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- Hacker News
- Cool line of questioning, but one piece of information is pivotal and critically not-yet-included: equivalent accomplishments in each language. For example, if I want to write standard things: web server, memoized fibonnaci, recipe search engine, what's the length-and-density of these outputs for each language? I think that would add in some ~normalization.by summarybot
- One thing worth noting is that syntactic density doesn't necessarily mean cheaper because because symbols don't chunk/tokenize as well as plain English
What I see from results like this is that the delta between languages is small enough now that it's hard to justify not not using something like Rust for the performance and correctness benefits if you're using LLMs and it fits the domain
by nylonstrung - Related:
Which programming languages are most token-efficient? - https://news.ycombinator.com/item?id=46582728 - Jan 2026 (91 comments)
by dang - It's not clear to me how useful of a signal replicating existing pieces of well-known software is for this kind of evaluation, given what we know about how effectively LLMs can retrieve data from their training corpus and style-transfer it across different settings (programming languages here). That would explain their convergence in ability across different languages on the tasks in this post. I'd be far more interested in people's real-world experiences.by gr_norm
- Contrary to what most comments seem to indicate, my takeaway from this is that it doesn't really matter all that much for the agents what language you pick. If humans are still to be involved in the process at some point, then its imperative that the language can be read by them, so the preference or skills of the developer(s) are of primary concern, not the agent.by Lutger
- We studied this question pretty systematically in the MirrorCode paper [1], comparing Python, C, Rust, Go, OCaml, and Ada across 19 very long-horizon tasks, for Claude Opus 4.7 and GPT-5.5.
> In our results, there was little sign of inter-language differences in solve rates, for any model (Figure 5b). This suggests that AI models have learned generalized programming skills, rather than pattern-matching syntax. This does not mean that implementation language is irrelevant. Conditional on solving a target, we found a small effect on token usage: successful Python solutions tended to use fewer tokens than average, while successful Ada solutions tended to use more (Appendix C). We consider these to be small differences, given that these six programming languages vary widely in how concise they are, and in how much functionality is provided by their standard library (recall that agents cannot download dependencies in MirrorCode, they must solve the task using only the standard library).
In Appendix C, Ada tended to use only about 25% more tokens than the average language. Ada is a language used mainly in safety-critical aerospace and defense systems, which has ~200x less pre-training data available than C or Python.
We're also comparing more recent language models (on just Go vs Ada, for cost reasons), on our leaderboard [2].
by tadamcz - Ive been amazed at how well LLMs are at writing Gleam[1] and Lustre[2]. Compared to a mainstream language, there is basically zero gleam code in the training data.
I have no evidence to back this up, but I suspect that languages that are good for humans[3] will be good for LLMs. Compiled, strongly typed, statically typed, immutable, pure functions, pattern matched, memory safe, etc.
[1] https://gleam.run [2] https://lustre.hexdocs.pm [3] Yes I realize that languages features that are "good for humans" is a hotly debated topic. That's just my personal list for what I like in a language.
by MichaelNolan - I'm not sure I trust a source that says "just 70 tokens average, nearly half of Clojure (109 tokens)".
There's no reason to add the phrase "nearly half of", and there's especially no reason to add it when it's significantly far away from half.
But on the main topic, I still feel that Go is an excellent choice for LLMs. There is pretty much just one way of doing most things, and the available training data is pretty consistent. This is very different from Python, where training data is polluted (I presume) with tons of code written by non-software engineers and demonstrating many different ways of doing the same thing.
Also a big plus for Go is the tooling. Fast compiles and good linting shortens the iteration cycle time, resulting in less need for me to tell the LLM to correct mistakes.
For some reason, most LLMs I've used default to wanting to write Python. I have to repeatedly teach them to use Go unless there is a very compelling reason to choose otherwise.
I would personally rather see and use Clojure, but I don't feel its ecosystem would provide the same benefits as Go, including obviously the easy single binary distribution.
by michaelteter