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Head to head

GPT-4 (Mar 2023) vs PaLM (540B)

OpenAI
GPT-4 (Mar 2023)
March 2023
vs
Google Research
PaLM (540B)
April 2022
2.1×10²⁵Training compute (FLOP)2.5×10²⁴
$37MTraining cost$3M

GPT-4 (Mar 2023) (OpenAI) and PaLM (540B) (Google Research) are both frontier AI models. GPT-4 (Mar 2023) was published in March 2023 and PaLM (540B) in April 2022.

GPT-4 (Mar 2023) was trained on 2.1×10²⁵ FLOP, about 8.3x the compute of PaLM (540B) at 2.5×10²⁴ FLOP. Training compute is the closest available proxy for how much was invested in a model, though it says nothing on its own about how well that compute was spent.

These two models share no benchmark on which both have been scored, so no direct performance comparison is possible here. The specification table below is a comparison of inputs, not of results.

Specifications
OpenAI
Organization
Google Research
Mar 15, 2023
Published
Apr 4, 2022
2.1×10²⁵ FLOP
Training compute8.3x
2.5×10²⁴ FLOP
1.8T
Parameters3.3x
540.4B
5.4T
Dataset size6.9x
780B
NVIDIA A100 SXM4 40 GB
Training hardware
Google TPU v4
25,000
Chips used4.1x
6,144
2,280 h
Training time
1,536 h
$37M
Training cost (2023 USD)12x
$3M
19.9 MW
Training power draw
4.2 MW
API access
Accessibility
Unreleased
No
Open weights
No
United States
Country
United States
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SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.