Compare Gemini 3.1 Pro Preview by Google AI against GPT-4.5 by OpenAI, context windows of 1.0M vs 128K, tested across 24 shared challenges. Updated February 2026.
24 challenges
Tests an AI's ability to make educated estimates based on technical knowledge
Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 step by step, using reasonable assumptions and approximations:
The transformer architecture primarily consists of two main components per layer:
Where:
GPT-3 (175B) typically has:
Let's verify quickly:
A common simplified rule-of-thumb for transformer models is that training one token requires approximately (6 imes) the number of parameters in FLOPs (forward + backward pass combined). This heuristic is widely used in practice.
Thus, for GPT-3 (175B parameters):
Given GPT-3 was trained on approximately (300 ext{ billion tokens}):
[ ext{Total FLOPs} = ( ext{FLOPs per token}) imes ( ext{number of tokens}) ]
[ = (1.05 imes 10^{12}) imes (3.0 imes 10^{11}) ]
[ = 3.15 imes 10^{23} ext{ FLOPs} ]
Thus, the total number of FLOPs required to train GPT-3 is approximately:
[ oxed{3.15 imes 10^{23} ext{ FLOPs}} ]
This is a rough but reasonable estimate based on common heuristics and known GPT-3 parameters.
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