AI Token Calculator
Estimate token count and API cost for GPT-4o, Claude, Gemini, and Llama models. Paste text or enter word count to calculate input/output costs across all
AI Token Calculator
Calculator
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Formula: Tokens ≈ Words × 1.33 | Cost = (Tokens / 1,000,000) × Price per 1M Tokens
Worked example — ~$0.01 per request — processing 1,000 blog posts would cost about $10
Formula
Tokens ≈ Words × 1.33 | Cost = (Tokens / 1,000,000) × Price per 1M Tokens
English text averages about 1.33 tokens per word (varies by model and content). API costs are calculated separately for input and output tokens at per-million-token rates. Output tokens are typically more expensive due to sequential generation.
Worked Examples
Example 1: Blog Post Analysis with GPT-4o
Problem:Estimate the cost to process a 1,000-word blog post with GPT-4o, expecting a 500-word summary output.
Solution:Input: 1,000 words × 1.33 = 1,330 tokens Output: 500 words × 1.33 = 665 tokens Input cost: 1,330/1M × $2.50 = $0.003325 Output cost: 665/1M × $10.00 = $0.006650 Total: $0.009975
Result:~$0.01 per request — processing 1,000 blog posts would cost about $10
Example 2: Cost Comparison: GPT-4o vs Claude Haiku
Problem:Compare costs for 10,000 API calls with 500 input tokens and 200 output tokens each.
Solution:GPT-4o: (5M/1M × $2.50) + (2M/1M × $10.00) = $12.50 + $20.00 = $32.50 Claude Haiku: (5M/1M × $0.25) + (2M/1M × $1.25) = $1.25 + $2.50 = $3.75
Result:Claude Haiku is ~8.7× cheaper ($3.75 vs $32.50) for this workload
Frequently Asked Questions
What is a token in AI/LLM context?
A token is a chunk of text that language models process. Tokens can be whole words, parts of words, or individual characters. For English text, 1 token ≈ 0.75 words (or equivalently, 1 word ≈ 1.33 tokens). The word 'hamburger' might be split into 'ham', 'bur', 'ger' (3 tokens). Common words like 'the' or 'is' are typically 1 token. Tokenization varies by model — different models use different tokenizers (BPE, SentencePiece, etc.).
How are API costs calculated?
LLM API costs are based on token usage with separate rates for input (prompt) tokens and output (completion) tokens. Output tokens are typically 2-5× more expensive because they require sequential generation (autoregressive decoding). Costs are quoted per million tokens. Total cost = (input tokens × input rate) + (output tokens × output rate). Caching, batching, and fine-tuned models may have different pricing.
Why do input and output token prices differ?
Output tokens cost more because generating each output token requires a full forward pass through the model, and tokens must be generated sequentially (each depends on all previous tokens). Input tokens can be processed in parallel through the transformer layers. This computational asymmetry — parallel input processing vs. sequential output generation — is why output tokens are 2-5× more expensive.
How can I reduce API costs?
Key strategies: (1) Use smaller models for simple tasks (GPT-4o mini, Claude Haiku, Gemini Flash). (2) Minimize prompt length — remove unnecessary instructions. (3) Use prompt caching for repeated prefixes. (4) Set max_tokens to limit output length. (5) Use batch APIs for non-real-time tasks (often 50% cheaper). (6) Fine-tune smaller models for specific tasks. (7) Consider open-source models (Llama) for high-volume use cases.
How do tokens-per-minute rate limits differ from the price per token?
Price and rate limit are separate constraints and they bind at different times. The per-million-token price decides what a workload costs; a tokens-per-minute quota decides how fast it is allowed to run, and prompt tokens, cached prefixes and generated tokens usually all count against it. A batch of requests that is comfortably affordable can still be throttled to a crawl, which is why bulk jobs are queued, retried with exponential backoff, or moved to a batch endpoint with a separate quota. Check both numbers before committing to a throughput target, since raising a quota is a support request rather than a code change.
Why does source code or non-English text use more tokens than the same word count of English prose?
Subword vocabularies are learned from corpora dominated by English prose, so ordinary English words map to a single token while everything else fragments. Punctuation, camelCase identifiers, indentation and long URLs each split into several pieces, pushing code and JSON toward 2-3 tokens per word. Scripts outside the Latin alphabet fall back to multi-byte sequences and can cost several tokens per character. Treat the 1.33 tokens-per-word default used here as an English-prose figure and raise it for anything else.
What is the difference between the context window and a max_tokens limit?
The context window is the total number of tokens the model can hold at once: system prompt, conversation history, retrieved documents, tool definitions and the response being generated all share it. max_tokens caps only the completion. If your prompt occupies 120,000 of a 128,000-token window, no max_tokens value can buy more than about 8,000 tokens of output. The usage bar on this page counts input tokens only, so leave headroom for the reply.
How does prompt caching change the cost arithmetic?
Caching stores the model's processed state for a prefix of the prompt so repeat requests skip prefill over that span. Matching runs from the very start of the prompt, so layout matters: put stable system instructions and reference documents first and the varying user turn last, or nothing matches. Cache reads are billed at a reduced input rate and some providers charge a premium for the write, so caching pays off when one prefix is reused many times within its lifetime.
When is a word-count estimate not good enough, and how do I get exact counts?
A heuristic is fine for capacity planning and for ranking models by cost, where a ten-percent error changes no decision. It is not fine for enforcing a hard context limit, chunking documents that must fit a fixed budget, or billing customers per token. For those, run the model's own tokenizer — a byte-pair encoding library for GPT-family models, a SentencePiece model for many Gemini and Llama derivatives — or call the provider's token-counting endpoint before sending the request.
How do I estimate the cost of a multi-turn conversation rather than a single request?
Chat completion APIs are stateless, so every turn resends the entire transcript. If each exchange adds roughly t tokens, the prompt at turn n is about (n-1) x t and the cumulative input across n turns is t x n x (n-1) / 2 — quadratic growth, not linear. A twenty-turn session therefore costs far more than twenty separate one-shot requests. Estimate the final turn's length on this page, then cap the growth with a rolling window, a running summary, or cached history.
References
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Reviewed for accuracy by Daniel Agrici, Founder & Lead Developer · Editorial policy
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