Compare Gemini 3 Flash Preview by Google AI against GPT-5.4 by OpenAI, context windows of 1.0M vs 1.1M, tested across 51 shared challenges. Updated March 2026.
46 fights queued
Tests an AI's ability to make educated estimates based on technical knowledge
A standard back-of-the-envelope estimate for training FLOPs is:
[ \text{Training FLOPs} \approx 6 \times N \times D ]
where:
This is a common transformer scaling approximation.
GPT-3 is known to have about:
[ N = 175 \text{ billion} = 1.75 \times 10^{11} ]
A typical quoted training token count is about:
[ D = 300 \text{ billion} = 3 \times 10^{11} ]
First compute parameter-tokens product:
[ N \times D = (1.75 \times 10^{11})(3 \times 10^{11}) = 5.25 \times 10^{22} ]
Now multiply by 6:
[ 6 \times 5.25 \times 10^{22} = 31.5 \times 10^{22} = 3.15 \times 10^{23} ]
[ \boxed{\text{Training GPT-3 required on the order of } 3 \times 10^{23} \text{ FLOPs}} ]
This is usually expressed as a few (10^{23}) FLOPs, which matches the well-known rough estimate.
If you want, I can also derive where the (6ND) rule comes from in more detail.
Tests an AI's ability to understand game rules and strategy
Tests an AI's ability to solve a simple but potentially confusing logic puzzle
1
If Sally is one of the 2 sisters each brother has, then there is only 1 other sister besides Sally.
Tests an AI's randomness and creativity
Unique jokes: 5
Tests an AI's ability to generate vector graphics
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
Tests an AI's humor and creative writing ability
Tests an AI's ability to simulate personalities and predict future trends
Tests an AI's humor and understanding of current events
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Tests an AI's ability to generate a complete, working landing page
Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.
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