The focus of the study was the different harness approaches and how they scale across model sizes. The fact that they used any particular set of models is irrelevant.
The focus of the study was the different harness approaches and how they scale across model sizes. The fact that they used any particular set of models is irrelevant.
I'm not totally convinced that models are fungible, the claudes/gpts/Gemini all have pretty individual feels when you're working with them. I wouldn't be surprised if the approaches don't scale or even work the same in a poly model setup
that's anecdotal though, right? your subjective feeling of how a model responds to you will greatly influence how you 'feel' about a model and its performance in the same way that a co-worker who you get along with will fuck something up and you'll be more forgiving than when you work with a too-verbose, mansplainer of a co-worker who fucks up
I think until we have actual repeated-use measurements tracked over time (eg consistent prompts used to do the same tasks, count number of hallucinations and errors and bugs over a long period of time) you won't really have any idea of which model is better
I also think of it like a car - some just feel better to drive even if they are materially worse in other measures. until you start measuring the metrics important to you (eg MPG and cost of maintenance over a long period), you have no idea which car is actually better suited for you. and the fact that you can only do so with a limited number of cars (or hours available to work, or money to burn on tokens) means there's no true measure approaching objectivity
Anecdotal or subjective don't mean "wrong." I would 100% agree that claude and chatgpt have different 'styles.' They do have their own patterns, and those patterns are distinguishable.
it doesn't mean wrong, it's just probabilistically full of unregarded bias and prone to hallucinated ineferences, much in the same way that LLMs sometimes are
You can write a config that points your claude code at any anthropic-compatible endpoint, and replace opus and sonnet with whatever you want. Things like deepseek and glm in there do not feel at all like they do in more minimal harnesses, but neither do they convincingly act like claude models. It's very stark and frankly they run like shit this way. Try it.
it's almost like the harness was specifically designed for their model and not others :)
But the behaviour of the system can totally change under different scales.
comparing x10 to x100 doesn’t necessary inform you about x100_000 to x1_000_000
That can only be assumed by someone with zero clue about harness engineering.
So i can skip this study.
Nope. Sorry. Not how this works.
How does it work then?
I'm going to cherry pick one example where newer models are noticeably improving at least in my experience.
What is a noticeable improvement with something that struggles to read a message longer than 200 characters without missing information in the middle, may be a 0.000000001% improvement with a model that... almost never misses info in the first place.
Agree. Harnesses are effective because they interact with the underlying model effectively. If the latest models were fundamentally different, excluding them would be a miss. But I don’t think they are, at least not in ways that would affect these observations.
I think the confounding issue is that by now, millions of sessions of Claude Code and Codex are now in the training set for these models. So they have been trained to work the way these harnesses are configured, and at least in the case of Claude Code the harness itself is greatly stripped down because the model has absorbed it.