Is pandas now faster than data.table?
Has anyone done any benchmarks?

Yes, the 2014's benchmark in question has turned into foundation for db-benchmark project. Initial step was to reproduce 2014's benchmark on recent version of software, then to make it a continuous benchmark, so it runs routinely and automatically upgrades software before each run. Over time many things have been added. Below is high level diff of the 2014's benchmark comparing to db-benchmark project.
New:

continuous benchmark: runs routinely, upgrades software, re-run benchmarking script
more software solutions: spark, python datatable, cuda dataframes, julia dataframes, clickhouse, dask
more data cases

two new smaller data sizes: 0.5GB (1e7 rows) and 5GB (1e8 rows)
two more cardinality factors: unbalanced, heavily unbalanced
sortedness
data having NA


advanced groupby questions

median, sd
range v1-v2: max(v1)-min(v2)
top 2 rows: order(.); head(.,2)
regression: cor(v1, v2)^2
count and grouping by 6 columns


benchmark task: join

Changes (see groupby2014 task for 2014 fully compliant benchmark script):

using categorical/factor instead of character
cardinality of v2 and v3 measures increased
function calls are NA-aware
aggregated columns are named
order of groups is irrelevant whenever possible
extra call to dim()/.shape is included in timings to force lazy evaluation
machine that runs benchmark has 128GB mem (not 244GB mem)
no 100GB (2e9 rows) data size

We are planning to add even more software solutions and benchmark tasks in future.
Feedback is very welcome, feel invited to our issue tracker at https://github.com/h2oai/db-benchmark/issues.


Is pandas now faster than data.table?

According the our results pandas is not faster than data.table.
I am pasting medium size data 5GB (1e8 rows) groupby benchmark plot taken from the report at h2oai.github.io/db-benchmark as of 20210312. Consult the h2oai.github.io/db-benchmark#explore-more-data-cases for other data sizes (1e7, 1e9), data cases (cardinality, NAs, sorted), questions groups (advanced), or tasks (join).
For up-to-date timings please visit https://h2oai.github.io/db-benchmark.