Token Calculator for Developers
A web-based calculator is fine for a quick check, but once token counting matters to your actual product — enforcing a context budget, catching an oversized prompt before it fails in production, or writing a test that guards against regressions — you need it running in your own code. This page covers the libraries and patterns for counting tokens programmatically, alongside Growfiy's calculator for fast manual checks.
Built for developers wiring token awareness directly into their codebase, CI pipeline, or request-handling logic.
AI Token Cost Calculator
Estimate your AI API costs before you spend. Compare GPT, Claude, and Gemini pricing in seconds.
$2.5/1M input tokens · $10/1M output tokens · Context: 128,000 tokens
Used to project your monthly cost (assumes 30 days).
Input Cost
$0.0025
Output Cost
$0.0050
Total Cost / Request
$0.0075
Projected Monthly Cost
Based on your requests/day input
$22.50
Compare All Models
Cost for the same 1,000 input / 500 output tokens, cheapest first.
| Model | Provider | Input Cost | Output Cost | Total Cost | Context Window |
|---|---|---|---|---|---|
| Gemini 1.5 FlashCheapest | $0.0001 | $0.0002 | $0.0002 | 1,000,000 | |
| GPT-4o mini | OpenAI | $0.0002 | $0.0003 | $0.0005 | 128,000 |
| Claude Haiku | Anthropic | $0.0008 | $0.0020 | $0.0028 | 200,000 |
| Gemini 1.5 Pro | $0.0013 | $0.0025 | $0.0038 | 1,000,000 | |
| GPT-4o | OpenAI | $0.0025 | $0.0050 | $0.0075 | 128,000 |
| Claude Sonnet | Anthropic | $0.0030 | $0.0075 | $0.0105 | 200,000 |
| Claude Opus | Anthropic | $0.0150 | $0.0375 | $0.0525 | 200,000 |
Counting Tokens in Your Own Code
For OpenAI models, the tiktoken library encodes text locally using the exact tokenizer the model uses, so you get a precise count without an API round-trip:
enc = tiktoken.encoding_for_model("gpt-4")
token_count = len(enc.encode(prompt_text))
Anthropic's SDK exposes a similar token-counting method for Claude models, and most other providers return exact prompt and completion token counts in the usage field of every API response — useful for logging actual usage even if you estimated beforehand.
Guardrails: Tests and Checks Worth Adding
- Unit test on assembled prompts: encode your full system prompt template and assert it stays under a target token budget, so a future edit that quietly bloats it gets caught in CI instead of production.
- Pre-request truncation logic: count tokens for the full assembled request before sending it, and truncate or summarize older context if it's approaching the model's context limit.
- Logging real usage alongside estimates: store the token counts returned in each API response so you can compare actual usage against your local estimates and catch drift over time.
- Alerting on abnormal token counts: flag requests where token count is far outside your typical range, since this often signals a bug (like unbounded history) rather than normal variation.
Frequently Asked Questions
How do I count tokens in Python before sending a request?
For OpenAI models, the tiktoken library lets you encode a string locally and get an exact token count offline, without an API call. Anthropic's SDK also exposes a token-counting method for Claude models, so you can check length before sending a request rather than after it fails.
Is there a JavaScript/Node.js equivalent for counting tokens?
Yes — there are JS ports of tiktoken-style tokenizers available as npm packages for OpenAI-compatible counting, and most provider SDKs expose usage data in the API response even if a pre-request local counter isn't available for every model.
Should I add a token-limit check to my test suite?
For any feature with a fixed or templated prompt structure, a simple unit test that encodes the fully-assembled prompt and asserts it stays under your target token budget catches regressions early — for example, when someone adds a new field to a system prompt without checking the impact.
How do I prevent a request from silently exceeding the context window?
Count tokens for your assembled prompt (system instructions + history + new input) before sending the request, and truncate or summarize the oldest content if the total approaches the model's limit — catching this in code is more reliable than waiting for an API error.
Do I need the exact tokenizer, or is an estimate good enough?
For hard limits — like staying under a context window — use the exact tokenizer library for that model family, since a rough estimate can undercount and cause a request to fail. For cost budgeting and reporting, an estimate is usually accurate enough.
Where can I quickly sanity-check a token count without writing code?
The calculator above is built for exactly that — paste your text, pick a model, and get an instant estimate without setting up a library, which is useful for a quick check during prompt design before you wire up local tokenization in your codebase.