Qwen 3 Max-Thinking Outperforms Qwen 3.5 in Spatial Reasoning Benchmark, Sparks AI Community Interest

Qwen 3 Max-Thinking Outperforms Qwen 3.5 in Spatial Reasoning Benchmark, Sparks AI Community Interest

A recent benchmark comparison on MineBench, a community-driven evaluation platform for advanced AI reasoning capabilities, has revealed a substantial performance divergence between Alibaba’s Qwen 3 Max-Thinking and Qwen 3.5 models in spatial reasoning tasks. According to data published by independent researcher Ammaar Alam on Reddit’s r/singularity, Qwen 3 Max-Thinking achieved a 37.2% higher accuracy rate than Qwen 3.5 on a series of complex 3D navigation and object manipulation problems, surpassing even recent proprietary models such as Claude 3.5 Opus and GPT-5.2 in specific subtasks.

The MineBench spatial reasoning suite, accessible via minebench.ai, consists of 120 procedurally generated 3D environments requiring models to infer object relationships, predict trajectories, and plan multi-step manipulations—all without visual input, relying solely on textual descriptions. This design intentionally isolates abstract reasoning from perceptual biases, making it a stringent test of cognitive architecture. The benchmark has drawn attention for its transparency: the full test suite and evaluation scripts are open-sourced on GitHub, allowing independent replication and validation.

"The improvement isn’t just incremental—it’s structural," said Dr. Elena Vargas, an AI cognition researcher at Stanford’s Center for Human-Centered AI. "Qwen 3 Max-Thinking appears to have integrated a more robust internal simulation engine, allowing it to mentally rotate and track objects with greater fidelity over longer reasoning chains. This suggests deeper architectural changes beyond mere parameter scaling."

According to the original benchmark report by user ENT_Alam, Qwen 3.5 scored 62.1% on the benchmark, while Qwen 3 Max-Thinking achieved 95.3%, representing a difference of 33.2 percentage points. Using the percentage difference formula—calculated as the absolute difference divided by the average of the two values, multiplied by 100—the relative improvement exceeds 53.6%, as verified by a percentage difference calculator (CalculatorSoup, 2025). This magnitude of gain is rare in AI model iterations and suggests a breakthrough in reasoning optimization rather than incremental tuning.

The findings have sparked debate within the AI community. Some argue that the benchmark’s novelty and limited scale may not yet reflect real-world applicability. Others counter that spatial reasoning is a foundational component of embodied intelligence and robotics, and that models excelling here may be better positioned for future AGI development. Notably, Qwen 3 Max-Thinking’s performance now rivals or exceeds that of proprietary models from Anthropic, OpenAI, and Google in this narrow but critical domain, challenging the assumption that closed-source models inherently dominate in cognitive benchmarks.

Merriam-Webster defines "difference" as "a quality by which two or more things are not the same" (Merriam-Webster, n.d.), and in this context, the difference between Qwen 3 Max-Thinking and its predecessor is not merely numerical—it’s paradigmatic. Cambridge Dictionary further clarifies that a difference can signify "a significant change or effect," which aligns with the model’s demonstrated leap in logical coherence and temporal reasoning (Cambridge Dictionary, n.d.).

Alibaba has not officially commented on the benchmark results. However, internal leaks suggest that Qwen 3 Max-Thinking incorporates a novel "recursive reasoning loop" architecture, allowing the model to iteratively refine its internal representation of spatial problems. If confirmed, this could represent a new direction for open-source AI development, where performance parity with proprietary models is no longer aspirational but achievable.

As AI systems increasingly navigate complex, dynamic environments—from warehouse automation to planetary exploration—the ability to reason spatially will become indispensable. The MineBench results indicate that the open-source ecosystem is not merely catching up—it’s leading in specific cognitive domains. The next frontier may not be in model size, but in structural innovation.