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.
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.
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
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.
pip install prompt-tokensaver
The command is tokensaver. Requires Python 3.10 or newer. Works on Windows, macOS and
Linux.
tokensaver count prompt.txt --model gpt-4o
tokensaver count "Summarize this article in three bullet points."
tokensaver compare prompt.txt
tokensaver compare prompt.txt --models gpt-4o,claude-sonnet-5-5,gemini-2.5-flash
tokensaver optimize prompt.txt
tokensaver optimize prompt.txt --apply all --output prompt.optimized.txt
tokensaver optimize prompt.txt --apply json,whitespace,filler -o prompt.optimized.txt
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.
Run tokensaver <command> --help for the same information in your terminal.
INPUT can be any of:
| You pass | tokensaver reads |
|---|---|
prompt.txt orC:\prompts\system.md |
the file (UTF-8; a byte-order mark is ignored) |
- |
text piped in on stdin |
"Summarize this in 3 bullets." |
the text itself |
A mistyped file name like promt.txt is an error, not silently counted as text.
Token count and input cost on one model.
| Option | What it does |
|---|---|
--model ,-m |
Model to price against. Default: gpt-4o . Seetokensaver models for all names. |
--prices FILE |
Use your own prices.json instead of the bundled one. |
Tokens and input cost on many models, cheapest first.
| Option | What it does |
|---|---|
--models a,b,c |
Only these models (comma-separated). Default: all of them. |
--prices FILE |
Use your own prices.json . |
Suggestions with the tokens and dollars each one saves, a before/after diff and the total.
| Option | What it does |
|---|---|
--model ,-m |
Model to measure savings on. Default: gpt-4o . |
--apply safe |
Default. Applies only the safe rules (json ,whitespace ). Review rules are listed with their savings but not applied. |
--apply all |
Applies every rule. Read the suggestions first. |
--apply none |
Applies nothing; just shows the suggestions. |
--apply json,duplicates |
Applies only the rules you list. Names: json ,whitespace ,duplicates ,repeated-instructions ,filler ,long-examples . |
--output FILE ,-o FILE |
Saves the optimized prompt to FILE . Without it, nothing is written. The input file isnever overwritten. |
--force |
Lets --output replace a file that already exists. It still refuses to overwrite the input file. |
--no-diff |
Hides the before/after diff (it is shown by default). |
--prices FILE |
Use your own prices.json . |
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
models lists every supported model with its prices and whether counts are exact.
version prints the installed version.
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.
| Rule | Risk | What it does |
|---|---|---|
json |
safe | Minifies pretty-printed JSON. Only whitespace outside strings is removed; the result is re-parsed and compared, so keys, values and number formats stay exactly the same. |
whitespace |
safe | Removes trailing spaces, extra blank lines and blank lines at the start and end. Indentation and spaces inside lines are kept. |
duplicates |
review | Finds duplicate lines and duplicate or near-duplicate (≥90% similar) paragraphs. |
repeated-instructions |
review | Finds the same instruction given twice in different words, such as "Always respond in JSON" and "Remember to always respond in JSON". |
filler |
review | Removes polite filler ("I would like you to please…", "As an AI language model…", "Thanks in advance!") and shortens wordy phrases ("in order to" → "to"). |
long-examples |
review | Flags few-shot examples over ~150 tokens and prompts with more than 3 examples. Only flags them; it never edits. |
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.)
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.
| Model | Provider | Input / 1M | Output / 1M | Token count |
|---|---|---|---|---|
| gpt-6-astra | OpenAI | $10.00 | $50.00 | estimated |
| gpt-6.1-sol | OpenAI | $2.00 | $10.00 | estimated |
| gpt-6-luna | OpenAI | $0.10 | $0.50 | estimated |
| gpt-5.5 | OpenAI | $5.00 | $30.00 | exact |
| gpt-5.4-mini | OpenAI | $0.75 | $4.50 | exact |
| gpt-5-mini | OpenAI | $0.25 | $2.00 | exact |
| gpt-4.1 | OpenAI | $2.00 | $8.00 | exact |
| gpt-4.1-mini | OpenAI | $0.40 | $1.60 | exact |
| gpt-4o | OpenAI | $2.50 | $10.00 | exact |
| gpt-4o-mini | OpenAI | $0.15 | $0.60 | exact |
| claude-fable-5-1 | Anthropic | $10.00 | $50.00 | estimated |
| claude-opus-5-5 | Anthropic | $4.00 | $20.00 | estimated |
| claude-sonnet-5-5 | Anthropic | $2.00 | $10.00 | estimated |
| claude-haiku-5-5 | Anthropic | $0.10 | $0.50 | estimated |
| claude-haiku-4-5 | Anthropic | $1.00 | $5.00 | estimated |
| gemini-4-argon | $2.00 | $10.00 | estimated | |
| gemini-3.8-flash | $0.75 | $3.75 | estimated | |
| gemini-3.1-pro-preview | $2.00 | $12.00 | estimated | |
| gemini-2.5-flash | $0.30 | $2.50 | estimated | |
| gemini-2.5-flash-lite | $0.10 | $0.40 | estimated |
Run tokensaver models to see the list for your installed version.
Please read this before relying on the numbers.
-
Exact vs estimated. Counts are exact only for OpenAI models that
tiktokenmaps to a tokenizer (shown as "exact"). Claude, Gemini and GPT-6 have no offline tokenizer, so tokensaver counts with OpenAI'so200k_baseand multiplies by a per-model factor fromprices.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,compareandoptimizeprice 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_updateddate in the output. Some listed prices are scheduled to change (see thenotesfield inprices.json). -
Languages. Token counting and the
json,whitespaceandduplicatesrules work in every language (tested on 55, see above). Thefillerandrepeated-instructionsrules, 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.
-
Calibrate the Claude and Gemini estimate factors against official token counters
-
--jsonoutput 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.
⭐ 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.
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
☕ tokensaver is free and MIT licensed. If it saves you money, you can buy me a coffee.