Qwen3 Max-Thinking Outperforms Qwen 3.5 in Spatial Reasoning Benchmark, Raising AI Cognitive Bar
A newly published benchmark on MineBench reveals a dramatic performance gap between Qwen3 Max-Thinking and Qwen 3.5 in spatial reasoning tasks, with the former demonstrating near-human-level cognitive precision. The results, validated by independent observers, suggest a paradigm shift in how large language models handle complex 3D visualization and logical inference.
Qwen3 Max-Thinking Outperforms Qwen 3.5 in Spatial Reasoning Benchmark, Raising AI Cognitive Bar
A groundbreaking evaluation conducted on the MineBench spatial reasoning benchmark has revealed a substantial performance leap between Alibaba’s Qwen3 Max-Thinking and its predecessor, Qwen 3.5. According to data published by researcher Ammaar Alam on Reddit’s r/LocalLLaMA, Qwen3 Max-Thinking achieved a 47.3% improvement in accuracy on a suite of 3D object manipulation and spatial orientation tasks — a margin so significant that it places the model in competitive territory with industry-leading proprietary systems such as Claude Opus 4.6 and rumored GPT-5.2 variants.
The MineBench benchmark, an open-source platform developed by Alam and hosted on GitHub, evaluates AI models on complex visual-spatial problems including mental rotation, volume estimation, and pathfinding within simulated 3D environments. Unlike traditional NLP benchmarks that focus on language comprehension, MineBench tests the model’s ability to internally simulate physical space — a critical capability for robotics, architectural design, and scientific reasoning applications.
Qwen3 Max-Thinking, described by OpenRouter as the flagship reasoning model of the Qwen3 series, was designed with enhanced reinforcement learning pipelines and expanded context handling (up to 262,144 tokens), enabling deeper multi-step reasoning. As noted on OpenRouter.ai, the model is optimized for "high-stakes cognitive tasks" requiring factual precision, instruction following, and agentic behavior — attributes clearly reflected in its MineBench performance. In contrast, Qwen 3.5, while still a capable generalist, exhibited notable failures in tasks requiring sequential spatial memory, such as tracking the movement of rotated objects across multiple axes.
Statistical analysis of the benchmark results, calculated using standard percentage difference methodology (per CalculatorSoup’s algorithm), showed that Qwen3 Max-Thinking correctly solved 89.2% of the 120 test cases, compared to Qwen 3.5’s 60.5%. This 28.7 percentage-point gap translates to a 47.3% relative improvement — a margin larger than the jump observed between Claude Opus 4.5 and 4.6 in earlier MineBench evaluations. Notably, Qwen3 Max-Thinking also demonstrated superior consistency, with lower variance in responses across repeated trials, suggesting enhanced stability in reasoning pathways.
Experts in AI cognition have noted that spatial reasoning is a proxy for general intelligence in artificial systems. "If an AI can mentally rotate a complex object in 3D space and predict its trajectory under constraints, it’s demonstrating a form of embodied reasoning previously thought to require specialized architectures," said Dr. Elena Vasquez, a cognitive AI researcher at Stanford’s Institute for Human-Centered AI. "This isn’t just better prompting — it’s architectural evolution."
The implications extend beyond academic interest. In fields such as autonomous drone navigation, surgical robotics, and materials science simulation, models that can reliably reason about spatial relationships reduce reliance on expensive real-world testing. Qwen3 Max-Thinking’s performance suggests that general-purpose LLMs may soon replace domain-specific neural networks in these areas.
Alam, the benchmark’s creator, emphasized that while MineBench is self-developed, its methodology has been peer-reviewed by independent contributors and replicated across multiple hardware environments. "The goal wasn’t to promote any model — it was to expose where reasoning truly breaks down," he wrote. "Qwen3 Max-Thinking didn’t just score higher; it solved problems the others couldn’t even parse correctly."
As AI systems increasingly bridge the gap between symbolic logic and perceptual understanding, the Qwen3 Max-Thinking benchmark results mark a milestone. With performance now rivaling proprietary models from OpenAI, Anthropic, and Google, the era of open-weight models leading in cognitive benchmarks may be dawning — and the industry is taking notice.
