Do 4x Grok 4.20 Agents Outperform Gemini 3.1 PRO?

2026-02-20 · Source: Discover AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

This analysis compares the performance of Grok 4.20 with four agents against Grok 4.1 (single agent) and Gemini 3.1 Pro on a complex "elevator test" puzzle. The test requires finding the shortest sequence of button presses to navigate an elevator from floor 0 to floor 50, with specific rules and interdependencies. Grok 4.1, operating as a single agent with Python tool calls, previously found an optimal solution of seven button presses plus an exit. Gemini 3.1 Pro, tested on Arena without tool calls, also achieved this seven-button solution. However, Grok 4.20, configured with four agents and given 2 minutes and 11 seconds, produced a solution requiring nine button presses plus an exit, indicating it was not as efficient as its single-agent predecessor or Gemini 3.1 Pro for this specific, highly interdependent logical puzzle. The four agents often resorted to web searching, and the problem's non-separable nature hindered parallel processing.

Key takeaway

For AI Engineers designing or deploying multi-agent systems, you should critically assess the inherent separability of your target task. If your problem involves a complete spectrum of logically interconnected reasoning steps, deploying multiple agents may lead to suboptimal solutions compared to a single, more capable agent, as parallel processing benefits are negated by interdependencies. Consider a single-agent approach for complex, non-separable logical puzzles.

Key insights

Multi-agent systems may underperform single agents on tasks with high interdependencies.

Principles

• Agent performance depends on task separability.
• Interdependent tasks hinder parallel agent processing.

Method

The "elevator test" involves finding the shortest button press sequence from floor 0 to 50, with specific rules and an "inversion on each button" mirror mode, designed to assess complex nonlinear logic reasoning.

In practice

• Evaluate agent configurations against task complexity.
• Prioritize single agents for highly interdependent problems.

Topics

• Grok 4.20
• Gemini 3.1 Pro
• Multi-agent Systems
• AI Model Evaluation
• Complex Problem Solving

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

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