Those are some fine languages, but how did you pick them? What was the criterion?

The initial criteria was strongly typed and functional first. Using an LLM for answers, of course, that returned me a list that looked like:

- 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.

Thanks for the recs, we will look into adding some of these, maybe OCaml for variety. I'm not familiar with Racket.

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

Thanks for that, I hadn't scrolled down far enough.

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).

The deeper you go into the filters (single models, cross correlated by specific languages), the smaller your sample sizes. A known limitation, tbh I doubt Mistral is better than GPT 5.5 at programming in any specific language and probably hit a few lower quality generations by GPT 5.5 by chance (but I could be wrong! We're always adding more samples so data improves over time. We always prioritize largest sample counts for near-frontier models first).

What's going on with Qwen3.6 27b? Filtered to Python it comes out at the top of the list, which seems... well, unlikely.

While Qwen3.6 27B and 35B-A3B are very good, I am skeptical about them being that good. I think another factor is at play here.

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.

Qwen3.6 27b is a really strong model.

Yeah but that strong?

Yes that strong. Its only lacking in context length, but it's not that small there and it gets caught in circles more often then say a 1t parameter model does.

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.

> it gets caught in circles more often then say a 1t parameter model does.

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.

Is that on a single 3090? I need to change my settings it sounds like

Yes, single RTX 3090 with this model https://huggingface.co/llmfan46/Qwen3.6-35B-A3B-uncensored-h... following these https://huggingface.co/havenoammo/Qwen3.6-35B-A3B-MTP-GGUF instructions (should add "-j 8" to last cmake command for parallel build) and llama-server with --reasoning off

Note that the MTP PR https://github.com/ggml-org/llama.cpp/pull/22673 is still under development, so things might be broken.

The more filters applied (one-shot coding only, Python only), the more variation you can expect from fewer samples -- that being said, it really is a great model so it's probably not too far above where it would end up with infinite samples.

Racket is a variety of Scheme that grew up as a teaching language, but now also has a few other notable niches as well.

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.