[cyanydeez]

this would be more interesting as a local LLM anlysis; throw out all the costs, and figure out primary-subagent model architecture, and maximize token generation and prefill.

I don't see how anyone can operationalize this information.

[jarodrh]

That's an interesting thought, and one I'll take note of, but that would be a different tool. However, if you look hard enough I'd say we're tackling the same issue. I'm just choosing to look at the problem from a cost perspective as opposed to a raw token generation/prefill perspective. The mindset can be applied to both sides, but Frugon prices the cloud side.

The end goal is essentially the same and your mind went to that point, "primary-subagent model architecture". This is what the tool helps you figure out. It's not there to hold your hand and explain what your architecture should be, as I wanted it to be a small simple tool that would give the user insight into triggering your exact thought process. The thought process of breaking down their tasks by type, mapping that to individual models regardless of app or dev tool, regardless of cloud or local (the thought process transfers). It shows the user that they could route a portion of their calls to a cheaper model. It's then up to the user to understand the task type and point those calls to different models.

To directly answer the operationalization statement, it depends on what the call log is from (app/harness).

If harness, then the direction would be to pin models per role per task type; the routing recommendation maps directly to that, and measure/judge allows you to verify this before switching.

If app, then this is a similar shape as above, where you would then categorise and pin those calls identified in the recommendation to the model recommended, and as above, measure/judge allows you to verify before switching.

You're proposing an optimisation for throughput whereas Frugon is for spend. Same issue, just different lens.

[cyanydeez]

yes, but $ is operationally useless since $ is model depenedent and as other articles currently on HN show, the model+harness are symbiotic or antagonistic; if you strip out the $ part of it, you can focus on the interdependence of Agent+subagent and you can evaluate that in a stricter sense because I would want to select the model+subagent that improves my outcomes instead of whicheere is cheapest. If you tell me this model has a 99% chance of succeeding at $10 and this one has a 50% chan

[jarodrh]

You got cut off at the end, but I hear your point.

I completely agree that cost-per-successful-outcome is the goal, and "cheapest" is never the entire solution, but only routing to the cheapest isn't what frugon does. If it did that, then it would just pick something like GPT-4.1-Nano (one of the current cheapest models regardless of quality) for everything, and it would be useless.

The whole pipeline in frugon is predicated on quality tier gating. Frugon doesn't strip out quality. This is based on quality tiers from LMArena leaderboard data (CC-BY licensed). The easy/hard split only recommends routing calls predicted to survive the downgrade.

> $ is operationally useless since $ is model depenedent

> model+harness are symbiotic or antagonistic

To these points, frugon gives you --measure (you can compare the outcomes yourself via prompt sampling from both models - current and candidate/recommended) and --judge (championing a model to determine whether the recommended/candidate model actually successfully holds the quality of the current model from the prompts sampled). The win/tie rate summary outputted by the judge tells you whether the candidates you're comparing preserve the outcome you're currently getting from the current model measured on your own prompts. So, if your model succeeds at the prompt and the candidate ties this (judge's verdict), then you've successfully evaluated that you can swap to a different model at a lower cost. It's measured on your task distribution as a quality confirmation of frugon's initial offline quality tier gating routing recommendation.

Success rates are task-dependent, as every user prompts a model differently which could either get a successful outcome or a failure-doomed-to-retry outcome. No benchmark/table can tell you what your agent+subagent's odds of success are, unless all you do is pass an unaltered prompt that has been externally benchmarked. Hence why --measure and --judge exist. You must confirm the initial routing recommendation with your own evaluation (human/ai/evals)

However, you have made me think of combining the initial dollar cost analysis with judge outcome as an "effective cost per judged success" metric...which is a great idea, as they are currently separated.