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.

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.

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).
• 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

License

MIT