I find the token/credit restrictions on Opus to be near useless even when using Claude Code. I only ever switch to it so get another model's take on the issue. Five minutes of use and I have hit the limit.

We have the $200 plans for work and despite only using Opus, we rarely hit the limits. CCUsage suggests the same via API would have been ~$2000 over the last month (we work 5 hours a day, 4 days a week, almost always with Claude).

Same context length and throughput limits?

Anecdotally I find gpt4.1 (and mini) were pretty good at those agentic programming tasks but the lack of token caching made the costs blow up with long context.

[0] https://github.com/ryoppippi/ccusage

Unfortunately there's no easy tool to inspect usage. I started a project to parse the Claude logs using Claude and generate a Chrome trace with it. It's promising but it was taking my tokens away from my core project.

Do you mostly use opus?

It uses way less tokens or much more effectively when running locally.

Also there's a cli argument that lets you specify the model. try `claude --help`.

Maybe I'm out of touch, but I'm not handing out my phone number to sign up for random SaaS tools.

There's additional storage costs with google caching, around $3.75 for 5 minutes/Mtok, and Claude Opus is $3.75 for 5minute Cache Writes / Mtok.

For cached reads Gemini Pro is 5X cheaper than Opus and like $0.01 more than Sonnet.

E.g. if need a self-contained script to do some data processing, for example, Opus can often do that in one shot. 500 line Python script would cost around $1, and as long as it's not tricky it just works - you don't need back-and-forth.

I don't think it's possible to employ any human to make 500 line Python script for $1 (unless it's a free intern or a student), let alone do it in one minute.

Of course, if you use LLM interactively, for many small tasks, Opus might be too expensive, and you probably want a faster model anyway. Really depends on how you use it.

(You can do quite a lot in file-at-once mode. E.g. Gemini 2.5 Flash could write 35 KB of code of a full ML experiment in Python - self-contained with data loading, model setup training, evaluation, all in one file, pretty much on the first try.)

Small models are for querying the context

Opus is cheap if you use it for its niche

> Small models are for querying the context

I respectfully disagree.

My experience is that large models are capable of understanding large contexts much better. Of course they are more expensive and slower, too. But in terms of accuracy, large models are always better at querying the context.