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Qwen3-Thinking: Alibaba’s AI Model for Complex Logic & Code

Alibaba's Qwen team has unveiled its latest innovation, the Qwen3-Thinking-2507 model, an open-source system specifically engineered to handle complex tasks that demand deep logical reasoning and advanced deductive abilities.

This model arrives with a sharp and specific focus: thinking. It is not intended to be just another conversational assistant, but rather a powerful tool capable of step-by-step sequential analysis in fields like mathematics, programming, and science.

Performance of the New Thinking Model

Initial benchmarks show the new model demonstrating clear superiority in several key standardized tests.

For instance, on the AIME25 test, which measures mathematical problem-solving skills, the model achieved a score of 92.3.

A bar chart illustrating the superiority of the Qwen3-Thinking-2507 model on performance benchmarks like AIME25 and LiveCodeBench compared to Gemini and OpenAI models
Performance results for Qwen3-Thinking-2507 (in red) show its clear superiority in logic and programming benchmarks compared to other models

The most significant leap, however, was in the field of programming, where the model scored 74.1 on the LiveCodeBench scale. This result places it at the forefront of all tested models, outperforming strong competitors.

It also displayed an exceptional ability in tasks requiring multi-step planning. Its score on the TAU2-Retail benchmark rose from 40.4 to 71.9, reflecting a major advancement in its capacity for sequential decision-making across different contexts.

Qwen3-Thinking's Efficient Architecture

The secret to this model's power lies in its unique architecture, known as a Mixture-of-Experts (MoE).

Although the model contains 235 billion parameters, it does not use all of them for every operation.

Instead, it relies on an intelligent system that selects a small group of specialized "experts"—about 22 billion active parameters—to process each task.

You can think of it as a massive team of specialists, where only the most relevant experts for the current job are called upon.

This architecture grants the model immense cognitive capabilities while maintaining a response speed and computational efficiency comparable to smaller models.

Furthermore, the model supports a long context of up to 256,000 tokens, which allows it to process and analyze enormous documents or entire codebases without losing critical information.

A Strategic Shift Toward Specialization

The launch of this model also signals a strategic shift for the Qwen team.

Rather than relying on a single, hybrid model attempting to do everything, the team is now moving toward developing specialized models for each domain.

Qwen3-Thinking comes as part of an integrated family that includes other models such as:

  • Qwen3-Coder: Designed specifically for complex programming tasks.
  • Qwen3-Instruct: Geared toward general conversations and following instructions.
  • Qwen3-MT: Specialized in translation across more than 92 languages.

This focus on specialization provides developers and companies with more precise and effective tools to meet their specific needs.

Accessing Qwen3-Thinking-2507

Staying true to the open-source ethos, the Qwen team has made the new model available for free download on popular platforms like Hugging Face and ModelScope, under the permissive Apache 2.0 license that allows for free commercial use and modification.

You can also try it for free on the company's platform at chat.qwen.ai.

A screenshot showing the steps to select the Qwen3-Thinking-2507 AI model from the list of available models on the chat.qwen.ai website
Selecting the Qwen3-Thinking model directly from the platform's interface to begin using it.

The team has also provided optimized versions of the model that run on lower technical requirements, opening the door for researchers and small businesses to leverage these advanced capabilities.

For companies that prefer cloud-based solutions, the model is available through a competitive API.

Access the Model on Hugging Face

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