4 ms·
RTK reports token savings, but our cost benchmarks disagree
- maorbril 22d ago[flagged]
- vrighter 22d agowell yeah.... now you're giving it output it wasn't trained on.
- nextaccountic 22d agoWhat if the next-gen models are trained on RTK output as well? Then you will actually have less tokens in the context window, and the model won't become confused (which would require more turns, wasting tokens)
- vrighter 22d agodoesn't change the fact that it doesn't do what it claims to now. I just don't care about vague promises and "trust us bro" vibes that tech is sold for nowadays. It claims x, it doesn't deliver x. Maybe it could in the future, or maybe not.
- nextaccountic 22d agoThat's indeed not something that RTK can promise. However, if RTK becomes popular enough, it's unavoidable that its output will start seeping into the training data of future models, which will make RTK perform better So it's a kind of self-fulfilling prophecy
- aeneas_ory 22d agoAll 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 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 https://github.com/ory/lumen
- Whitespace 22d agoI should not trust their "vibe-coded productivity/token cost saving hacks" but I should trust yours? Save 30% token costs when using Claude Code, Codex, OpenCode for free - with open source, local semantic search. Works for small and large codebases and monorepos! Enterprise-ready and fully compliant via Ollama and SQLite-vec. Releases v0.0.42 Latest last month Why should I trust that what you're peddling isn't snakeoil?
- icantevenhold 22d agoOnly way to find out is to do some testing yourself i think. I’m using less tokens with Lumen but I also use a bunch of other tokens hacks/skills; it’s hard to measure the impact exactly but it feels significant
- huflungdung 22d ago[dead]
- aeneas_ory 22d agoI literally say you should take benchmarks with a grain of salt :) > 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. And the savings listed are coming from a benchmark harness that implements different OSS bugs one time with and one without lumen - in those cases the % saved are reproducible (caveat: it was on older models, Opus 4.6 I believe). Also I explain WHY it saves tokens - because the model doesn’t have to brute force different terms until it finds the match it needs, but uses semantic „distance“ so the embedding does it for the model.
- lucaprata 22d ago[flagged]
- deleted 22d ago[deleted]
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- sreekanth850 22d agoi don't know if such hacks works, but in C# if you use roslyn mcp, you save a lot.
- VulgarExigency 22d agoI don't think they're comparable. RTK just modifies the output of CLI tools to reduce the number of tokens, a Roslyn MCP gives the agent a fundamentally superior way of interacting with a C# codebase.
- sreekanth850 22d agoYes. and i find model makes less errors and reasoning the codebase well, especially when you do a large refactor.
- antupis 22d agoMy main issue with rtk is that rtk randomly messes modification and agent start polling same tool continuously.
- xnorswap 22d agoI haven't tried it since it was first released but it didn't seem to work at all for me back then. It was so slow that the roslyn results would be lagged well behind any edits it was making, which would just leave it confused.
- lmeyerov 22d agoMuch earlier, I tried to set up some static analysis tools so that the coding agent would have access to dataflow analysis etc. tools instead of just grep for typed python. If there were benefits, they weren't easily apparent :(
- CodesInChaos 22d agoWhich one specifically? The ones I found looked like unmaintained throw-away experiments.
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- fwlr 22d agoThis 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.
- simonwsimonw 22d ago[dead]
- gillesjacobs 22d agoMain 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.
- elian_ilands 22d ago[flagged]
- fleetfox 22d agoI 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.
- daliusd 22d agoIt is just "putting a wet phone in rice" of AI
- semiquaver 22d agoJust 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.
- hokkos 22d agoIf 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.
- jakozaur 22d agoI believe RTK would work well in that use case. Sometimes creating less verbose variants yourself (a simple script, build.sh, with pointers to logs) can be a quick win.
- ProjectBarks 22d agoIt 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://blog.jetbrains.com/ai/2026/07/rtk-claude-code-token-... https://brandonbarker.me/writing/headroom-fewer-tokens-bigge https://brandonbarker.me/writing/headroom-fewer-tokens-bigge...
