This has been a bit U-shaped; c. 2023-early 2025 Julia was catching up to these languages, but since then it’s fallen behind. This is mostly because Julia lacks the static checking used for informed speculative decoding and RLVF (Reinforcement Learning from Verifiable Feedback) in other programming languages. (Unfortunately, JET.jl doesn’t count: it’s slow, not sound, still doesn’t have traits or interfaces, and isn’t included by default so nobody knows about it and the LLMs often don’t think of including it in their tests.)
The other big issue is the long compile and startup times and big runtime, which make the iteration/training process painfully slow. This means you can’t make up for it in sheer quantity of data the way you can in Python (which has both a larger pretraining corpus and a faster feedback loop). Julia is kind of stuck in a “worst of both worlds” situation.
I flagged this back in 2023 when the AI companies started broadcasting their plan going forward was to use RLVF, but it lines up pretty closely with the most common complaints people have had about Julia since it came out in 2014.