fable vs. forecast ETS model estimation performance

Question

I'm seeing a large difference in the performance of the forecast ets function and the fable ETS function when estimating multiple models, surprisingly in favor of forecast. Here's a simple example using the "tourism" data set from fpp3. The inelegant loop in the forecast section is intentional. Apologies for the rest of the code in advance:

# libraries ---------------------------------------------------------------

library(fpp3)
library(forecast)

# fable functions ---------------------------------------------------------

start_time <- Sys.time()

fable_fit <- tourism %>%
  model(
    ets = ETS(Trips)
  )

fable_out <- fable_fit %>%
  forecast(h=4) %>%
  hilo(c(95,80)) %>%
  unpack_hilo(c('95%','80%')) %>%
  as_tibble() %>%
  unite(Key_FC,Region, State, Purpose, Quarter, sep = '@', remove = FALSE)

end_time <- Sys.time()

fable_time <- end_time - start_time
cat("Time for `fable` estimation & forecast:", round(as.numeric(fable_time,units = "secs"),2), "sec\n")


# forecast functions ------------------------------------------------------
tourism_aug <- tourism %>%
    unite(Key,Region, State, Purpose, sep = '@', remove = TRUE)

keys <- unique(tourism_aug$Key)

forecast_out <- tibble()

start_time <- Sys.time()
for(i in 1:length(keys)) {
  ts_single <- tourism_aug %>% 
                filter(Key == keys[i]) %>% 
                select(-Key) %>%
                ts(frequency=4, start=c(1998,1))
  
  forecast_new <- forecast(ets(ts_single[,2]),h=4) %>%
    as_tibble(rownames="index") %>%
    mutate(Key = keys[i])
  
  forecast_out <- forecast_out %>% bind_rows(forecast_new)
}
end_time <- Sys.time()

forecast_time <- end_time - start_time
cat("Time for `forecast` estimation & forecast:", round(as.numeric(forecast_time,units = "secs"),2), "sec\n")


# Comparison --------------------------------------------------------------

comparison_data <- forecast_out %>% 
  unite(Key_FC, Key, index, sep="@", remove = FALSE) %>%
    left_join(fable_out,
            by = c("Key_FC" = "Key_FC"),
            suffix = c(".forecast",".fable")) %>%
  mutate(mean_2 = (`Point Forecast` - .mean)^2) %>%
  mutate(`80%_lower_2` = (`80%_lower`-`Lo 80`)^2) %>%
  mutate(`80%_upper_2` = (`80%_upper`-`Hi 80`)^2) %>%
  mutate(`95%_lower_2` = (`95%_lower`-`Lo 95`)^2) %>%
  mutate(`95%_upper_2` = (`95%_upper`-`Hi 95`)^2) %>%
  arrange(desc(mean_2))

comparison_summary <- comparison_data %>%
  select(Key, Quarter,
                mean_2, `80%_lower_2`, `80%_upper_2`,
                `95%_lower_2`,`95%_upper_2`) %>%
  group_by(Key) %>%
  summarise(MSE_mean = mean(mean_2),
            `MSE_80%_lower` = mean(`80%_lower_2`),
            `MSE_80%_upper` = mean(`80%_upper_2`),
            `MSE_95%_lower` = mean(`95%_lower_2`),
            `MSE_95%_upper` = mean(`95%_upper_2`),
            max_err = max(MSE_mean,
                          `MSE_80%_lower`,
                          `MSE_80%_upper`,
                          `MSE_95%_lower`,
                          `MSE_95%_upper`
            )
  ) %>%
  arrange(desc(max_err))

On my machine, my output is

Time for `fable` estimation & forecast: 106.93 sec
Time for `forecast` estimation & forecast: 37.03 sec

The comparison_summary table seems to indicate that the models are giving the same forecasts, only fable is taking almost 3 times as long as using forecast in a simple loop.

Am I using either of the functions incorrectly? My first thought was that fable ETS is searching a much larger set of models, but on default settings the search space for both algorithms should be the same.