In addition to the 2 nice previous answers (though I'd not include CPython myself, as writing high-performance CPython means writing/using C libraries; and note

In addition to the 2 nice previous answers (though I'd not include CPython myself, as writing high-performance CPython means writing/using C libraries; and note that Lua and LuaJIT are different)... I italicized "average", and wouldn't include Java, because most languages are very small efforts, and thus are different than the top few. A person-century or person-kiloyear of optimization effort has an impact. Observations that "implementations of strategy X generally have characteristic Q" can be true, but there's a hidden context there of "severely resource-limited implementations of X". But two caveats: Sometimes you are trapped. In CPython and PyPy (but not in Jython or IronPython), parallelism remains defined by the GIL. Language implementation tooling sucks less than it used to. Now even toy languages have JIT and rich compilation infrastructure. Aside: In the late 80's, before ARPA was hit by Bush I, project managers had a great deal of autonomy. There was discussion of "what more neat things could we do to accelerate progress?" One observation was that JIT expertise was highly localized, and we could either wait many years for it to slowly spread, or pay someone to stand on people's desks and catalyze its being written up. But that kind of micro-grant didn't yet exist, and time ran out on creating it. Society chose option 1, but a human generation has now passed, and we finally have accessible JIT infrastructure. So, yay? (In fairness, note autonomy and "old boy network"ness was a less happy thing for potential researchers at other than the few main research institutions. Some change was needed, it's just not clear it required societally critical tech to remain largely unfunded for decades. We don't even have a (language) wiki. Though even national science education improvement efforts have failed (but oh so close) to attempt one.)