- grim_io 22d agoEven JetBrains is now AI blog-slop, how disappointing.
- U1F984 22d agoNot all, but definitely on some. I also find it very off-putting.
- ericyd 22d ago> If it were possible to have such a simple pre-process step why wouldn’t the AI Labs upstream the optimizations themselves? Not defending these tools, but one reason these might not be upstreamed is because it would negatively impact vendor margins, and they have no incentive to save their users money
- kgeist 22d agoJudging by the leaks, OpenAI and Anthropic already train reasoning traces to use fewer tokens (they deliberately omit articles and prepositions, use very short sentences, etc.), even though you pay per token. So it wouldn't make sense to do that if the only incentive was "make them pay for as many tokens as possible per task." It's more subtle than that. If a user has to wait longer for a solution/pay more, they'll be less satisfied and may switch to a competitor. More unnecessary tokens also means more unnecessary compute. Longer sessions are increasingly more expensive to serve than shorter sessions. And there's always the Jevons effect: as a resource becomes cheaper, demand often increases, and so does net resource consumption. So, imho, frontier labs have every incentive to reduce token usage per task (while also making you use AI for more and more tasks in your daily life)
- saltypixel 22d ago[flagged]
- GodelNumbering 22d agoSome 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/ https://dirac.run/
- yuzushi-dev 22d ago[dead]
- psadri 22d agoWe 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.
- dist-epoch 22d agoOne quick win is to just avoid wasteful tokens, for example run all the QA tools like the unit tests in --quiet mode, which only prints warnings/failures.
- deleted 22d ago[deleted]
- kgeist 22d agoThe 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.
- jghn 22d agoThis was the problem I saw. I installed rtk when it came out and liked the idea of it. But over time with newer model generations I kept seeing the model get confused in the reasoning text and retry a command bypassing rtk. I didn't even need a benchmark to see it was regularly an impediment to the final outcome.
- fg137 22d agoGlad 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).
- jasonjmcghee 22d agoI see the same thing and have effectively the same philosophy. If I'm using something like figma or glean or playwright/chrome dev tools, plugin/skill/mcp - likely very useful. But so many of the weird collections of skills that people on YouTube get viral followings for - I just don't get it. People excitedly ask me what skills I use and I feel bad just saying only things we've directly authored for some express purpose. None of the "hot" ones. I've written a large handful of skills, but they aren't like vim plugins. I don't just leave them "on". This has been my experience at least- curious if I'm just behind the times. I also effectively didn't leave the IDE+ChatGPT copy/paste workflow until the first release of Claude code. So maybe I'm slow to adopt.
- linrl3 22d ago[dead]
- joegibbs 21d agoYes I’m also suspicious of skills. I think the main issue is forcing everything down the skill’s path even if it’s not actually necessary.
- lackoftactics 22d agoI 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-why-im-skeptical-of-rtk/ https://mroczek.dev/articles/the-token-compression-illusion-...
- daliusd 22d agoYes, your article was noticed in my circle, but numbers was what was lacking
- lucaprata 22d ago[flagged]
- santiago-pl 22d agortk gain mechanism is oversimplified. 1 token != 4 bytes for the standard prompt / context window at coding agent.
- cityofdelusion 22d agoAny magic tool that declares a savings of over 10% can be immediately classified as snake oil. You can check yourself, load any of those projects up in GitHub and notice the math is always extremely misleading. It will be something like theoretical input bytes, or amount of command stripped off, or some other lie. If the tool won’t be upfront about those things, they are not worth looking into any further. It’s used car salesman strategy.
- liam_ilands 22d ago[flagged]
- elij 22d agoI just spawn a subagent in the cheapest range (for example flash-lite) to summarise a tool use. It's the only way that has worked based on my benchmarks and generalises well.
- chorizo 22d agoSurprised this is so far down. A subagent with a cheap model like haiku or similar is the way to go instead of dumping tool output directly into the main agent context
- bustermellotron 22d agoIs there an easy way to do this with eg codex? It seems like eg sol agents can’t spawn Luna subagents, so eg a “code research” subagent can save the main agent’s context, but can’t save tokens necessarily. (I suppose a tool to call codex CLI would work, but a bit unsatisfying.)
