What's the best programming language for coding agents?

This somewhat widely cited post (I keep seeing it cited, anyway) suggests that dynamic languages and/or languages that represent things more concisely are more token efficient. It seems to be cited enough that LLM search results agree. For example, when I searched for "dynamic vs static language token cost" (no quotes), Google's AI summary opened with

Dynamically typed languages generally have a lower LLM token cost than traditional statically typed languages because omitting explicit type declarations makes the code more compact.

Google's AI cited the same post, which suggests that some concise dynamic languages have maybe 1/2 to 1/3 the token cost of static languages like Rust, Go, C++, etc. The author says

There was a very meaningful gap of 2.6x between C (the least token efficient language I compared) and Clojure (the most efficient).

And then they later tried J, saying

It dominates at just 70 tokens average, nearly half of Clojure (109 tokens). Array languages can be extremely token-efficient when they avoid exotic symbol sets. If token efficiency turns out to be a key driver, this is perhaps a very interesting way for languages to evolve.

The other dynamic vs. static language token comparison I've found floating around is this one, which supports the same conclusion. If you want to treat this as part 8 of this series of exercises on benchmarking, evals, and experimental design, you can click through to the links and think about eval issues before reading further.

Without running our own eval, one problem the first experiment has is that the problems are trivial, which we can see from quote above; a problem that can be solved in 70 tokens in J and 109 in Clojure isn't much of a problem at all (the author used Rosetta Code). As we saw when we looked at other evals of caveman mode vs. our own evals, you can get very different results from trivial problems where most of the work is in printing out an answer vs. slightly less trivial problems that actually require some amount of "real work"; the big gains claimed by caveman mode and shown in replications go away when you start looking at problems that take more than just a few tokens. In general, performance on trivial tasks doesn't generalize.

The issues in the second link are a little more subtle, so we'll defer most of them to an appendix, but they include issues like one of the tests executing the wrong path (which doesn't exist), causing a test to fail. One of the later agents then symlinks the non-existent path to its own executable, which works for that case, but also causes every later test to run that one agent's executable instead of the correct executable. The author tries to draw conclusions about what it means that Rust had some failures, but all it means is that scoring for Rust ran before the Go agent symlinked all scoring on that broken test to the Go executable.

Instead of relying on these evals, we can try running some of our own evals. As we can see from these evals as well as the evals discussed in our last exercises on evals, it's very easy to make an eval that doesn't say what the creator of the eval seems to think it's saying. No doubt these evals will not be an exception to this and will be flawed (see appendix below for more details).

As a way to build my intuition about things, I like to pre-register guesses before looking at results 1. Some things I pre-registered with friends were:

- High confidence (95%): the overall dynamic vs. static language claim won't hold

- For reasons stated above: this feels analogous to the caveman eval, where the result will, at best, get diluted as the problem gets larger

- Low confidence (60%): static languages will be somewhat better than dynamic at ultra effort

- Very weak confidence that, at ultra effort, the harness will get feedback to the model more quickly and this will result in some kind of benefit for either correctness or efficiency, but it would also seem reasonable for this to not be the case for all kinds of reasons, e.g., I've noticed that codex, when invoking the Rust compiler, very often makes the exact same error and then has to fix it; perhaps this kind of thing dwarfs things like a hypothetical faster feedback cycle

- High confidence (98%): the "weird" language supremacy of something like J won't hold

- Same reasoning as the overall static vs. dynamic claim, with the additional thought that AI labs are going to have much less (and possibly zero) synthetic data RL env effort on obscure languages

Zstd

For the first eval, I tried giving agents the zstd RFC (plus errata) and telling them to implement a complete zstd decoder (agents are stuck in a container without internet access). The tests were not given to agents. For something with the surface area of zstd, it's not really reasonable to expect that the tests cover every possible case. For example, even though zstd is a fairly well-tested piece of software, I once found a data corruption bug in zstd. The test suite isn't intended to find extreme corner cases that might be lurking for years and is instead intended to check various cases that can "easily" be derived from the RFC that should work.

Below, the x-axis is cost and the y-axis is correctness score (up and to the left is better / down and to the right is worse); average result on medium and ultra efforts with GPT-5.6 Sol. If we only look at medium (and ignore the fact that results often wildly differ on different tasks), we might come to a conclusion like the Alderson evaluation, that dynamic languages are more efficient and better when using LLMs because (ignoring relatively obscure languages) the cluster of dynamic languages lands up and to the left of the cluster of static languages (we used Alderson's color-coding for static vs. dynamic to make it easy to compare at a glance). But if we look at ultra effort, the results are quite mixed, with a couple static languages doing the best, with more static than dynamic languages among the better results.

The graphs below also have a toggle to convert the x-axis to time instead of cost. The mame/ai-coding-lang-bench noted that it's valuable to get results more quickly (I personally don't find this to be the case because results take long enough that I multitask instead of waiting), so we can also look at that. Similarly, we observe that neither language type dominates the other although, at medium effort on this particular task, the best dynamic language results are once again better than the best static language results (though, once again, they're fairly close).

We can observe that, just like when we compared completely trivial caveman mode evals to a less trivial caveman mode eval, the very strong relationships that held in the trivial evals don't generalize to this larger case. As was the case there, the extreme ratios in performance go away in these larger evals, except in cases where we might expect poor performance, such as when using assembly (which would be significantly more time consuming and difficult for a human) and when using relatively obscure languages where we might not expect that AI labs are expending effort generating synthetic RL environment data.

Note that this is the opposite of what the 1st eval found when it suggested that very dense languages like J would make sense for efficiency reasons. Perhaps using an obscure (and "weird") language can make sense if you have a very large budget and you can train or fine-tune a model to be effective for your pet language, but if you're a normal user of LLMs, it seems like sticking with a mainstream language is likely a better bet than using an obscure dense language.

And it turns out that if we plot language popularity vs. performance on this eval (not shown), we observe a weak to moderate positive correlation where more popular languages end up with more correct as well as cheaper solutions.