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 |
LLM Token Calculator
If you're building with an LLM API, tokens aren't just a billing detail — they decide whether your prompt fits, how much history your chat can hold, and how your RAG chunks get split. Growfiy's LLM Token Calculator estimates token counts using each provider's actual tokenizer behavior, so you can check context window limits, size chunks correctly, and debug truncated responses before they hit production.
Built for developers working directly with OpenAI, Claude, Gemini, DeepSeek, Llama, and Mistral APIs — whether you're designing a RAG pipeline, fine-tuning a model, or just trying to keep a long chat session inside the context window.
How LLM Tokenization Actually Works
Most modern LLMs don't split text into whole words. Instead, they use subword tokenization — GPT models rely on a byte-pair encoding (BPE) scheme via tiktoken, while several other model families use SentencePiece or their own trained vocabularies. A common word might be a single token, while a rare word, a typo, or a technical term can be broken into two or three tokens.
This is why token counts don't map cleanly to word counts, and why the same input can tokenize differently depending on which model you send it to — the calculator accounts for this per-model difference instead of using a single universal estimate.
Context Window Planning & RAG Chunk Sizing
- Context window budgeting: add up system prompt, chat history, retrieved documents, and expected output tokens — if the total exceeds the model's limit, the oldest messages or retrieved context typically get dropped first.
- RAG chunk sizing: smaller chunks (200-500 tokens) improve retrieval precision but can lose surrounding context; larger chunks preserve context but reduce how many relevant passages fit in the window.
- Chat history truncation: long-running conversations need a strategy — sliding window, summarization, or hard cutoffs — to stay inside the context limit without losing critical earlier context.
- Fine-tuning datasets: knowing per-example token counts upfront helps estimate total training cost and catch oversized examples before a training run fails or gets truncated.
Frequently Asked Questions
Why does the same text produce different token counts on different LLMs?
Each model family uses its own tokenizer — GPT models use a BPE-based tokenizer (tiktoken), while others use SentencePiece or custom vocabularies. Different vocabularies split words, punctuation, and whitespace differently, so identical text can produce different token counts across models.
How do I know if my prompt will fit inside a model's context window?
Add your prompt tokens, any retrieved context or chat history, and your expected output tokens, then compare that total against the model's published context window. If the sum exceeds the limit, the request will either be truncated or rejected depending on the API.
What's a safe chunk size for RAG pipelines?
Most RAG setups use chunks between 200 and 500 tokens with some overlap (10-20%) to preserve context across boundaries. The right size depends on your embedding model's limits and how much surrounding context each retrieved chunk needs to stay meaningful on its own.
Does whitespace or formatting affect token count?
Yes. Extra line breaks, indentation, JSON formatting, and markdown syntax all consume tokens. Minifying structured input or removing unnecessary whitespace before sending it to the model can noticeably reduce token usage in high-volume applications.
How accurate is a token estimator compared to the actual API response?
A well-built estimator modeled on the correct tokenizer is typically within a few percent of the real count. For exact numbers before a production run, it's still good practice to check the `usage` field returned in the API response itself.
Do system prompts and function/tool definitions count toward the token limit?
Yes. System prompts, tool/function schemas, and any injected context all count as input tokens and are billed and counted the same way as the user's message, which is why verbose tool definitions can quietly eat into your available context window.