tokensaver

Find out what your prompt costs on every major AI model, and exactly which changes make it cheaper.

Most token counters stop at a number. tokensaver tells you what to change: every suggestion comes with a reason, the tokens it saves, and what that is worth in dollars. It runs 100% offline. Your prompt never leaves your machine.

🌍 Works in every language

tokensaver works with prompts in any language and any script. Token counting, cost comparison, JSON minification, whitespace cleanup and duplicate detection are tested in CI on 55 languages on every commit (see the test):

Arabic · Bengali · Bulgarian · Burmese (Myanmar) · Chinese (Simplified) · Chinese (Traditional, Hong Kong) · Chinese (Traditional, Macau) · Chinese (Traditional, Taiwan) · Croatian · Czech · Danish · Dutch · English · Estonian · Finnish · French · German · Greek · Hebrew · Hindi · Hungarian · Indonesian · Italian · Japanese · Kannada · Khmer · Korean · Lithuanian · Malay · Malayalam · Marathi · Nepali · Nigerian Pidgin · Norwegian · Persian (Farsi) · Polish · Portuguese (Brazil) · Portuguese (Portugal) · Punjabi (Gurmukhi) · Romanian · Russian · Serbian (Cyrillic) · Slovak · Slovenian · Spanish · Swahili · Swedish · Tagalog (Filipino) · Tamil · Telugu · Thai · Turkish · Ukrainian · Urdu · Vietnamese

Right-to-left scripts (Arabic, Hebrew, Persian, Urdu) and languages without spaces between words (Chinese, Japanese, Thai, Khmer, Burmese) included. The filler rule still speaks only English. Native speakers: add your language's filler phrases in #5.

Before / after

examples/messy_prompt.txt is a typical customer-support system prompt: polite filler, a rule written twice, a paragraph pasted twice, and pretty-printed JSON.

$ tokensaver optimize examples/messy_prompt.txt --apply all

  #  Suggestion                                                    Tokens  $/1k calls  Status
  1  Minify JSON block (140 chars, was 185)  line 13                  -18     $0.0450  applied
  2  Minify JSON block (200 chars, was 392)  line 39                  -43     $0.1075  applied
  3  Duplicate line: “- Never share internal information…”  line 30   -12     $0.0300  applied
  4  Duplicate paragraph: “Please note that you must only…”  line 34  -41     $0.1025  applied
  5  Repeated instruction: “Make sure you keep the answer…”  line 32  -12     $0.0300  applied
  7  Filler: “I would like you to…” (×1)  line 4                      -6     $0.0150  applied
 ...
 15  Long example (~198 tokens)  line 60                             ≤198     $0.4950  flag

╭──────────────────────── Total savings on gpt-4o ─────────────────────────╮
│ Before: 834 tokens                                                       │
│ After:  665 tokens                                                       │
│ Saved:  169 tokens (20.3%) = $0.000423 per call, $0.4225 per 1,000 calls, │
│ $422.50 per 1M calls                                                     │
╰──────────────────────────────────────────────────────────────────────────╯

What changed in the prompt:

-I would like you to please read the customer's message carefully and answer their question.
-As an AI language model, you should always be polite, friendly and professional.
+Read the customer's message carefully and answer their question.
+You should always be polite, friendly and professional.
...
-{
-    "answer": "string",
-    "category": "billing | technical | account | other",
-    ...
-}
+{"answer":"string","category":"billing | technical | account | other","confidence":0.0,...}
...
-- Never share internal information, discount codes or employee names.
-- Make sure you keep the answer under 120 words.

With only the default safe rules (no wording changes at all), the same prompt drops from 834 to 772 tokens. tokensaver then tells you that the review suggestions would save 107 more, so you decide.

Install

pip install prompt-tokensaver

The command is tokensaver. Requires Python 3.10 or newer. Works on Windows, macOS and Linux.

Quick start (2 minutes)

# 1. How many tokens, and what does one call cost?
tokensaver count prompt.txt --model gpt-4o
tokensaver count "Summarize this article in three bullet points."

# 2. Which model is cheapest for this prompt?
tokensaver compare prompt.txt
tokensaver compare prompt.txt --models gpt-4o,claude-sonnet-5-5,gemini-2.5-flash

# 3. What should I change? (safe changes are applied, others are suggested)
tokensaver optimize prompt.txt

# 4. Apply everything you agree with and save a copy (the original is never overwritten)
tokensaver optimize prompt.txt --apply all --output prompt.optimized.txt
tokensaver optimize prompt.txt --apply json,whitespace,filler -o prompt.optimized.txt

# Pipe text in with "-", list models and prices
cat prompt.txt | tokensaver count -        # PowerShell: Get-Content prompt.txt | tokensaver count -
tokensaver models

Windows paths work as usual: tokensaver optimize examples\messy_prompt.txt.

