MiMo 2.5 PRO (1T) Tested: Is It Intelligent?

2026-04-28 · Source: Discover AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, long

Summary

Xiaomi's Mimo 2.5 Pro, a 1 trillion total parameter Mixture-of-Experts (MoE) model with 42 billion active parameters, was released in public beta on April 22nd, 2026. This model was rigorously tested using a complex "elevator test" designed to evaluate causal reasoning, long reasoning traces, and scientific problem-solving capabilities, requiring strategic optimization rather than brute-force exploration. The test involved navigating from floor 0 to 50 with energy, token, and time constraints, aiming for the shortest sequence of button presses. Mimo 2.5 Pro was compared against models like Kimi K 2.6 (1 trillion total, 32 billion active parameters) and QN 3.6 (3 billion active parameters). While Mimo 2.5 Pro demonstrated a more strategic approach than Kimi K 2.6, it ultimately achieved a 10-button press solution plus an emergency exit, which was deemed acceptable but not exceptional, especially when compared to QN 3.6's better performance.

Key takeaway

For AI Scientists evaluating large language models for complex causal reasoning tasks, you should carefully assess a model's strategic exploration capabilities beyond raw parameter count. While Mimo 2.5 Pro shows promise in strategic reasoning, its performance on the "elevator test" suggests that even 1 trillion parameter models may not always deliver superior results compared to smaller, more focused architectures for highly specialized scientific problems. Consider running multiple tests and prioritizing models that demonstrate efficient, focused problem-solving over broad exploration.

Key insights

Large MoE models like Mimo 2.5 Pro excel in strategic reasoning but may not always outperform smaller, specialized models.

Principles

• Causal reasoning requires strategic exploration.
• Optimal quantization for MoE models is Q8.
• Statistical models can yield variable results.

Method

The "elevator test" evaluates AI models on causal reasoning, long reasoning traces, and scientific problem-solving by optimizing button presses under complex constraints, requiring strategic pathfinding over brute-force exploration.

In practice

• Test models multiple times due to stochasticity.
• Consider specialized models for specific tasks.
• Quantize 1T MoE models with Q8 for 80GB VRAM.

Topics

• Mimo 2.5 Pro
• Mixture-of-Experts
• Causal Reasoning
• Elevator Test
• Kimi K 2.6

Best for: AI Engineer, Machine Learning Engineer, AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Discover AI.