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A token is a unit created by an LLM’s tokenizer. It may be a character, part of a word, a complete short word, punctuation, whitespace-related text, or a model-specific structural unit. It is not a fixed number of words or characters. API billing normally multiplies the number of tokens in each reported category by that model and service tier’s rate, so the complete request and the generated response can cost more than a count of the visible prompt and answer suggests.
To estimate a call accurately, count the same structured payload with the provider’s tool, then reconcile the response’s usage fields and the provider’s billing records. A plain word count is useful only for a rough budget.
What a token actually is
An LLM does not read text as a human reader does. Before inference, a tokenizer converts the input into an ordered sequence of token IDs from that model’s vocabulary. The model processes those IDs, and generated IDs are converted back into text. OpenAI describes tokens as “the units that OpenAI models use to process text.” Google’s developer documentation similarly explains that the vocabulary is the set of tokens and tokenization is the process of splitting text into them.
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A token can be smaller or larger than a word
Token boundaries are learned from text statistics rather than from a dictionary. A common short word may be represented by one token, while a longer or less common word may be split into several pieces. OpenAI’s example splits “ tokenization” into “ token” and “ization.” Punctuation, numbers, line breaks, emoji, and fragments of code can each receive their own tokens or be combined with nearby text, depending on the encoding.
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That is why “one token equals one word” is wrong. A sentence with many uncommon names, source-code identifiers, symbols, or accented characters can use substantially more tokens than a sentence with the same number of familiar English words.
Token counts are model- and language-specific
The same string can produce different counts with different model families because each uses its own tokenizer or encoding. Language also matters: the ratio of characters or words to tokens changes across English, Chinese, Japanese, Arabic, code, and mixed-language text. Capitalization, spelling, inserted spaces, and punctuation can change boundaries even when the text looks nearly identical.
Provider rules of thumb are only planning estimates. OpenAI gives approximate relationships of about four characters per token and roughly three-quarters of an English word per token. Google gives similar guidance—about four characters and about 60–80 English words per 100 tokens. These are not conversion formulas, guarantees, or interchangeable figures across models.
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Input or prompt tokens
Input tokens are the content sent to the model. Depending on the endpoint, that can include system instructions, developer instructions, user messages, conversation history, and other text fields. Re-sending a long history on every turn increases input usage even when the latest user message is short.
Message structure and control data
A request is more than the visible text copied from a chat window. Roles, message boundaries, formatting wrappers, tool definitions, function schemas, and response-format instructions may be serialized into the model’s input. The exact overhead is endpoint- and model-specific. A local tokenizer applied only to a pasted prompt therefore tends to undercount a production request.
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Tools, schemas, files, and images
Tool declarations and JSON schemas can be large, especially when a tool exposes many parameters or detailed descriptions. Uploaded documents may be extracted and tokenized; an image may be processed through a separate vision representation rather than as ordinary text. Audio and other modalities can have their own usage measurements. OpenAI’s input-token-count endpoint accepts the Responses API input structure so that formatting and structured elements are included. Anthropic documents a Message Token Count API that accounts for tools, images, and documents. Use the provider’s equivalent rather than assuming a text-only count covers every modality.
Output is more than the visible answer
Completion or output tokens
Output tokens are the model-generated usage reported for the response. The visible text is a useful approximation only when the endpoint exposes no additional output categories and the response is ordinary text. Stop sequences, structured output wrappers, and other protocol details can make reported usage differ from a character count of what your application displays.
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Reasoning tokens
Some models perform internal reasoning that is not shown to the user. OpenAI reports reasoning tokens separately in usage details or includes them in output usage, depending on the endpoint and response format. They count toward output usage and billing even though they are absent from the visible answer. A concise displayed reply can therefore have a surprisingly high output-token count.
