Comment by divan 4 days ago
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?
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 $4.5 per million tokens for typical input/output mix), you can run this every day for roughly eight months for a bit over three dollars. Most non-coding automations end up in this same low range.
You're not wrong, though I suspect the AI "bubble burst" begins to happen when companies like Anthropic stop giving us so much compute for 'free' the only hope is that as things get better their cheaper models get as good as their best models today and so it costs drastically less to use them.
Sonnet is 3$ per million tokens, Grok Code Fast is 0.2$. IME the latter is better for me. Wish everybody treats AI as a pay-as-you-go commodity instead of getting dependant on rugpulls. My stack is Openrouter (model marketplace) and Aider (Kilocode and Cline for user friendly alternatives).
Will check out Grok Code Fast - thanks for the pointer. In my experience, coding agents can swing a lot in quality depending on the model’s reasoning power. When the model starts making small but avoidable mistakes, the overhead tends to cancel out the benefit. Curious to see how Grok performs on multi-step coding tasks.
Zed is nicely setup for this I just have not taken the time. I do like how Claude works atm. The coding agent functionality is what's nice about Claude Code. I don't know that Grok Code Fast has that?
Came across this interesting article on the internals of Claude Code: https://medium.com/@outsightai/peeking-under-the-hood-of-cla... . The author redirected the LLM calls into LiteLLM to analyze how it behaves.
Yeah, I think when they made the bet it genuinely made sense. But in coding workflows, once models got cheaper, people did not spend less. They just started packing way more LLM calls into a single turn to handle complex agentic coding steps. That is probably where the math started to break down.
What non coding tasks do you use Claude Code for? Genuinely curious.
LOTS! Sometimes for quick file system organization, creating Claude skills, deep document analysis.
Anthropic published a doc or two about this too, here's one of them: https://www-cdn.anthropic.com/58284b19e702b49db9302d5b6f135a...
That’s great to know. I’ve come to the same conclusion. I’ve found that things work best when they happen right where I’m already working. Uploading files or recreating context in a web service adds friction, especially when everything is already available locally.
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.