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GPT-3.5 (davinci-002) vs PaLM (540B)

OpenAI
GPT-3.5 (davinci-002)
March 2022
vs
Google Research
PaLM (540B)
April 2022
2.6×10²⁴Training compute (FLOP)2.5×10²⁴
$5MTraining cost$3M

GPT-3.5 (davinci-002) (OpenAI) and PaLM (540B) (Google Research) are both frontier AI models. GPT-3.5 (davinci-002) was published in March 2022 and PaLM (540B) in April 2022.

GPT-3.5 (davinci-002) was trained on 2.6×10²⁴ FLOP, essentially the same compute as 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, 2022
Published
Apr 4, 2022
2.6×10²⁴ FLOP
Training compute1.0x
2.5×10²⁴ FLOP
--
Parameters
540.4B
--
Dataset size
780B
NVIDIA A100 SXM4 40 GB
Training hardware
Google TPU v4
--
Chips used
6,144
--
Training time
1,536 h
$5M
Training cost (2023 USD)1.6x
$3M
--
Training power draw
4.2 MW
API access
Accessibility
Unreleased
No
Open weights
No
United States
Country
United States
Related comparisons
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.