unsloth/Qwen3-235B-A22B-Thinking-2507
Qwen3-235B-A22B-Thinking-2507 is a 235 billion parameter causal language model from Qwen, featuring 22 billion activated parameters and a native context length of 262,144 tokens. This model is specifically enhanced for complex reasoning tasks across logical reasoning, mathematics, science, and coding, achieving state-of-the-art results among open-source thinking models. It also demonstrates improved general capabilities like instruction following and tool usage, making it suitable for highly complex analytical and problem-solving applications.
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Qwen3-235B-A22B-Thinking-2507: Enhanced Reasoning Model
This model is a specialized version of the Qwen3-235B-A22B series, developed by Qwen, with a strong focus on thinking capabilities. It features 235 billion total parameters (22 billion activated) and an impressive native context length of 262,144 tokens, designed to handle extensive information for complex problem-solving.
Key Enhancements & Capabilities
- Superior Reasoning Performance: Significantly improved on tasks requiring logical reasoning, mathematics, science, and coding, achieving state-of-the-art results among open-source thinking models.
- General Capability Improvements: Enhanced instruction following, tool usage, text generation, and alignment with human preferences.
- Extended Context Understanding: Features 256K long-context understanding capabilities, crucial for deep analysis.
- Thinking Mode Only: This model is specifically designed for a "thinking mode," with its chat template automatically including a
<think>tag to facilitate complex reasoning processes.
Performance Highlights
Benchmarks indicate strong performance across various domains:
- Reasoning: Achieves 92.3 on AIME25 and 83.9 on HMMT25, outperforming many competitors.
- Coding: Scores 74.1 on LiveCodeBench v6 and 2134 on CFEval.
- Knowledge & Alignment: Shows competitive results on MMLU-Pro (84.4) and WritingBench (88.3).
Recommended Use Cases
- Highly Complex Reasoning Tasks: Ideal for applications demanding deep analytical thought, such as advanced mathematical problem-solving, scientific inquiry, and intricate logical deductions.
- Agentic Applications: Excels in tool-calling capabilities, with recommendations to use Qwen-Agent for streamlined integration.
- Long-Context Processing: Suited for scenarios requiring the model to process and reason over very long input sequences, leveraging its 262,144 token context window.