> Of course, it's always dependent on statistical noise + host system load, and running sufficiently large benchmarks is simply too expensive, so take em with a grain of salt.

And the savings listed are coming from a benchmark harness that implements different OSS bugs one time with and one without lumen - in those cases the % saved are reproducible (caveat: it was on older models, Opus 4.6 I believe).

Also I explain WHY it saves tokens - because the model doesn’t have to brute force different terms until it finds the match it needs, but uses semantic „distance“ so the embedding does it for the model.

I’m using less tokens with Lumen but I also use a bunch of other tokens hacks/skills; it’s hard to measure the impact exactly but it feels significant

But anecdotally I do think it saves me quite a lot of time on reading LLM outputs. And that, if nothing else, is good for my sanity.

The caveman gimmick makes sense to me as a clever hack. Caveman talk is a longstanding meme that's presumably well-represented in the models' training data. So just asking it to do that is just an ultra-concise way to tell the LLM to be ultra-concise. Which, in turn, is theoretically good for accuracy because putting too many instructions in the prompt is bad for task performance.

https://blog.jetbrains.com/ai/2026/05/what-happens-when-you-...