Alibaba’s Qwen team released Qwen3-Max-Thinking on January 26, 2026, positioning it as their most advanced reasoning model to date.
Trained on massive scale with reinforcement learning, this model excels in complex problem-solving, tool usage, and agentic capabilities.
It introduces two standout innovations: adaptive tool-use (automatically selecting and applying tools like search, memory, and code interpreter) and test-time scaling (TTS, a multi-round self-reflection process that refines answers during inference for superior reasoning depth).
These features enable Qwen3-Max-Thinking to outperform leading competitors including GPT-5.2, Claude Opus-4.5, Gemini 3 Pro, and DeepSeek-V3.2 across multiple rigorous benchmarks.
The release demonstrates a clear focus on practical intelligence, where the model “thinks deeper” to tackle graduate-level science, competition math, coding challenges, software engineering tasks, function calling, and broad expert exams.
Key Innovations Explained
- Adaptive Tool-Use: The model intelligently decides when and how to use external tools without user prompts, improving reliability in real-world applications.
- Test-Time Scaling (TTS): During inference, it performs iterative self-reflection and refinement, significantly boosting accuracy over single-pass generation. Results show TTS versions consistently outperform non-TTS variants and rivals.
Benchmark Performance Highlights
Qwen3-Max-Thinking dominates in most categories, especially with TTS enabled. Below is a summarized comparison of select benchmarks (scores in percentage accuracy):
| Benchmark | Description | Qwen3-Max-Thinking (with TTS) | Qwen3-Max-Thinking (without TTS) | Top Competitor (e.g., GPT-5.2 / Claude / Gemini) |
|---|---|---|---|---|
| GPQA Diamond | PhD-level science questions | 92.8 | 87.4 | 91.9 – 92.4 |
| IMO-AnswerBench | IMO-level math problems | 91.5 | 83.9 | 83.3 – 86.3 |
| LiveCodeBench | Competition coding problems (2025) | 91.4 | 85.9 | 80.8 – 90.7 |
| SWE-bench Verified | Real software engineering tasks | 75.3 | – | 73.1 – 80.9 |
| τ²-Bench | Function calling / tool use | 82.1 | – | 80.3 – 85.7 |
| Humanity’s Last Exam | Expert-level questions across subjects | 36.5 | – | 25.1 – 37.5 |
| Humanity’s Last Exam (with Search) | Same, but using search tools | 49.8 | – | 40.8 – 45.8 |
These results highlight consistent leadership in reasoning-heavy tasks. Notably, it achieves elite scores like 98.0 on HMMT advanced math competitions and strong gains when tools are allowed.
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Why This Matters for Users
For developers, researchers, students, and professionals, Qwen3-Max-Thinking offers:
- Superior handling of complex math, science, and coding problems
- More reliable agent behaviors through automatic tool integration
- Improved accuracy via TTS without extra user effort
- Competitive edge in areas where traditional single-pass models fall short
Access is available via the Qwen Chat interface for interactive use and through Completions and Responses APIs for integration into applications.
The model supports a broad range of tasks, making it suitable for education, research, software development, and automated workflows.
While it sets new standards in reasoning, keep in mind that performance can vary by task, and API usage incurs token-based costs.
