Token Audit
See where your LLM budget really goes
Token Audit launched on August 11, 2026 with 2 votes, #5,788 of 23,274 launches that month.
What it does
Token Audit breaks every LLM API request into question / thinking / reply tokens and shows where your money goes. Reasoning models like o1 burn up to 77% of tokens on hidden thinking — at premium prices. The tool estimates costs across 14 models, fires alerts on wasteful patterns (high reasoning ratio, runaway automation), and its AI Advisor suggests cheaper alternatives. CSV import, spend by model/task, 14-day trend. Runs 100% locally — no sign-up, no API keys.
See where your LLM API budget really goes.Token Audit is a self-hosted tool that breaks every API request into question / thinking / reply tokens — and shows you exactly where your money disappears.Reasoning models (o1, DeepSeek-R1, o3) spend most of their tokens on hidden "thinking" — and you pay premium prices for it. Token Audit makes that waste visible.📊 FEATURES:• Token split visualization —
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Token price calculator for 400+ LLMs2024 · github.com · ▲268Hey HN! Tokencost is a utility library for estimating LLM costs. There are hundreds of different models now, and they all have their own pricing schemes. It’s difficult to keep up with the pricing changes, and it’s even more difficult to estimate how much your prompts and completions will cost until you see the bill. Tokencost works by counting the number of tokens in prompt and completion messages and multiplying that number by the corresponding model cost. Under the hood, it’s really just a simple cost dictionary and some utility functions for getting the prices right. It also accounts for…
Frugon – Find which LLM calls a cheaper model could handle (local, MIT)Jul 2026 · github.com · ▲67I 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…
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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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