llm-tokens-atlas 0.0.1

Open benchmark of LLM tokenization across 5 providers — offline vs empirical calibration deltas.

llm-tokens-atlas

Open benchmark of LLM tokenization — offline vs empirical calibration deltas. v0.1.0 ships 3 providers (Anthropic, OpenAI, Mistral); Google + Cohere pending for v0.2.0.

What this is

llm-tokens-atlas is a reproducible, open dataset and analysis pipeline that measures how offline tokenizers (e.g. tiktoken proxies, the published BPE vocabularies) compare against the empirical token counts returned by each provider's own API or OSS tokenizer. v0.1.0 covers 3 providers (Anthropic claude-opus-4-7, OpenAI gpt-4o, Mistral mistral-large-latest), 5 prompt formats (Markdown, XML, JSON, YAML, plain text), and 7,485 real-world prompt requests (499 unique prompts × 5 formats × 3 providers; n=2,495 per provider) drawn from open corpora. Google gemini-2.5-pro and Cohere command-r parity sweeps are scheduled for v0.2.0 — the schema is already validated for both providers; the only missing piece is the empirical sweep.

The output is a per-provider, per-format calibration delta distribution — so anyone estimating cost or context-window budgets ahead of an API call can quantify the bias of their offline counter instead of treating it as exact.

This project builds on tokenometer, which surfaced the underlying methodology and a notable preliminary finding: cl100k_base underestimates claude-opus-4-7 tokens by ~62% (median).

Status

v0.1.0 (released 2026-05-11) — 3-provider coverage (Anthropic + OpenAI + Mistral). Schema, drivers, and data are stable for the three shipped providers; Google + Cohere rows will be added in v0.2.0 without a breaking schema change.

Headline findings (v0.1.0)

Released 2026-05-11. n = 7,485 rows (2,495 per provider). Detailed numbers live in analysis/results.json.

Provider	Model	Median offline-vs-empirical delta	OLS slope	R²
anthropic	claude-opus-4-7	+41.3% (cl100k_base underestimates)	1.611	0.9956
openai	gpt-4o	0.0% (tiktoken-as-truth oracle, mean +3.0%)	1.024	0.9986
mistral	mistral-large-latest	−0.1% (mistral-tokenizer-js, mean +1.9%)	1.016	0.9993

The Anthropic row is the headline: the publicly-recommended offline tokenizer underestimates real claude-opus-4-7 cost by ~41% across thousands of prompts, and 100% of rows underestimate (no exact / overestimate cases). OpenAI and Mistral are baselines confirming the offline-vs-empirical pipeline is calibrated correctly when the provider's own tokenizer is the oracle.

Install

TBD — see pyproject.toml. The recommended workflow uses uv:

uv sync

Usage

TBD. See llm_tokens_atlas/ for collection and counting drivers, and analysis/notebooks/ for plots.

Reproducing results

make reproduce

This regenerates the dataset from scratch. Tokenizer and provider API versions are pinned (see data/lockfile.json once published).

See docs/REPRODUCING.md for full instructions — required API keys per provider, expected runtime at each scale, output sizes, and a CI-friendly tiny variant (make reproduce-tiny).

Tokenometer integration

Atlas reuses tokenometer's multi-provider tokenizer logic (5 providers supported upstream; Atlas v0.1.0 exercises 3 of them — Anthropic, OpenAI, Mistral — with Google + Cohere exercises arriving in v0.2.0) instead of reimplementing it in Python. The integration lives in a single module:

• llm_tokens_atlas/tokenometer_bridge.py — Python facade over the tokenometer CLI. Exposes count_offline, count_empirical, list_providers, list_models, list_formats, plus a count_offline_batch / count_empirical_batch pair for the high-throughput atlas pipeline.
• llm_tokens_atlas/install_tokenometer.sh — idempotent installer; make install runs it. Finds tokenometer via (1) tokenometer on PATH, (2) a sibling ../tokenometer/ repo build, (3) builds the sibling if source is present, or (4) fails with an install hint.

Any new Python code that needs token counts should import from llm_tokens_atlas.tokenometer_bridge. Do not invoke the tokenometer CLI directly from other modules.

Publishing the dataset

The canonical home for the dataset is https://huggingface.co/datasets/faraa2m/llm-tokens-atlas. The Hugging Face dataset card lives at data/README.md. The upload script is llm_tokens_atlas/publish_to_hf.py; set HF_TOKEN in your env and run it with --dataset llm-tokens-atlas.

Reproducing

• docs/REPRODUCING.md — make reproduce mechanics + expected runtime.

Citation

Released 2026-05-11. Cite as v0.1.0 (3-provider coverage). Coverage will expand to 5 providers in v0.2.0 (Google + Cohere); cite the version you used. Until the paper is on arxiv, cite the GitHub repo and the HuggingFace dataset directly:

@misc{llm-tokens-atlas-2026,
  author       = {Faraazuddin Mohammed},
  title        = {{llm-tokens-atlas}: An Open Benchmark of LLM Tokenization Calibration},
  year         = {2026},
  version      = {v0.1.0},
  howpublished = {\url{https://github.com/faraa2m/llm-tokens-atlas}},
  note         = {3-provider coverage (Anthropic, OpenAI, Mistral); v0.2.0 adds Google + Cohere. Companion arxiv preprint forthcoming.}
}

License

• Code: Apache-2.0
• Data (everything under data/): CC-BY-4.0