AI Token Calculator

How much does it cost to use AI APIs? Our token cost calculator multiplies your input and output token counts by the model's per-token pricing.

GPT-4o costs $2.50/$10 per million tokens (input/output), while Claude Sonnet 4 costs $3/$15.

Enter your text length and see exact costs instantly.

How many tokens is my text? English text averages 1.33 tokens per word. A 1,000-word prompt is about 1,333 tokens.

Code and non-English text may use more tokens. Our calculator converts words to tokens automatically and shows the cost for any AI model.

Which AI model gives the best value? Compare GPT-4o, GPT-4o Mini, Claude Opus, Claude Sonnet, Claude Haiku, and Gemini side by side.

Our LLM pricing calculator shows cost per call and monthly estimates so you can choose the right model for your budget and quality needs.

A token is the atomic unit of text that a large language model reads and generates, typically a subword fragment rather than a whole word.

Modern LLMs use byte-level Byte Pair Encoding (BPE), a compression method introduced by Sennrich et al. and refined in tokenizers like OpenAI's tiktoken and the Hugging Face tokenizers library. During BPE, frequent character sequences merge into single tokens, so common words become one token while rare or technical terms split into several.

Because text is Unicode-encoded (see the Unicode Consortium's UTF-8 standard, RFC 3629), a single emoji or CJK character can consume multiple tokens. Understanding this mapping explains why token counts rarely equal word counts and why cost estimates are approximate.

AI tokenization converts raw text into integer IDs using a fixed vocabulary learned during model training.

First, the text is normalized and encoded as UTF-8 bytes per RFC 3629 — and because most BPE vocabularies are case-sensitive, standardizing text with a case converter beforehand can subtly change which merges apply; then a Byte Pair Encoding or SentencePiece algorithm greedily merges byte pairs into the longest matching vocabulary entries. Each resulting token maps to an ID that the model embeds into a vector.

OpenAI documents this in its tiktoken library, and Google's Gemini and Anthropic's Claude use their own comparable subword schemes. Because vocabularies differ, identical text can yield different token counts across providers.

This is why our estimator uses the widely cited ~1.33-tokens-per-English-word ratio as a fast approximation rather than an exact per-model tokenizer.

AI API cost is calculated by multiplying your input and output token counts by each provider's published per-million-token rate, then summing them.

Providers such as OpenAI and Anthropic list separate input and output prices in their official pricing documentation, and output tokens usually cost several times more than input tokens because generation is computationally heavier.

The formula is: cost = (input tokens x input rate / 1,000,000) + (output tokens x output rate / 1,000,000). System prompts, few-shot examples, and retrieved context all count as billed input on every call.

Because prices change, always confirm current rates on the provider's official pricing page before finalizing a production budget.

To count tokens accurately, run your exact text through the model's own tokenizer rather than relying on a word-count ratio. For OpenAI models, the open-source tiktoken library returns precise counts; Anthropic and Google provide token-counting endpoints and SDKs in their official developer documentation.

A word-based estimate (words x 1.33) is fine for budgeting but can drift by 10-20 percent for code, JSON, URLs, or non-English scripts, which fragment into many small tokens.

Remember that the full request payload is tokenized, including system instructions, role delimiters, and function or tool definitions. For repeatable measurement, tokenize the assembled request exactly as your API client sends it.

Token counts differ across GPT, Claude, and Gemini because each model was trained with a distinct tokenizer vocabulary and merge rules, so the same sentence splits into different subword units.

OpenAI's newer models use larger vocabularies than older ones, which reduces token count for common English text, while non-Latin scripts may tokenize less efficiently on some models. Anthropic and Google publish their own tokenization behavior in their developer docs.

This variance means a prompt that is 1,000 tokens on one model might be 1,050 or 950 on another, changing both cost and context-window usage. Always benchmark with each provider's actual tokenizer when precision matters.

You can reduce LLM token usage by trimming input, capping output, and reusing computation:

• Shorten system prompts and few-shot examples, since they are re-billed on every request
• set a max-output-tokens limit to stop runaway generations
• route simple tasks to cheaper models

Prompt caching, offered in OpenAI and Anthropic official APIs, lets you avoid re-billing repeated context at full price, and batch endpoints often discount asynchronous jobs. Retrieval-augmented generation helps by sending only relevant chunks instead of entire documents.

Because output tokens are the most expensive component, concise, structured responses (for example, requesting JSON) usually deliver the largest savings without hurting quality.

Every model enforces a context window, the maximum combined input plus output tokens it can process in one request, and exceeding it causes truncation or errors.

Beyond capacity, treat prompts as a security surface: OWASP's Top 10 for LLM Applications lists prompt injection as the leading risk, where untrusted text smuggles instructions into your context and inflates token usage or hijacks behavior.

Never place secrets, API keys, or personal data in prompts, since providers may log requests and long contexts widen the attack surface. NIST's AI Risk Management Framework recommends bounding inputs and validating outputs.

Counting tokens before sending also helps you detect abnormally large, potentially malicious payloads early.

The most common mistake is estimating cost from word count alone and ignoring that code, JSON, punctuation, and non-English text tokenize far more densely than plain prose.

Others forget that system prompts, chat history, and tool definitions are billed as input on every single call, so multi-turn conversations compound quickly.

Many users also apply the same price to input and output despite output tokens costing several times more, and they overlook that pricing on official OpenAI, Anthropic, and Google pages changes over time.

Finally, assuming one provider's token count transfers to another leads to budget surprises. Always verify with the real tokenizer and current published rates.