[vunderba]

Anecdotally but it definitely feels like in the last couple weeks CC tends to be more aggressive at pulling in significantly larger chunks of an existing code base - even for some simple queries I'll see it easily ramp up to 50-60k token usage.

[troyvit]

This really speaks to the need to separate the LLM you use and the coding tool that uses it. LLM makers utilizing the SaaS model make money on the tokens you spend whether or not they need them. Tools like aider and opencode (each in their own way) use separate tools build a map of the codebase that they can use to work with code using fewer tokens. When I see posts like this I start to understand why Anthropic now blocks opencode.

We're about to get Claude Code for work and I'm sad about it. There are more efficient ways to do the job.

[ayewo]

When you state it like that, I now totally understand why Anthropic have a strong incentive to kick out OpenCode.

OpenCode is incentivized to make a good product that uses your token budget efficiently since it allows you to seamlessly switch between different models.

Anthropic as a model provider on the other hand, is incentivized to exhaust your token budget to keep you hooked. You'll be forced to wait when your usage limits are reached, or pay up for a higher plan if you can't wait to get your fix.

CC, specifically Opus 4.5, is an incredible tool, but Anthropic is handling its distribution the way a drug dealer would.

[Brian_K_White]

It's like the very first days of computers at all. IBM supplied both the hardware and the software, and the software did not make the most efficient use of the hardware.

Which was nothing new itself of course. Conflicts of interest didn't begin with computers, or probably even writing.

[vidarh]

OpenCode also would be incentivized to do things like having you configure multiple providers and route requests to cheaper providers where possible.

Controlling the coding tool absolutely is a major asset, and will be an even greater asset as the improvements in each model iteration makes it matter less which specific model you're using.

[jack_pp]

You think after 27 billions invested they're gonna be ethical or want to get their money back as fast as possible?

[genewitch]

I'm curious if anyone has logged the number of thinking tokens over time. My implication was the "thinking/reasoning" modes are a way for LLM providers to put their thumb on the scale for how much the service costs.

they get to see (if not opted-out) your context, idea, source code, etc. and in return you give them $220 and they give you back "out of tokens"

[throwup238]

> My implication was the "thinking/reasoning" modes are a way for LLM providers to put their thumb on the scale for how much the service costs.

It's also a way to improve performance on the things their customers care about. I'm not paying Anthropic more than I do for car insurance every month because I want to pinch ~~pennies~~ tokens, I do it because I can finally offload a ton of tedious work on Opus 4.5 without hand holding it and reviewing every line.

The subscription is already such a great value over paying by the token, they've got plenty of space to find the right balance.

[NitpickLawyer]

> My implication was the "thinking/reasoning" modes are a way for LLM providers to put their thumb on the scale for how much the service costs.

I've done RL training on small local models, and there's a strong correlation between length of response and accuracy. The more they churn tokens, the better the end result gets.

I actually think that the hyper-scalers would prefer to serve shorter answers. A token generated at 1k ctx length is cheaper to serve than one at 10k context, and way way cheaper than one at 100k context.

[genewitch]

> there's a strong correlation between length of response and accuracy

i'd need to see real numbers. I can trigger a thinking model to generate hundreds of tokens and return a 3 word response (however many tokens that is), or switch to a non-thinking model of the same family that just gives the same result. I don't necessarily doubt your experience, i just haven't had that experience tuning SD, for example; which is also xformer based

I'm sure there's some math reason why longer context = more accuracy; but is that intrinsic to transformer-based LLMs? that is, per your thought that the 'scalers want shorter responses, do you think they are expending more effort to get shorter, equivalent accuracy responses; or, are they trying to find some other architecture or whatever to overcome the "limitations" of the current?

[jumploops]

I believe Claude Code recently turned on max reasoning for all requests. Previously you’d have to set it manually or use the word “ultrathink”

[vidarh]

It's absolutely a work-around in part, but use sub-agents, have the top level pass in the data, and limit the tool use for the sub-agent (the front matter can specify allowed tools) so it can't read more.

(And once you've done that, also consider whether a given task can be achieved with a dumber model - I've had good luck switching some of my sub-agents to Haiku).

[behnamoh]

> more aggressive at pulling in significantly larger chunks of an existing code base

They need more training data, and with people moving on to OpenCode/Codex, they wanna extract as much data from their current users as possible.