In the increasingly growing discourse around companies wildly spending on tokens and blowing token budgets, I think its really just highlighting the changing modality of models (pun intended).

I feel like a lot of products and companies have moved beyond "use the latest model all the time" a while back. A lot of agents (non-coding) use things like Sonnet and Flash-based and open-weight models. Its good enough.

But for coding I feel like companies have mostly opted to use the latest at all times, for the most part. And I think that should shift with these Mythos-class models. I mean, I think it should've already been shifting with GPT 5.5 xhigh too.

The problem is that I think broadly speaking, a lot of people don't have the skill (a new skill!) to judge when to use what model.

But I think sandbagging, like 18 months from now we'll see a ton of habits change just due to cost (not even capability, but also that).

Fable is a good model. As with all new models, it is simultaneously excellent and entirely unremarkable (relative to other models). It is slow and expensive, and the "loops are all you need" discourse they are pushing is obvious in the context of someone using Fable-class models

What I've found so far is that for broad scope design (code architecture) tasks, Fable is unremarkable. Or, not better enough to justify its cost and speed.

But in highly targeted goal-oriented loops, it is another beast entirely. It is very slow but produces very good results.

I let it churn on optimizing a SwiftUI-layout resolver in Go I wrote and it was able to bring it down to an order of magnitude I could not reach myself (micro => nanosecond scale). But it took 2 hours and $40 to do it and I had to claw back some changes it overfit to Apple Silicon. Still, very worth it.

In comparison, for "implement this feature/change" iterative work, I ran head-to-head Fable vs GPT5.5 vs. GLM-5.1. They all produced equally acceptable final results, but GPT5/GLM did it in a couple minutes and Fable was churning away for 40 minutes. And GLM cost me less than a dollar, GPT5.5 ~$1.50, and Fable cost $9.

You can see that in this context, interactively working with an agent is nonsense. Its too slow. You need to write loops to keep the agent working and you probably want to highly parallelize the work being done. As with all things, I think a balance makes sense...

My sense is that I'd reserve Fable for targeted, surgical analysis and work. Not for daily driving everyday tasks.

I'm going to keep spending a shitload of money (relatively) and maining Fable for the rest of the week to continue to judge, will report if anything changes. I'll continue to head-to-head as well.

It is becoming far too obvious that in order to use every new model effectively we need strong technical skills, a good technological understanding, and a lots of time and money to spend on testing them and restructuring the way they should be approached for our own projects.

There is no unified approach to agentic development and it doesn't look like ever will. Not between different company models and not even between different model versions of the same company. It is all largely very experimental, a guessing game and almost entirely based on claims (like yours) that cannot be properly documented and reproduced in different dev/project environments.

Ignoring for now how hard this goes against the promises made of a brighter simpler future accessible to everyone, the real question is how can companies adapt to the rate new models are being churned and how much is all this costing them financially and to the quality of their products.