Raw models aren't as effective with clojure as they are with typescript or python, but clojure has a superpower that most other languages don't have: the REPL! Specifically nREPL and the ecosystem around it.

An LLM is only as good as its feedback loop. If your LLM can actually test the code it writes, it's going to be much more effective. Static types are a form of feedback (if it can use the LSP), unit/integration tests are another.

Clojure has an exceptionally good repl. LLMs can eval any piece of any function. They can test out functions they aren't familiar with. They can fetch data, try out different arguments, try different approaches before committing to one. They can query a database (read-only connection, of course), look at the result, fetch data from an API, and stitch it all together. It can even hook into your running program and debug it from the inside out!!

It makes it so much more effective at using libraries or paradigms that it isn't trained on. In my experience, hooking an LLM up to the clojure repl lets it write WAY more complex stuff. I'm talking like 10x more complex programs with zero errors, cause it can literally try it out every little piece before putting it together. It's like watching a human programming. But like, really fast.

Sorry I get a little ranty when clojure + LLMs come up, because I don't think most people realize what they're missing out on. It's crazy stuff. It's also easy peasy if you use vscode. There's an extension called calva-backseat-driver that just hooks it all up for you. Gives copilot access to the repl, and I think it exposes an mcp if you want to give claude access too.

GPT 5.4+ models are extremely good at writing Clojure, agreed. In the agentic coding part of our benchmark, they do have access to the REPL via bash if they choose to use it. Filtered here: https://gertlabs.com/rankings?mode=agentic_coding