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PaperclipPerfector  1mo ago (text post)   23566 thread views

Culture War Roundup for the week of June 8, 2026

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So TL;DR, tokens are cheaper than basically all white collar workers in the modern west, but they get overused for fun projects?

Largely (though Mythos approaches white collar wages in terms of dollars per hour at API rates). But it's not like all tokens go straight to code written. Tokens are more like measuring thought.

When I give Mythos/Fable instructions it first goes 'I'll explore the codebase' and so it searches for relevant things (those search commands are output tokens). Then it reads files which have the relevant data, more input tokens. Then it thinks for a while (that's output tokens). Then it makes its to do list. Then it reads some more, thinks some more. There are pages and pages of just reading and thinking before it goes 'i have a full picture'. Then it starts editing code!

Then it'll try and test if it actually works, often writing some test cases, so that's more code. Then it tells me everything it did in summary and adds stuff to its memory files.

So a lot of thought is happening even if it only adds a few pieces here and there for a new feature.

Yeah, the "thinking" process itself also counts as output tokens. When you use a reasoning model, it's basically writing a long monologue about how it's going to solve your problem and then immediately throwing it away at the end. (Different providers have different policies about whether you're allowed to see this monologue, but it often significantly exceeds the length of the actual code or whatever that the AI is writing.)

So, I'm kind of clueless about this, but are reasoning models are actually different models, as in different neural net weights?

Like, do you get a reasoning model by running a single-pass model in a loop where you feed it prompts like: "first, understand the problem and make a plan for solving it, formatted like this", then "here's the plan you thought up before, try to execute point 1 now", and so on?

Or do you need a different model specially-trained for this kind of thing and it's a big secret black box how it all works?

So, I'm kind of clueless about this, but are reasoning models are actually different models, as in different neural net weights?

Yes. Generally, reasoning models are trained to use special "start thinking" and "stop thinking" tokens, and to generate a specific kind of monologue in between those tokens. Similar to how RLHF biases models towards producing text that's appealing to human readers, reasoning models use techniques like RLVR to bias towards generating monologues that end in a correct solution to a problem.

Many reasoning models are trained in a way that lets you disable the reasoning by forcing them to never generate the "start thinking" token -- Claude Opus 4.8 probably uses the same weights regardless of whether you enable or disable thinking, for example -- but their weights are different from models that were never trained for reasoning in the first place.

With that being said, people used to use "chain of thought prompting" to get a similar kind of result out of regular LLMs. (I think reasoning models basically got started when AI companies saw the early success of chain-of-thought prompting and started baking it in at the training stage.)

More that they're cheaper than a code monkey, only weakly expensive if you're doing something hard or novel (or novel-to-you), and they can get ludicrously expensive if you just start firing the slop cannons, either to solve a problem by volume or by producing a lot of useless or specialized lines-of-code.

And because they allow brute-forcing problems in ways that weren't possible before, or tackling new problems. Or because the user cocks up, as in the case where they fail to notice their two bots getting into an infinite loop.

The problem is that a token is cheap, but the amount of tokens you need to do useful things can be very high.

For agentic programming, the agent needs to hold a non trivial amount of the codebase in the context. That can easily be millions of tokens. Then you have whatever pile of "skills" (read: markdown files) you use, then add the various layers of prompts, then add reasoning chains. It adds up very quickly.

Once you start adding parallel agents and loops, it can get insane.

It is weird to think back to the time when bringing out a 4k token model was a massive deal. You could hold, like, paragraphs in context. Like, nearly a whole chapter of an actual book.

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