Overrun

Why your LLM bill does not match your estimate. Overrun estimates what a request will cost before you send it, counts it with real tokenizers in your browser, prices it with integer nano-dollars, and reconciles the estimate against the usage the provider reports. Spec: docs/PRD.md.

Live: https://overrun-ashy.vercel.app (no key, no upload needed: demo mode loads a real usage log).

Findings

From results/study.json (full tables in RESULTS.md):

• The 4-characters rule underestimates Tamil by 5.35× on cl100k_base (0.74 characters per token).
• Tamil on cl100k_base is 5.35× (0.74 characters per token) against 1.41× (2.8 characters per token) on o200k_base.
• For English news prose on o200k_base it overestimates: 0.84× (4.68 characters per token).
• In 50 real free-tier requests, openai/gpt-oss-20b on Groq reported 71 more input tokens than the o200k_base count of the text (74 with a system prompt), on every request. gemini-3.1-flash-lite reported 1 more than the Gemma 3 count, on every request.
• Held-out reconciliation: with that overhead measured on a fixed random half of the 50 requests (seed 20261027), the named reasons explain 100% of the input-token gap on the other 25 (0 of 1067 tokens unexplained).

What is inside

Req	Where
R1 estimator (Unicode chars, ceil division, labelled "estimate")	site/lib/estimate.ts
R2 tokenizers in the browser: o200k_base, cl100k_base (js-tiktoken), Llama 3.1, Gemma 3 (@huggingface/transformers), loaded on demand	site/lib/tokenizers.ts
R3 usage validator / normaliser for OpenAI, Anthropic, Gemini, Groq	site/lib/usage.ts
R4 integer nano-dollar pricing (BigInt, checked u64), dated rate-card fixtures with sources	site/lib/pricing.ts, site/lib/ratecards.ts
R5 budget ledger and replay	site/lib/ledger.ts
R6 reconciliation with reasons, CSV / Markdown export	site/lib/reconcile.ts, /reconcile
R7 study: corpus, measurement, real usage check	study/, results/, /findings
R8 demo mode: the 50 real requests from the study as a bundled log	site/public/data/demo-log.json

Rate cards are dated fixtures copied from each provider's pricing page on 2026-10-26 or 2026-10-27 (each card carries its own date and source). They are not current prices; edit them on the site.

The site follows the "Fog & Signal" design handoff (Direction A): Instrument Serif for headlines, JetBrains Mono with tabular figures for numbers, light and dark themes, and a thermal receipt that prints each estimate (the animation is skipped under prefers-reduced-motion).

Run it

cd site && npm install && npm test && npm run dev

Rebuild the study (tokenizer measurements are free; the real usage check needs GROQ_API_KEY and GEMINI_API_KEY in .env, uses free tiers, caches every call in study/cache/ and resumes after a quota stop):

cd study && npm install && npm run corpus && npm run study

Corpus licences

Parallel prose: NTREX-128 (CC BY-SA 4.0), 15 lines in each of 10 languages. Code and markdown: the original course project (MIT) and this repo (MIT). JSON and emoji posts: written for this study (MIT). Each sample in study/corpus.json carries its source and licence.

Credit

Overrun grows out of "Token Counter and Cost Meter" from AI Engineering from Scratch by Rohit Ghumare (MIT). Its four stages (estimate, validate usage, integer pricing, budget ledger) and their unit tests are ported to TypeScript in site/lib/core.test.ts, and its example usage log is a test fixture.