In diretta
Head to head

Gemini 1.0 Ultra vs U-PaLM (540B)

Google DeepMind
Gemini 1.0 Ultra
December 2023
vs
Google
U-PaLM (540B)
October 2022
5×10²⁵Training compute (FLOP)2.5×10²⁴
$31MTraining cost--

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

Gemini 1.0 Ultra was trained on 5×10²⁵ FLOP, about 19.8x the compute of U-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
Dec 6, 2023
Published
Oct 20, 2022
5×10²⁵ FLOP
Training compute20x
2.5×10²⁴ FLOP
--
Parameters
540B
--
Dataset size
1.3B
Google TPU v4
Training hardware
Google TPU v4
57,000
Chips used111x
512
2,400 h
Training time
120 h
$31M
Training cost (2023 USD)
--
38.4 MW
Training power draw
348.3 kW
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.