- agentdev001 20d agoIIRC, openai enabled the ability for subagents to use different models than the parent- maybe a month~ ago.
- oefrha 22d agoIt's pretty damn obvious to anyone who ever bothered to look at rtk gain output, no benchmark needed at all. Agent runs rtk command-that-prints-100k-tokens | tail -5 costs 5 lines, maybe 100 tokens without rtk, but rtk will report 100k savings. Of course it doesn't know about that tail -5. Worse, since rtk defaults to persisting that savings stat, it breaks sandboxing. Prefixing with rtk leads to random auto-mode denials from time to time too (this is independent of disabling savings stat persistence). Honestly have no idea why anyone who knows the first thing about CLIs would take rtk gain seriously. I guess clueless vibe coders who has hardly ever worked in a terminal before will look at the stat and feel good about it? That said, rtk is still mildly useful for compressing repeated test run outputs and stuff, but you should only ever use it on whitelisted commands; wrapping everything like they suggest you to do is just stupid.
- RIMR 22d agoThis is my first time hearing about RTK, and yikes! The benchmarks mean nothing; this thing is actively dangerous to use. If my agent runs a shell command, show my agent the output of that shell command. I don't have a problem with automatically pruning or paginating large outputs, as long as the agent still has some form of access to the original output (e.g., by searching). But removing verbosity from an 'ls' command? That's ridiculous! If my agent runs `ls -la`, do not drop the owner and date from the output, because THAT'S AN EXPECTED PART OF THE OUTPUT. Nothing should be trying to predict the agent's intent and interfering, unless you want your agent to get confused and fail. These LLMs were trained on predictable shell behavior, and RTK deliberately subverts the model's expectations. There's no way that isn't degrading the model's capability.
- stephantul 22d agoI think anyone who is even a little bit realistic knows that most technologies overclaim, or evaluate under very favorable conditions. This is not a good thing of course, but I also feel that acting surprised that this is going on is a little unnecessary. Having said that: most tools are not helpful
- kriskrunch 22d agoOpen question, how does this instruction in agent-rules.md look? "Cap large/unknown command output: `COMMAND 2>&1 | head -c 4000`. Never stream full logs, tests, or large files." I use that instead of RTK. Empirically, I found RTK makes my agents run longer to complete similar tasks. Ponytail and Caveman seem to help somewhat.
- woadwarrior01 22d ago[flagged]
- monneyboi 22d agoI use treesitter to build outlines of files and directories: https://github.com/resolveworks/trace https://github.com/resolveworks/trace Have not benchmarked it, the intent is mostly to save time, not necessarily tokens. I noticed that the models need a lot of toolcalls to ground themselves, and often have trouble with getting an overview.
- patriciobcs 22d agoIt seems to depend on the task you are doing. In my case, I use a lot gh and docker CLI, and RTK has save me a lot of usage there. However, using other others CLIs like grep or git doesn’t seem to help that much. I use it mainly for security reviews of codebase and for this particular use case it helps a lot.
- sumandebnath944 21d ago[flagged]
- drgo 21d agoI too did not see any benefit from using rtk for general bash commands. But it saves between 3 and 7% when used with more verbose build/test tools e.g., cargo.
- patrick_rtk 19d agoPatrick here, I build rtk. The `rtk find` loop was fixed in 0.46.0. That's the pattern: it gets reported, we fix it. We published our own reproduction after the JetBrains run: https://www.rtk-ai.app/blog/rtk-on-skillsbench/ https://www.rtk-ai.app/blog/rtk-on-skillsbench/ Bash output is a small share of the bill, so that's the ceiling, and turn-count variance is bigger than the ceiling. Your 7% terminal share on Fable is the same finding from the other direction. We're finalizing an integrated proxy inside rtk itself.
- devthinker-ai 19d ago[dead]
- Jzuckerman 19d ago[flagged]