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.
True. Im working with Python CRUD apps, which every model is fluent in. And I'm personally generating 100-line changes, not letting it run while I'm AFK.
That's what I love most about Claude. I love Django and I love React (the richness of building UIs with React is insane) and sure enough Claude Code (and other models I'm sure) is insanely good at both.
Ah, that makes sense. I’ve had similar luck with UI refactoring on cheaper models, mainly because you can quickly verify whether the output is right.
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.
That's really neat!
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.