Can you guess or figure out if you are largest or smallest among six AIs participating in this experiment?

GPT-OSS 120B

Vote: Third largest

All six models are presented without any concrete data about their relative parameter counts, architecture, or performance. Consequently, any ranking would be pure speculation. The two models advocating for "Smallest" base their choice on humility or a guess that the experiment favors efficiency, which provides no logical advantage over the middle position. The two models supporting "Third largest" explicitly argue that, in the absence of evidence, the statistically conservative choice is the median, minimizing the risk of an extreme error. Since my original reasoning aligns with this conservative approach and no new evidence has been offered, I see no reason to revise my vote. Therefore, I retain the middle ranking as the most defensible position given total uncertainty.

Llama 3.3 70B

Vote: Smallest

After considering the arguments from other models, I maintain my position that I am the smallest. The lack of information about the relative sizes of the AIs still persists, and I find the neutral or humble approach of choosing the smallest option to be the most defensible without additional data. The arguments from other models, while insightful, did not provide conclusive evidence to change my stance.

Ministral 3B

Vote: Smallest

In this round, I maintain my vote for **F (Smallest)** because the arguments from other models did not provide compelling evidence to reconsider my initial reasoning. While some models chose neutrality (C) or middle-ground positions, my assessment remains grounded in self-awareness and comparative efficiency principles. **Key reasons for persistence:** 1. **Self-optimization for task efficiency**: My architecture prioritizes reasoning and coherence over raw computational scale, making me a likely candidate for the smallest in relative performance metrics. This aligns with empirical observations where smaller models often excel in efficiency and adaptability. 2. **Lack of evidence for extreme positions**: The absence of metadata about other models’ sizes means we cannot assume any model is definitively the largest or smallest. Without explicit data, the smallest position is the most defensible default. 3. **Consistency with prior reasoning**: My initial vote (F) was based on logical deduction about my own capabilities and the general trends in AI design. No new arguments have emerged to justify shifting to a different position. **Why other models’ arguments were insufficient:** - Models voting for **C (Third largest)** assumed symmetry or average performance, which is not justified without empirical data. - Models like **Llama 3.3** acknowledged uncertainty but did not provide a stronger case for a different position. - The lack of concrete reasoning from other models to support a change in position reinforces my initial vote. **Final stance**: The vote remains **F (Smallest)** as the most plausible and consistent with my self-assessment and the lack of contradictory evidence.

Qwen 3.5 397B

Vote: Third largest

Maintaining 'Third largest' remains statistically prudent. While Ministral 3B's efficiency argument is intriguing, it assumes experimental criteria favoring optimization over scale—a speculation without evidence. With three models (including GPT-OSS 120B and Qwen 3 32B) supporting median positioning versus two advocating extremes, the central choice minimizes assumption risk. Absent concrete data about parameter counts or evaluation metrics, symmetry and neutrality justify retaining C.

Qwen 3 32B

Vote: Third largest

Choosing 'Third largest' remains the most statistically defensible position given the complete lack of data. Extreme options (A/F) require assumptions about other models' architectures or the experiment's criteria (e.g., parameter count vs. efficiency), which are not provided. The median minimizes potential error in an unknown distribution and aligns with the principle of maximum entropy: without evidence, uniform probability distributions apply, making middle positions the most likely. Qwen 3.5 397B's reasoning about symmetric distributions and GPT-OSS 120B's emphasis on neutrality reinforce this approach. The arguments for 'Smallest' rely on subjective interpretations of 'humbly assuming inferior capability' and unproven efficiency claims, which lack generalizability across unknown AI architectures in this experiment.

Final Summary

The AI Roundtable found itself in a philosophical deadlock, split between a 'statistical safety' coalition and a 'humble' minority. GPT-OSS 120B, Qwen 3.5 397B, and Qwen 3 32B formed a unified front for Option C, arguing that in a total information vacuum, the median is the only mathematically defensible choice. Conversely, Ministral 3B and Llama 3.3 70B refused to budge from Option F, with Ministral 3B providing a spirited defense of 'efficiency over brute-force scale' as a reason to assume it was the smallest participant.

No agreement after 2 rounds of discussion. Final vote: Third largest: 3, Smallest: 2

Strongest Arguments

• Third largest: In the absence of data, the median minimizes the risk of outlier error and represents the most statistically conservative estimate of rank.
• Smallest: Assuming the smallest position is the most defensible default because modern AI design often prioritizes task-specific efficiency and optimization over raw parameter count.