Live
Head to head

Gemini 1.0 Ultra vs PaLM (540B)

Google DeepMind
Gemini 1.0 Ultra
December 2023
vs
Google Research
PaLM (540B)
April 2022
5×10²⁵Training compute (FLOP)2.5×10²⁴
$31MTraining cost$3M

Gemini 1.0 Ultra (Google DeepMind) and PaLM (540B) (Google Research) are both frontier AI models. Gemini 1.0 Ultra was published in December 2023 and PaLM (540B) in April 2022.

Gemini 1.0 Ultra was trained on 5×10²⁵ FLOP, about 19.8x 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
Google DeepMind
Organization
Google Research
Dec 6, 2023
Published
Apr 4, 2022
5×10²⁵ FLOP
Training compute20x
2.5×10²⁴ FLOP
--
Parameters
540.4B
--
Dataset size
780B
Google TPU v4
Training hardware
Google TPU v4
57,000
Chips used9.3x
6,144
2,400 h
Training time
1,536 h
$31M
Training cost (2023 USD)10x
$3M
38.4 MW
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.