alt Hacker NewsBut that's not very informative.
Levenshtein distance is not only a well-understood problem, it's small, self-contained, and extremely well-represented in the training data. The kind of problem where even small/bad models can excel. The golden standard for those tasks is just "use a library" so no wonder the beefy models are expensive: you're chartering a commercial airplane to go grocery shopping.
My personal benchmarks are software engineering tasks (ideally spanning multiple packages in a monorepo) composed of many small decisions that, compounded, make or break the implementation and long-term maintainability.
There's where even frontier models struggle, which makes comparisons meaningful.
>> many small decisions
It’s making guesses not decisions, framing as decisions will lead you astray to wasted time and tokens.
It’s vaguely productive to tell them a ton of relevant info upfront attempting to minimise their need for load bearing guesses. I say vaguely because obedience is generally only around the level where it's good enough to lull you into a false sense of security, not to actually be obedient.
It’s a bit more productive to use the various loop mechanisms (hooks, /goal etc) to evaluate each end of turn against guard rails and reject with clear instruction on whats unacceptable. Obviously if you only do this without the front load of info then you’re likely to spend more tokens to reach a satisfactory end of iteration.