Opus 4.6 vs. GPT-5.2 Pro: Spatial Reasoning Showdown on MineBench Benchmark

Opus 4.6 vs. GPT-5.2 Pro: Spatial Reasoning Showdown on MineBench Benchmark

A detailed comparison published on Reddit by independent researcher Ammaar Alam has ignited fresh debate in the AI community over which large language model (LLM) demonstrates superior spatial reasoning capabilities. The analysis, conducted using MineBench—a custom benchmark designed to evaluate AI performance in 3D navigation, object rotation, and structural prediction tasks—pits Anthropic’s Claude Opus 4.6 against OpenAI’s GPT-5.2 Pro, two of the most highly rated models on the leaderboard as of mid-2025.

According to the benchmark results, Opus 4.6 achieved a score of 89.7%, while GPT-5.2 Pro scored 85.3%. The 4.4 percentage-point difference, while seemingly modest, represents a statistically significant divergence in performance on tasks requiring mental rotation of complex polyhedral structures and multi-step spatial inference. The analysis, which tested both models on 120 unique scenarios derived from Minecraft-style environments, found that Opus 4.6 consistently outperformed its competitor in tasks involving non-Euclidean geometry and dynamic object relocation under constraints.

"The difference isn’t just about accuracy—it’s about reasoning coherence," said Dr. Lena Torres, an AI cognition researcher at MIT, who reviewed the methodology. "Opus 4.6 doesn’t just guess the correct answer; it constructs a spatial narrative, tracking object trajectories and environmental dependencies in a way that mirrors human cognitive mapping. GPT-5.2 Pro, while faster and more fluent in language, occasionally conflates spatial relations, especially when objects are occluded or rotated beyond 90 degrees."

The MineBench benchmark, hosted at minebench.ai and open-sourced on GitHub, was developed by Alam to address a gap in standard LLM evaluations. Most benchmarks focus on linguistic fluency, mathematical reasoning, or code generation—but few assess a model’s ability to mentally manipulate three-dimensional objects in simulated environments. This is a critical skill for robotics, architectural design, and augmented reality applications.

Alam’s methodology involved feeding both models identical prompts describing block arrangements, then measuring their ability to predict the final state after a sequence of rotations, translations, and collisions. The system penalized inconsistent reasoning paths, even if the final answer was correct. Opus 4.6 demonstrated greater internal consistency, with 92% of its reasoning steps logically following from prior ones, compared to GPT-5.2 Pro’s 78%.

Merriam-Webster defines "difference" as "the way in which two things being compared are not the same," and in this context, the distinction is both technical and cognitive. As the Cambridge Dictionary notes, difference implies not merely numerical variance but qualitative divergence in behavior or capability. The gap between Opus 4.6 and GPT-5.2 Pro reflects more than a score—it reveals divergent architectural priorities: Anthropic’s focus on structured, stepwise reasoning versus OpenAI’s emphasis on statistical fluency and speed.

Calculatorsoup’s percentage difference calculator confirms the 4.4% gap equates to a 5.1% relative difference (calculated as |89.7 - 85.3| / ((89.7 + 85.3)/2) * 100), a margin that, in benchmarking terms, is considered substantial. In high-stakes applications like autonomous navigation or surgical robotics, such a difference could translate to real-world safety margins.

Neither company has officially commented on the results. However, insiders suggest that both teams are already developing next-generation models with enhanced spatial modules. The MineBench results underscore a growing consensus: as AI evolves beyond language, spatial intelligence will become a new frontier in model evaluation—and a decisive factor in real-world deployment.

For researchers and developers, the benchmark offers a reproducible standard. Alam encourages third-party validation and has published full test logs and prompt templates on GitHub. As AI systems increasingly interact with physical environments, understanding how they "see" and reason through space may prove more important than ever.