Numbers don’t lie, and this one made me stop scrolling: 2,737 tokens down to 1,764. Same task, same output, same model, just leaner input. That’s a 35.6% cut, and nobody touched the underlying codebase to get there. No fine-tuning, no swapping models for a cheaper tier, no trimming the actual request. Just the context around it got smarter.
Here’s the setup. This Reddit user built a small test project called ejemplo-token-firewall: one code file plus a 53-line log file where 48 of those lines were nearly identical noise, the kind of repeated stack trace or retry message you get when a service keeps failing the same way over and over. u/1982_miguel ran it twice, once raw and once through a tool called Mova Context, and measured the token count both times. The gap came entirely from stripping repeated log lines and bloated structure the model never needed in the first place. Think about how often that happens in a real debugging session: you paste in a log dump, half of it is the same warning firing on a loop, and the model has to read through all of it just to find the two lines that actually matter.
What makes this interesting isn’t the percentage. It’s the method. Most cost-cutting tricks either summarize context with another LLM call (which burns tokens to save tokens, and adds latency on top) or just tell you to “be concise” and hope the model listens. Neither approach is reliable, and the summarization route actually adds a point of failure since the summarizing model can drop details you needed. This one runs a deterministic algorithm that filters noise in microseconds, no model involved, so the savings are free in every sense. There’s also a Cache Layout Guard that keeps a stable prefix (agents, skills, prompt) so providers like Anthropic, OpenAI, or Gemini can actually trigger native prompt caching instead of missing it every run. That second piece matters more than it sounds. If your prefix shifts even slightly between calls, you lose the cache hit and pay full price again, so keeping that layout locked down is doing quiet work in the background.
Three ways to actually use this:
- Log-heavy debugging sessions. If you’re pasting stack traces or repeated log output into a chat window, that’s the exact scenario this was benchmarked on. Deduping near-identical lines before they hit the model is free money. Picture a Kubernetes pod crash-looping and dumping the same connection-refused error forty times before you catch it. You don’t need forty copies in context, you need one copy and a count.
- Scheduled or cron-driven agents. The tool ships a job engine (mova jobs start) for background runs, so if you’ve got agents firing on a schedule and re-sending similar context each time, this is where the savings compound. A daily report agent that reruns the same prompt shape every morning is paying the noise tax 365 times a year unless something upstream is filtering it.
- Multi-agent setups with shared context. With multi-agent orchestration via config.json, a stable cached prefix across agents means you’re not repaying the same token cost every time a new agent joins the conversation. In a pipeline where five agents each touch the same base context, that’s five separate chances to blow the cache, and this setup is built to keep all five landing on the same prefix.
Tips and pitfalls:
- 🔧 Don’t expect 35% on every project. The author is upfront that clean code or genuinely massive context will shrink the win. A tidy repo with no repeated logs simply doesn’t have the same fat to trim, so treat 35.6% as a best-case number from a noisy test file, not a baseline.
- 📊 Check the audit report before trusting the savings number. Real auditability was the whole design goal here, so use it. If a tool tells you it saved tokens but won’t show you which lines it dropped, that’s a red flag, not a feature.
- ⚠️ A circuit breaker aborts the call before it hits the API if you blow your budget. Worth configuring prices.json before you run anything at scale, especially if you’re wiring this into a cron job that runs unattended overnight and could otherwise rack up a surprise bill.
If you’re burning budget on repeated logs or bloated context and haven’t measured where those tokens are actually going, this is worth 20 minutes of your afternoon. Pull one of your own noisy log dumps, run it raw, then run it filtered, and just look at the delta yourself before deciding if it’s worth wiring in. The full writeup, the CLI setup, and the mova budget examples are in the original Reddit thread, go take a look.