Billing categories you may see
Do not treat every token as one undifferentiated pool. Providers expose different categories, and a category may be priced differently or reported only on certain models and endpoints.
| Category | What it represents | Billing implication |
|---|---|---|
| Input or prompt tokens | Uncached content supplied in the request, including applicable message structure and control data | Usually multiplied by the model’s input rate |
| Cached input tokens | Previously processed input recognized by a provider’s prompt-caching system | Often reported separately and may have a lower rate than uncached input |
| Cache-write tokens | Input written into a cache for later reuse, where the service supports this category | May carry its own charge; availability and treatment vary |
| Output or completion tokens | Generated usage, potentially including non-visible reasoning tokens | Usually multiplied by a higher output rate than input |
| Audio, image, or other modality usage | Provider-specific measurements for non-text processing or generation | Can be separate billable categories rather than ordinary text tokens |
| Tool or external service usage | Calls to tools or services attached to an API workflow | May be priced outside the token formula |
A practical category-based estimate is:
estimated charge = (uncached input tokens × input rate) + (cached input tokens × cached-input rate) + (cache-write tokens × cache-write rate, if applicable) + (output tokens × output rate) + separately priced non-token services
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This is a planning model, not a universal invoice equation. The provider, model, endpoint, region, service tier, cache policy, and non-token features determine which terms exist and how they are charged. Pricing can change, so use the live official rate for the exact model and category immediately before making a financial commitment.
How to estimate a request before sending it
- Define the exact payload. Include every system and developer message, conversation turn, tool declaration, JSON schema, response-format setting, file, image, and other input your application will send. Do not estimate from the text a user sees if the API will receive additional metadata.
- Use a rough ratio only for early planning. A published character- or word-to-token approximation can help size a prototype or set a first budget. Label the result as an estimate, and do not turn it into an invoice prediction.
- Run the provider’s counting tool with the same model. OpenAI’s input-token-count endpoint is designed for the structured Responses API input. Anthropic’s Message Token Count API supports tools, images, and documents. A local tokenizer remains useful for simple text experiments, but it cannot reliably account for provider-specific wrappers or multimodal processing.
- Repeat the count for representative cases. Measure short and long conversations, typical and worst-case tool schemas, common languages, and realistic file sizes. A single short prompt can hide the cost of a long system instruction or accumulated history.
- Budget categories separately. Keep uncached input, cached input, cache writes, output, reasoning, and non-token charges in separate columns. This makes a change in model or caching policy visible instead of masking it inside one total.
How to verify what the call used
Inspect the response usage object
After each call, record the provider’s usage object alongside the model name, endpoint, request ID, and application workload. OpenAI Chat Completions commonly reports fields such as prompt_tokens, completion_tokens, and total_tokens. OpenAI Responses commonly reports input_tokens, output_tokens, and total_tokens, with additional detail for cached or reasoning usage when available.
// Illustrative shape; field availability depends on endpoint and model
{
"usage": {
"input_tokens": 1200,
"output_tokens": 340,
"total_tokens": 1540
}
}The example shows the kind of accounting to capture, not a guaranteed response schema. Field names, nesting, and detail vary by API version. Some responses expose cached-input or reasoning fields only when those features are used. Persist the raw usage object so that later changes in reporting do not destroy the evidence needed for reconciliation.
Separate displayed text from reported output
If your application truncates, summarizes, or hides a model response, calculate cost from the API’s reported output usage, not from the characters ultimately rendered in the interface. Internal reasoning and protocol-level output can remain billable after presentation logic removes them.
How to reconcile usage with an invoice
- Group calls by model and service tier. A single application may use several models, regions, or priority tiers with different rates.
- Aggregate each usage category. Sum uncached input, cached input, cache writes, output, and any modality-specific units independently. Do not combine cached and uncached input before applying rates.
- Match the provider’s accounting window. Usage dashboards and invoices can use a billing timezone or reporting period that differs from your application logs.
- Compare against official billing data. OpenAI recommends its Costs endpoint or Usage Dashboard for financial reconciliation. Use the equivalent usage and billing tools for other providers, then check the current pricing page for the exact model and category.
