Frugon – Find which LLM calls a cheaper model could handle (local, MIT)
I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week. I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks. I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and…
Frugon – Find which LLM calls a cheaper model could handle (local, MIT) launched on July 7, 2026 with 67 votes, #568 of 24,623 launches that month and more than 95% of that year's launches.
In plain words
Frugon is a CLI tool that analyzes OpenAI-style logs to identify which language model API calls could run on cheaper alternatives. It reads usage data locally, compares costs across models using LiteLLM pricing and LMArena performance tiers, and estimates potential savings from switching tasks to lower-cost models. Built with an MIT license, it helps developers reduce AI spending by revealing which routine operations like searches or scans waste the most budget on expensive models.
written from the facts on this page · September 2026
From the sources
Free, local, open-source LLM cost analyzer - see where your LLM bill leaks, on your machine. - Rodiun/frugon
In the maker’s words, at launch
I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week. I realised I had to do something about it else I'd have to double…
Does a similar job
all alternatives →
Frugal Tokens – explore costs and usage across coding agentsAug 2026 · demo.frugaltokens.com · ▲37I wanted to share a project I’ve been working on called Frugal Tokens. I originally built it because I was curious to see how much all of my sessions cost and how much cache misses affected that spend. I’d noticed people had widely different spend profiles and wanted to better understand what might contribute to that. As I’ve worked on this, the tool has grown to show more usage patterns across all of your sessions. It shows overall usage, estimated working time and overlapping sessions, and where your spend is coming from across models and cache misses. I also have a few session level…
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I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
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European Swallow AI2025 · europeanswallowai.com · ▲12Are you spending hundreds of dollars a month on AI coding costs? I built European Swallow AI, an API that uses reasoning models (Claude, Deepseek) for thinking and cheaper specialized coding models (Qwen, Grok) to write code, so you can save token costs while still getting high quality code. With an OpenAI formatted endpoint you can try European Swallow in Cursor, Typing Mind, Xibe AI and your own custom apps. During testing, European Swallow scored 80.5% on Big Code Bench and over 90% on the HumanEval+. It averaged $2.60 per million tokens compared with the $15 per million output tokens of…
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Source: Show HN post ↗ · launch text quoted in part
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