Same, I either use xhigh on best model, or use cheapest and fastest models (mercury/jev/haiku/luna)

[delayed]

In executing tasks no, but in generating assets, art, UI, etc, higher reasoning leads to drastic improvements.

I feel like people use medium models thinking they are saving money, but in the end, it ends up wasting more time and tokens theough the increased rate of issues/bugs.

If the highest tier model makes even one or two fewer mistakes that require human intervantion, that almost makes it a no-brainer choice for production.

I've been doing some research lately on my own (aside from the major benchmarking going on) and I've definitely found cases where medium models do much poorly and cost more than the higher models. But on the other hand, if I can give the medium models certain skills or other resources to complete a task then they perform better in cost than the higher model (which, given the same resources, sometimes gets more confused than the medium does).

At least for my use case of evaluations of a set of features.

Yup, in my benchmarks usually medium or high do better than xhigh or max

Asking people to guess beforehand how much intelligence they require for a task will hopefully be a relic soon. We’re lacking useful routing techniques which accurately pick the right model for the job. Or a high intelligence model which completes simple tasks fast and cheap. Or something else.

Yeah, an xhigh models should know a task is easy and respond quicker.

Even better at the local LLM stage, where QFN can catch a mistake in prior work and patch it without any subscription costs at all. then I can use a cloud model for polish and finish and move on.

I'm building stuff at home with my work focused on managing infra. If anyone wants to sponsor a SOTA model subscription I will check it out. Until then I'll see how flexible a Strix Halo setup and my homelab can be.

I also started with Qwen-3.8-27b and used for a while, I still use it so Claude can delegate tasks to it, but the SOTA models are still a lot better than local models, so much that I don't see much sense to use local models as long as I have a paid subscription.

Even if the difference is a single extra error caught early, that can end up saving me hours of work later.

I have some tasks that deserve a bit more reasoning but for a long time most of my needs have been met by mid-range models, rather than the big ones. There's barely any motivation for me these days to use something more advanced than Terra on low/medium for everyday work. For a personal agent doing occasional coding, I'm still on Mimo flash.

I think many people still expect their tasks to need something more fancy, but could do with a reset of expectations and try to go much lower.

Production pipelines for non-coding usage. Jagged edge is very real. Just one example, loads of tasks where Gemma 4 26ba4b wipes the floor with Deepseek Flash v4.1. On Artificial Analysis-like benchmarks, the latter is many times more "intelligent". Not everything in the world is coding and math, but those are easy to "measure". Things that are hard to measure aren't part of their benchmarks.