- Investigate residual differences. Look for retries, streaming calls, failed requests that still consumed processing, asynchronous jobs, tool charges, taxes, credits, or rounding rules. Those items may not appear in the text response itself.
Why your count may not match the provider’s count
You counted visible text only
The most common mismatch is omitting role wrappers, conversation history, tools, schemas, files, or images. Recount the exact structured request rather than copying the prompt into a separate tokenizer.
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You used the wrong model encoding
Tokenizer libraries are model-specific. Selecting an encoding that belongs to another model can produce a plausible but wrong number. Ensure the preflight tool and the production request identify the same model family.
The response contains hidden usage
Reasoning tokens or provider-specific output categories can raise reported output usage above the visible answer. Check detailed usage fields before assuming an error.
Your application retried or streamed more than once
Network failures, timeouts, client retries, and duplicated jobs can create additional billable calls. Correlate request IDs and server logs with usage records.
Cache treatment changed
A cached prefix may be reported separately from uncached input, and cache writes may create a charge when a prompt is first stored. Compare the provider’s cache fields and retention rules rather than applying the ordinary input rate to every token.
How to compare two LLM APIs fairly
Different token counts and prices are not directly interchangeable. Compare the same representative payload, the same workload, and the same service conditions.
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| Comparison axis | What to record | Why it matters |
|---|---|---|
| Tokenizer and model | Model name, encoding, language mix, and token count for the identical payload | The same text can produce different counts |
| Structured request coverage | Whether roles, tools, schemas, files, images, and other modalities are counted by the preflight tool | Text-only estimates can understate real usage |
| Usage categories | Input, output, cached input, cache writes, reasoning, audio, image, and other reported units | Separate categories may have separate rates |
| Non-visible processing | How reasoning or control tokens appear in usage fields | A short visible answer may still consume substantial output usage |
| Current rates | Price per category for the same region, endpoint, and service tier | A headline input price is not a complete call cost |
| Measurement tools | Availability and accuracy of preflight counting and post-call usage data | Better observability makes budgets auditable |
Never assume that identical payloads produce identical counts or that a lower per-token rate guarantees a lower invoice. The workload’s input-to-output mix, cache hit rate, reasoning behavior, and tool use determine the result.
Ways to control token spend without breaking quality
Reduce repeated input
Keep conversation history only as long as the task requires. Summarize old turns, remove duplicated instructions, and avoid sending the same large reference text when a supported cache can safely reuse it.
Design compact tool schemas
Tool names, descriptions, parameter explanations, and enumerated values are part of the request. Remove redundant prose and unused tools from each call, while retaining constraints needed for reliable behavior.
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Use the endpoint’s output-token limit and stop conditions to prevent runaway responses. A limit controls the maximum generated length; it does not guarantee that the model will use the full allowance or eliminate hidden reasoning usage.
Choose models by workload
Match the model to the task’s accuracy, context, latency, and reasoning requirements. A cheaper model can cost more overall if it needs retries or produces longer outputs, while a more capable model may be wasteful for simple classification.
Monitor distributions, not just averages
Track input and output percentiles, cache-hit rates, retries, and the largest requests. A monthly average can conceal a small number of oversized documents or unusually long reasoning calls that dominate spend.
Pricing and availability qualifications
Token rates, cache rules, model names, regional availability, endpoint support, and service tiers are volatile. A price quoted for one model or tier cannot be transferred to another. Before launch or budgeting, verify the provider’s live pricing and usage documentation for the exact model, endpoint, region, modality, and cache feature you will use. If the provider lists a separate charge for a tool or service, add it outside the token calculation.
Bottom line: count the request the API receives
A token is a model-specific tokenizer unit, not a word or a fixed character block. Billing is usually category-based: uncached and cached input, cache writes, output, hidden reasoning, and sometimes modality or tool usage can all matter. Use rough ratios only for early estimates; preflight the complete structured payload with the provider’s counting tool; then trust the response usage fields and official billing records for the final charge. That workflow is the difference between a plausible token guess and an auditable API budget.
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