Alibaba’s Qwen team has unveiled a new open-source reasoning model that sets new performance records and signals a major advance in large-scale, transparent AI development.
The model—dubbed Qwen3-235B-A22B-Thinking-2507—represents the culmination of months of work dedicated to one goal: improving AI’s ability to reason through complex tasks. From math and science to logic puzzles and advanced programming, this model is built to tackle problems that typically stump even high-performing open models.
Crushing the Benchmarks
Qwen’s latest iteration boasts eye-catching benchmark scores:
- 92.3 on AIME25 (reasoning)
- 74.1 on LiveCodeBench v6 (coding)
- 79.7 on Arena-Hard v2 (alignment with human preferences)
These scores don’t just place it at the top of the open-source leaderboard—they make it competitive with closed models from industry giants.
How It Works: A Smarter, Leaner Giant
At 235 billion parameters, this is a true heavyweight model. But thanks to a Mixture-of-Experts (MoE) architecture, it only activates a small portion—about 22 billion parameters—per task. This selective engagement mirrors a system of 128 expert AIs, only calling upon the eight most relevant for any given prompt.
This efficiency not only reduces computational load, but also enhances performance by sharpening the model’s task-specific capabilities.
Memory That Rivals an Elephant’s
Qwen’s native context length of 262,144 tokens is among the highest in the industry. That gives it a major edge in understanding long documents, multi-step problems, and complex codebases—all without losing track of details.
For developers looking to deploy the model, it’s available now on Hugging Face, with support for sglang, vllm, and Qwen-Agent, Alibaba’s own framework for leveraging the model’s tool-calling capabilities.
Optimizing the Thinking Process
To get the most out of Qwen’s reasoning ability, the developers behind it recommend:
- Setting output limits at 32,768 tokens, or up to 81,920 for high-complexity tasks
- Prompting it to “reason step-by-step” when handling logic or math-based problems
These small adjustments help the model deliver more structured and insightful outputs.
Open-Source, But Enterprise-Class
What makes this release particularly compelling is the combination of raw power with open-source accessibility. It’s a rare opportunity for developers, startups, and researchers to work with a model that approaches the performance of proprietary solutions—without the usual closed ecosystem barriers.
The Qwen team’s latest model raises the bar for open-source AI, especially in the domain of reasoning and advanced problem-solving. With tools like this now freely available, we may soon see a wave of innovation driven by the broader developer community.
