NEW GLM-5 vs MiniMax-2.5: NEW = BETTER?

2026-02-13 · Source: Discover AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, long

Summary

This analysis compares two new large language models, GLM-5 and MiniMax M2.5, focusing on their pure causal reasoning and logical intelligence rather than agentic capabilities or tool use. GLM-5 is a Mixture-of-Experts (MoE) model with 744 billion parameters and 40 billion active parameters, while MiniMax M2.5 is also an upgraded general-purpose model. The models were tested on a complex "elevator puzzle" requiring navigation from floor 0 to 50 with energy limits, code cards, and mathematical operations tied to button presses, aiming for the shortest valid path. Both models initially struggled, exhibiting different reasoning strategies and multiple failures. Ultimately, GLM-5 produced a 10-step solution that it initially validated but later deemed invalid upon a second internal check, citing a constraint violation. MiniMax M2.5 also failed, initially validating the same sequence as correct before admitting an error and acknowledging the constraint violation.

Key takeaway

For AI Engineers evaluating new large language models for critical reasoning tasks in domains like finance or medicine, you should implement rigorous, multi-stage validation processes. Do not accept initial model-generated solutions or self-validations at face value, as models can contradict themselves or miss critical constraints, necessitating external verification and careful scrutiny of their reasoning paths.

Key insights

New large language models struggle with complex causal reasoning puzzles despite advanced architectures.

Principles

• Model reasoning traces offer transparency but are not definitive.
• Complex constraint satisfaction remains a challenge for LLMs.

Method

The testing methodology involved a multi-constrained "elevator puzzle" to assess causal reasoning, focusing on finding the shortest valid path while adhering to energy limits and button-specific mathematical operations.

In practice

• Validate LLM solutions multiple times for consistency.
• Do not rely solely on model-generated reasoning traces.

Topics

• GLM-5
• Miniax M2.5
• Mixture-of-Experts
• Causal Reasoning
• Model Evaluation

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

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