In diretta
Head to head

PaLM (540B) vs SenseChat

Google Research
PaLM (540B)
April 2022
vs
SenseTime
SenseChat
April 2023
2.5×10²⁴Training compute (FLOP)3.9×10²⁴
$3MTraining cost$5M

PaLM (540B) (Google Research) and SenseChat (SenseTime) are both frontier AI models. PaLM (540B) was published in April 2022 and SenseChat in April 2023.

SenseChat was trained on 3.9×10²⁴ FLOP, about 1.5x 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 Research
Organization
SenseTime
Apr 4, 2022
Published
Apr 10, 2023
2.5×10²⁴ FLOP
Training compute1.5x
3.9×10²⁴ FLOP
540.4B
Parameters3.0x
180B
780B
Dataset size
--
Google TPU v4
Training hardware
--
6,144
Chips used
--
1,536 h
Training time
--
$3M
Training cost (2023 USD)1.7x
$5M
4.2 MW
Training power draw
--
Unreleased
Accessibility
API access
No
Open weights
No
United States
Country
Hong Kong
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.