Trustworthy vibe coding. Much better than the other kind!

Not sure I really understand the comparisons though. They emphasize the cost savings relative to Haiku, but Haiku kinda sucks at this task, and Leanstral is worse? If you're optimizing for correctness, why would "yeah it sucks but it's 10 times cheaper" be relevant? Or am I misunderstanding something?

On the promising side, Opus doesn't look great at this benchmark either — maybe we can get better than Opus results by scaling this up. I guess that's the takeaway here.

There have been a lot of conversations recently about how model alignment is relative and diversity of alignment is important - see the recent podcast episode between Jack Clark (co-founder of Anthropic) and Ezra Klein.

Many comments here point out that Mistral's models are not keeping up with other frontier models - this has been my personal experience as well. However, we need more diversity of model alignment techniques and companies training them - so any company taking this seriously is valuable.

The real world success they report reminds me of Simon Willison’s Red Green TDD: https://simonwillison.net/guides/agentic-engineering-pattern...

> Instead of taking a stab in the dark, Leanstral rolled up its sleeves. It successfully built test code to recreate the failing environment and diagnosed the underlying issue with definitional equality. The model correctly identified that because def creates a rigid definition requiring explicit unfolding, it was actively blocking the rw tactic from seeing the underlying structure it needed to match.

Curious if anyone else had the same reaction as me

This model is specifically trained on this task and significantly[1] underperforms opus.

Opus costs about 6x more.

Which seems... totally worth it based on the task at hand.

[1]: based on the total spread of tested models

I absolutely called this a couple of weeks ago, nice to be vindicated!

> I'm interested to see what it is in the age of LLMs or similar future tools. I suspect a future phase change might be towards disregarding how easy it is for humans to work with the code and instead focus on provability, testing, perhaps combined with token efficiency.

> Maybe Lean combined with Rust shrunk down to something that is very compiler friendly. Imagine if you could specify what you need in high level language and instead of getting back "vibe code", you get back proven correct code, because that's the only kind of code that will successfully compile.

https://news.ycombinator.com/item?id=47192116

Pleasant surprise: someone saying "open source" and actually meaning Open Source. It looks like the weights are Apache-2.0 licensed.

FYI The Lean 4 paper: https://dl.acm.org/doi/10.1007/978-3-030-79876-5_37

What are these "passes" they reference here? Haven't seen that before in LLM evals

Could definitely be interesting for having another model run over the codebase when looking for improvements

I don’t know a single person using Mistral models.

Maybe a naive question: given that they see better performance with more passes but the effect hits a limit after a few passes, would performance increase if they used different models per pass, i.e leanstral, kimi, qwen and leanstral again instead of 4x leanstral?

is the haiku comparison because they've distilled from the model?

The TDD foundation! We might need one of those. :)

Does Mistral come close to Opus 4.6 with any of their models?

Here we go.

Oh, is this a new AI model?

This is great, congratulations to the Mistral team! I'm looking forward to the code arena benchmark results. Thanks for sharing.

Truly exciting