RTK reports token savings, but our cost benchmarks disagree

All of these "hacks" are snakeoil and I think deep down we all know. Whether it's caveman, RTK, or whatever other vibe-coded productivity/token cost saving hacks/skills/claude.md.

What I had success with (although benchmarks are older) is to index the codebase with a dedicated local code embedding model. It's a bit expensive on the CPU side but in my benchmarks it reduced token use and wall clock time significantly. Of course, it's always dependent on statistical noise + host system load, and running sufficiently large benchmarks is simply too expensive, so take em with a grain of salt.

Why does it work you may ask? Well, LLMs basically brute force words/phrases and pipe that into find/grep/pgrep/whatever (or as recently discussed here write a python script for it - https://news.ycombinator.com/item?id=49654229). Semantic search looks for similarities so you have to do less brute forcing. Comes of course at the cost of indexing everything first.

You can find the project here: https://github.com/ory/lumen

It seems like most of these tools are mostly vaporware. Benchmarks done on Headroom and RTK show that neither result in real savings. If it were possible to have such a simple pre-process step why wouldn’t the AI Labs upstream the optimizations themselves? My guess is they mostly don’t work or make the behavior much more confusing for the model. I really think there needs to be some kind of independent benchmark.

Here are other cases demonstrating the exact same issues with these kinds of tools:

https://blog.jetbrains.com/ai/2026/07/rtk-claude-code-token-... https://brandonbarker.me/writing/headroom-fewer-tokens-bigge...

I wrote article about it couple months ago that I didn't believe it works. Nice to see numbers now

https://mroczek.dev/articles/the-token-compression-illusion-...

Glad to see that more and more people realize these are just snake oils. Without objective metrics like benchmarks, none of the claims mean anything.

That's also how I feel about skills/plugins. While some provide important context for specific projects/environments, I am very skeptical about (over)generalized skills like "writing JS tests" or "creating a spec". There are dozens of these skills internally at my company, but I haven't seen a single benchmark that shows any of those are better than just plain, single sentence prompts in a meaningful way (aka statistically significant).

The technique looked dubious from the start, because LLMs were trained to expect certain outputs from common bash tools. If the output is not what it expects, an LLM may issue more tool calls than before, because it will assume the tool is broken, the arguments passed to it were wrong, or it's a newer/older version of the tool etc => more tokens. Sounds like just adding to the prompt to use `grep` and `tail` extensively will do the trick without any special tooling.

Some months ago I was evaluating command output compressors to integrate into Dirac[1] as that seemed like an easy win that would compliment and compound with Dirac's other mechanisms.

I tested rtk among these and it was actually a net negative in both CPU time and accuracy, the latter would throw LLMs way off and make it hard to recover. If you are building a coding agent, I'd hard pass on rtk.

   ~ $ time grep Return * 2> /dev/null | wc -l
   966
   grep Return * 2> /dev/null 0.36s user 0.02s system 98% cpu 0.382 total
   wc -l 0.00s user 0.00s system 1% cpu 0.380 total


   ~ $ time rtk grep Return * 2> /dev/null | wc -l
   260
   rtk grep Return * 2> /dev/null 4.10s user 17.10s system 92% cpu 23.008 total
   wc -l 0.00s user 0.00s system 0% cpu 23.007 total

Much worse CPU consumption, and more importantly, plain wrong result. These kind of results compromise the entire agent performance because the model trusts wrong output. Without the correct results, any hypothetical savings are penny wise pound foolish

So yeah I am still on the lookout for a credible CLI wrapper, do let me know if you have any in mind.

[1] https://dirac.run/

This makes sense. “Don’t try to penny-pinch your employees” is a lesson most managers learn eventually, and I guess agent-orchestrators will have to learn it too.

Main takeaway:

  Average cost per attempt, without → with RTK:
  
  Claude/Fable: $1.72 → $1.64 (~5% cheaper)
  DeepSeek: $0.115 → $0.121 (~5% more expensive)

  Almost all Claude savings came from a single task.
  Excluding it, savings were under 1%.

It took me a few rereads to parse out the top-line. This article really buries the lede.

We have been working in this space for the past year. Based on our experience, I no longer trust any claims unless they are backed by benchmark results (yes, benchmarks are painful to run reliably and expensive).

It is possible to reduce token usage. It’s just much harder than the basic approach.

If you are using maven you should tell your agent to use its quiet mode or rtk, because mvn love to write a lot of useless output.

I don't understand how this or all these magic skill bundles and methodologies get traction and why they are so popular. It's either plain worse or has serious trade offs.

i don't know if such hacks works, but in C# if you use roslyn mcp, you save a lot.

Just another instance of the bitter lesson. The model itself knows how to be clever and conserve tokens in command output by using shell primitives and as the models get smarter they get better at anticipating large output and defensively adapting the input commands.

well yeah.... now you're giving it output it wasn't trained on.