Surprisingly, LLMs are actually much worse at reasoning in Python than other common programming languages for agentic coding tasks.
Data here: https://gertlabs.com/rankings?mode=agentic_coding
Data here: https://gertlabs.com/rankings?mode=agentic_coding
Not how any of it works.
Certain popular PHP codebases appear to use a similar methodology.
So much copy/pasted code, some of it REALLY bad, and PHP has a lot of foot-guns that can lead to RCE.
I'm working with Clojure which is used mostly by senior engineers and it still blows my mind how well Claude writes software in it even though it's a fringe language. It's even able to pick up in-house DSLs written with macros.
Recently, I had a more pleasant experience using LLMs with Go. It reminds me a bit of Python 2.x, when the community seemed, in my view, more focused on embracing a stupid simple language, with everyone trying to write roughly similar "Pythonic" code.
If there’s one language that is the prime example of this, it’s C++, and according to this benchmark it ranks incredibly high.
I’m also thoroughly confused why Kimi 2.6 scores 83% while Opus 4.7 scores 67% for C++, GPT5.5 isn’t even in the top10.
Gemma 4 31B scores 100% success rate for Python (!!) while Opus 4.6 only 65%.
This benchmark really seems to be all over the place and doesn’t make sense.
I also don’t understand how these “games” map to real world complex problems. How are you measuring success? How does “adversarial customer service” map to “this LLM is better at C++ than the other” ? How are you sure you’re not just benchmarking language suitability for a problem ?
I have so many questions about this…
- You need to run evals at scale to converge on this kind of behavior: these benchmarks run samples across a pool of hundreds of different types of environments
- Some games are too open-ended to support code play. The customer service game is an example of that, where models are called on every tick of the environment to make a decision (that's the 'decision making' part of the evals which is weighted lowest). Very interesting results but not testing coding ability, just general reasoning.
Not sure what issues you have with models writing C++ vs other languages, but I can imagine all sorts of C++ specific bottlenecks not directly related to the model's ability to reason in the language, like the dependencies, verbosity, extra effort to manage memory, etc. I have only done a little C/embedded work since agentic coding happened but I was pleasantly surprised.
It seems to present results as if they’re testing language abilities, but the problems seem to be reasoning problems.
It would also be interesting to see how Python compares to other languages in its niche (Ruby, Perl, Raku).
Thanks for putting this together! It's interesting.
a) Typed Racket
b) OCaml
c) Julia
I would love to see those three added to your benchmarks. And Mistral Medium 3.5 added to the LLM list, please.
Mistral Medium 3.5 is on there, but you will have to scroll down pretty far to find it (does not perform well): https://gertlabs.com/rankings?mode=oneshot_coding
Just want to be sure I'm reading the results correctly... When I compare GPT-5.5 with Mistral Medium 3.5, I see in the tables:
a) Mistral beats GPT in Java and C++
b) It's close for Rust
c) GPT-5.5 easily wins for Go, Javascript, Python and Typescript
Model choice really does appear to be language dependent (assuming I'm reading the results correctly).
That's why a lot of people have been freaking out about local LLMs since april. There's finally a decent model that runs locally on a GPU or two that can do agentic programming at a reasonable enough tokens per second.
I've found that the Q5+ quants are less loopy than Q4. Still not perfect, but noticeably better.
> reasonable enough tokens per second
The speed has been amazing. I've been running the recent llama.cpp MTP branch with an uncensored variant of Qwen3.6-35B-A3B on my RTX 3090 over 170 tokens per second and it was able to turn a buffer overflow into a reliable shell exploit in just a few seconds (with reasoning disabled). Still a bit loopy though. Hopefully, the Qwen team will pay more attention to those looping issues. It feels like their models are especially susceptible.
Note that the MTP PR https://github.com/ggml-org/llama.cpp/pull/22673 is still under development, so things might be broken.
The Qwen3.6 models have memorized some common games. For example, if you ask it to create an index.html with a snake game, it will generate almost the same high quality snake game every time. The relatively low success rate of 25% but high average percentile of almost 100% for one-shot coding in Python suggests that the model is extremely good at few tasks.
Typed Racket is to Racket as TypeScript is to JavaScript: it adds some additional static checks to an otherwise dynamic language via gradual typing. This pair of languages might help begin answer the question "does gradual typing generally help LLMs, or does TypeScript outperform JavaScript for incidental reasons?".
Among Lisps, I'm most interested in seeing Clojure because it's a language I can see myself using with LLMs at work. But Typed Racket and Racket could make an especially interesting pair because of the gradual typing thing.
I'm not sure whether you want to include them in your project. The kind of selectivity you describe yourself as going for is hard for me, especially since I'm not the one doing the work. :)
PS: Aside from this benchmarking and comparison project: Racket is an interesting language and seems like a good place to start if you want to explore classic Scheme texts (Structure and Interpretation of Computer Programs, The Little Schemer, How to Design Programs) or newer ones that try to teach newer or more specialized ideas (e.g., The Little Typer). You may have to tweak the language a bit to stay faithful to some of those books, but that's something Racket is good at and there are already sources noting relevant differences online.
When a non-programmer in my life expressed curiosity about programming, we ended up starting HtDP together and it's been fun. I think Racket was a good choice for that.
- Haskell
- OCaml
- F#
- Scala
- Gleam
- Purescript
- Grain
- Idris
Then I asked if there were any Schemes or Lisps that met the initial requirements, which added a bunch more options (Typed Racket, Typol, Elm, ReScript etc).
Then I asked about Julia specifically, as it's a language I'm already reasonably familiar with and knew that it's possible to write it with static annotations.
Next I started filtering the list based on additional criteria; didn't want to target a JS compilation target, performance, size of package ecosystem, tooling, community, learning curve (I do want to review and understand the output).