Commands and options

Run tokensaver <command> --help for the same information in your terminal.

Input (every command)

INPUT can be any of:

A mistyped file name like promt.txt is an error, not silently counted as text.

tokensaver count INPUT

Token count and input cost on one model.

tokensaver compare INPUT

Tokens and input cost on many models, cheapest first.

tokensaver optimize INPUT

Suggestions with the tokens and dollars each one saves, a before/after diff and the total.

Common combinations:

tokensaver optimize prompt.txt                                   # look first: safe fixes only, nothing saved
tokensaver optimize prompt.txt --apply all -o prompt.min.txt     # apply everything, save a copy
tokensaver optimize prompt.txt --apply json,whitespace,duplicates -o prompt.min.txt
tokensaver optimize prompt.txt -m claude-sonnet-5-5 --no-diff    # savings on Claude, short output
tokensaver optimize prompt.txt --apply all -o prompt.min.txt --force   # re-run and replace the copy

tokensaver models and tokensaver version

models lists every supported model with its prices and whether counts are exact. version prints the installed version.

Optimizer rules

Each rule is a small module in src/tokensaver/optimizer/. Safe rules cannot change meaning and are applied by default. Review rules are shown with their savings and applied only when you ask (--apply all or --apply <rule,...>). Code blocks are never changed. Few-shot examples are never reworded.

Every saving is measured: tokensaver counts the prompt before and after each change on the model you chose. Changes that save nothing on that model are not listed. (Safe whitespace fixes that save 0 tokens are still applied for a tidy file, and reported in one line.)

Supported models

Prices are USD per 1M tokens, standard tier, as of 2026-10-26. They live in src/tokensaver/data/prices.json with a source URL for each model. Edit that file, or pass your own with --prices my_prices.json.

Run tokensaver models to see the list for your installed version.

Limitations

Please read this before relying on the numbers.

• Exact vs estimated. Counts are exact only for OpenAI models that tiktoken maps to a tokenizer (shown as "exact"). Claude, Gemini and GPT-6 have no offline tokenizer, so tokensaver counts with OpenAI's o200k_base and multiplies by a per-model factor from prices.json. These are always shown with ~ and labelled "estimated". The factors are rough heuristics, not measurements; real counts can differ by 10–20% or more, especially for code and non-English text. For exact counts, use the provider's own token-counting API.
• Input cost only. count, compare and optimize price the prompt you send. Output tokens, cached-input discounts, batch discounts and long-context surcharges (for example above 200K tokens on Gemini 3.1 Pro or 100K on Claude Haiku 5.5) are not included.
• Prices go stale. Check the last_updated date in the output. Some listed prices are scheduled to change (see the notes field in prices.json).
• Languages. Token counting and the json, whitespace and duplicates rules work in every language (tested on 55, see above). The filler and repeated-instructions rules, and "Example 1:" detection, currently understand English only. Estimates for Claude and Gemini are less reliable for non-English text.
• Message overhead is not counted. Chat APIs add a few tokens per message for roles and formatting. tokensaver counts the text you give it.
• Rules are heuristics. Review suggestions can be wrong: a repeated sentence can be intentional emphasis, and "duplicate" lines can be real data. That is why they are never applied unless you ask.
• Offline by design. tokensaver makes no network calls. The tokenizer files ship inside the package and are checked against their published SHA-256 hashes.

Roadmap

• Calibrate the Claude and Gemini estimate factors against official token counters
• --json output for CI pipelines and scripts
• Output-token and prompt-caching cost estimates
• Directory mode: scan all prompts in a project
• More rules: verbose markdown, long URLs, unused template variables
• GitHub Action that comments token savings on pull requests

Ideas and votes are welcome in the issues.

How to contribute

⭐ If tokensaver saved you tokens, a star helps other developers find it.

Contributions are very welcome: new rules, better heuristics, price updates and bug reports. Read CONTRIBUTING.md for the 5-minute setup. Each rule is one small file with its own tests, so it is a good first contribution.

Need help cutting your AI costs? Work with me

tokensaver catches the easy wins. If your AI bill is growing and you want a deeper review of prompts, model choice, caching and architecture, I can help.

👉 Get in touch or email info@axieo.ca

Support tokensaver

☕ tokensaver is free and MIT licensed. If it saves you money, you can buy me a coffee.

License

MIT