GPT-5 vs GPT-5.4
Compare GPT-5 and GPT-5.4, both from OpenAI, context windows of 400K vs 1.1M, tested across 45 shared challenges. Updated March 2026.
Compare GPT-5 and GPT-5.4, both from OpenAI, context windows of 400K vs 1.1M, tested across 45 shared challenges. Updated March 2026.
45 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.
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