One of the main reasons for me for sticking with Claude Code (also for non-coding tasks, I think the name is a misnomer) is the fixed price plan. Pretty much any other open-source alternative requires API key, which means that as soon as I start using it _for real_, I'll start overpaying and/or hitting limits too fast. At least that was my initial experience with API from OpenAI/Claude/Gemini.

Am I biased/wrong here?

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segmenta 5 hours ago | parent | next [-]

Yep, this is a fair take. Token usage shoots up fast when you do agentic stuff for coding. I too end up doing the same thing.

But for most background automations your might actually run, the token usage is way lower and probably an order of magnitude cheaper than agentic coding. And a lot of these tasks run well on cheaper models or even open-source ones.

So I don't think you are wrong at all. It is just that I believe the expensive token pattern mostly comes from coding-style workloads.

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kej 4 hours ago | parent [-]

I don't doubt you, but it would be interesting to see some token usage measurements for various tasks like you describe.

	▲	segmenta 4 hours ago \| parent [-]
		For example, the NotebookLM-style podcast generator workflow in our demo uses around 3k tokens end to end. Using Claude Sonnet 4.5’s blended rate (about $4.5 per million tokens for typical input/output mix), you can run this every day for roughly eight months for a bit over three dollars. Most non-coding automations end up in this same low range.

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giancarlostoro 2 hours ago | parent | prev [-]

You're not wrong, though I suspect the AI "bubble burst" begins to happen when companies like Anthropic stop giving us so much compute for 'free' the only hope is that as things get better their cheaper models get as good as their best models today and so it costs drastically less to use them.