There were a bunch of follow-up questions over a few hours of prompting, reading and a couple of beers. All this resulted in the shortlist of OCaml, Typed Racket and Julia.
Julia pretty much remains in there, even though it doesn't really meet the strongly typed initial criteria, based on my familiarity, the ecosystem especially for AI/ML tasks and performance factors.
I know zero about OCaml and find the thought of learning it a bit daunting. Typed Racket seems more approachable anyway.
A relative lack of training data might have a bigger effect though.
Nope. Not with Lisps. I've been using LLMs with Clojure/Clojurescript, Elisp and Fennel - for my personal stuff. And Python, Java, JS/TS, Go for work. LLMs are surprisingly good with Lisps, perhaps precisely because there's less fragmentation. There is so much variability with say Python for a given task, because the training set is enormous. But how many ways there exist to do the same thing in Clojure? Python/Java/TS's enormous training set is almost a liability for quality - the model has seen every beginner tutorial, every legacy pattern, every conflicting style guide. With Clojure it's more like the model learned from a curated corpus by default. Lisps have nearly zero syntactic noise - the AST is the source. This means an LLM doesn't need to learn parsing heuristics; structure is always explicit. That likely makes correct generation easier even with less data.
Prolog night be interesting because I bet nobody is trying to train very hard on it, but I'm less directly interested in model performance with Prolog.
That is only accurate if OOP means "inheritance-based class hierarchies with mutable state" - which is one narrow definition of it. Clojure has solid OOP support, just not in the class-hierarchy-first sense.
But I am curious about your favorite OOP-y tools in Clojure. I know it has flexible dispatch, it has a notion of agents that are a bit like objects in how they encapsulate state... but it's been a long time since I really used Clojure and I don't have a clear picture of what the best OOP-y idioms in Clojure look like or what makes them good to use.
Care to explain a bit more?
- Functional (its primary identity)
- Data-oriented (how you model your domain)/data-driven(how you control behaivor)
- Polymorphic/interface-driven (protocols, multimethods)
- Logic-style (via core.logic) - mostly unused
- Reactive/event-driven/actor-like concurrency (core.async, manifold)
- There's another paradigm vector (Spec/Malli), but it frankly has the gap in the terminology landscape. It's neither Dependent Typing, nor Gradual; not quite Contract-based Design; not Refinement Types (typically a static concept); calling it Schema Validation really undersells it - implies just input checking. It does something genuinely novel in combination. There isn't a single established term to capture all of that.
Where the language resists:
- Classical OOP with mutable stateful objects - you can do it via Java interop or careful use of atoms+records, but the language actively nudges you away. It won't feel natural and you'll be fighting the grain.
- Imperative/procedural style - possible, but again, why?
The calva backseat driver extension even includes a specific paren balancer for this reason, and it works quite well
In token terms it's more like the fingers problem than the strawberry problem. ")" is a single token, but the model gets confused by several repeats of the same thing.
Whoa, it seems you're using LLM to generate Clojure code like you'd do it with any other "static" PL. Give it a live REPL - it works wondrously.
Also somehow the 2 language comparison graphs (avg percentile and success rate) rank Python in dramatically different positions, with Python outranking Rust and Java in the success rate. What does the avg percentile mean in this context?
Percentile compares only the submissions that didn't hard-fail. So they are a bit different, and we incorporate them both into the combined score.
Oh wow, we got "tribal domination", "market simulator" and "adversarial customer service". I don't know what those are but it sure sounds like big torment nexus milestones
Maybe we could at least play nicer games like hackenbush and act surprised when there's some wicked use-case that's isomorphic.
EDIT: Ok fine. I like "Rubik's Cube Chess" a lot. Never heard of it, is this analyzed formally at all? Hard to search for since there's tons of collisions
When we reason we need to typically propagate the constraints to arrive at a solution to these constraints. I think the best language to reason in could be something like Lean, which allows both constraints and actual code to be expressed at the same time. Although this might not be the case for current LLMs, as I explain above.
But of course, because the deductive reasoning is inductively taught, there might be various shortcuts which compromise the soundness of deductive reasoning. That's why my claim - LLMs are not as good at it as other algorithms, although they have many other strengths that make up for it.
You could just as well say, wait until you look inside a human brain and realize they’re incapable of deductive reasoning!
Of course we’re not actually able to do that, but if you could you might very well find a similar apparent lack of whatever it is you expect to see at that level.
There are teams of people who don't come from an engineering background who do utilize Python as a series of scripts with some extra sugar. Just because you can do that doesn't mean that you should.
TIL. If i were to start a truly vibe project; Go would have a significant leg up.
https://github.com/Tencent-Hunyuan/AutoCodeBenchmark/blob/ma...
In my opinion, the only thing holding elixir back as an llm deliverable is that there's not as much training data for llms to work with.
Of course if we had a new AI that could be trained on a minimum of existing training data, common lisp would absolutely beat out everything else. everything you mentioned about elixir (repl, runtime, and ability to hot reload / directly test functions) are possible and were invented in lisp with an AST instead of a syntactic language as the ultimate build artifact. CL lets you recover from exceptions and rewind the stack before reloading your fixes and continuing. I can't even fathom the workloads an LLM could conceive of working with that.
Language like elixir were given easier problems to solve when compared to other languages.
As such, functionally speaking - focusing on inputs and outputs - models can be said to reason. If you’re tempted to argue that it’s “only statistics” or similar, consider that human reasoning is only chemical and electrical signals in neurons.
Actually, JS can get a surprising amount of "intellisense" as well. Not sure if that was used here though.
Q: Say, what does this Python code do?
A: Nobody f&%^ing knows.