodal device-control capabilities through Qwen2.5-VL. This release moves that reasoning effort from an earlier preview toward wider developer distribution and consumer-device use.
The significance is less the benchmark claim alone than the combination of open availability, a 32B-parameter footprint, and Alibaba's claim that reinforcement-learning-based training can narrow the performance gap with much larger reasoning models.
First-order effects
- Developers can obtain and run QwQ-32B through Hugging Face and ModelScope, while Qwen Chat users gain access through Alibaba's own service.
- Alibaba strengthens the Qwen portfolio's reasoning position by claiming DeepSeek-R1-level results with lower compute requirements; those claims now become easier for developers to test independently.
Second-order effects
- DeepSeek and other reasoning-model suppliers face greater pressure to demonstrate performance per unit of compute, not just benchmark leadership or parameter scale.
- Organizations evaluating reasoning models gain another open option that may fit local or lower-resource deployments, increasing leverage in model selection and infrastructure planning.
Third-order effects
- If smaller open reasoning models repeatedly approach frontier-model results, model competition may shift toward inference efficiency, distribution, tooling, and domain integration rather than raw model size alone.
- The pattern could widen buyer choice in reasoning workloads, but sustained adoption will depend on reproducible evaluations and whether real-world math and coding performance matches published comparisons.
The trend: Reasoning AI is entering an efficiency-and-distribution race in which open models seek to deliver competitive capability at lower deployment cost.