En vivo
Head to head

EXAONE 1.0 vs Flan-PaLM 540B

LG
EXAONE 1.0
December 2021
vs
Google
Flan-PaLM 540B
October 2022
1.7×10²⁴Training compute (FLOP)2.5×10²⁴
$3MTraining cost--

EXAONE 1.0 (LG) and Flan-PaLM 540B (Google) are both frontier AI models. EXAONE 1.0 was published in December 2021 and Flan-PaLM 540B in October 2022.

Flan-PaLM 540B was trained on 2.5×10²⁴ FLOP, about 1.5x the compute of EXAONE 1.0 at 1.7×10²⁴ 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
LG
Organization
Google
Dec 14, 2021
Published
Oct 20, 2022
1.7×10²⁴ FLOP
Training compute1.5x
2.5×10²⁴ FLOP
300B
Parameters1.8x
540B
--
Dataset size
1.4B
--
Training hardware
Google TPU v4
--
Chips used
512
--
Training time
37 h
$3M
Training cost (2023 USD)
--
--
Training power draw
348.3 kW
Unreleased
Accessibility
Unreleased
No
Open weights
No
South Korea
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.