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A simple LLM token counter can disagree with the model for two reasons: it may use the wrong tokenizer, or it may count visible text instead of the full structured request. You can use a local tokenizer to estimate text tokens, but for a pre-send request count, use the target API’s counting method with the same input structure you plan to send.
Why can a token counter disagree with a model?
“How many tokens is this?” has different answers depending on what “this” means. A local script can count a string using a selected encoding. A chat request, however, can include message roles and boundaries, tool definitions, schemas, images, or files in addition to visible text. The tokenizer and the counted material both need to match your goal.
Failure 1: The counter uses the wrong encoding
Tokenization is not a universal character or word count. Segmentation depends on the encoding, the model, the language, spelling, and surrounding text. A count or token ID produced with one encoding should not be assumed to apply to another model. OpenAI’s token guidance explains why text counts vary, while its tiktoken example shows model-aware encoding selection and notes that message-count estimates are not a permanent guarantee.
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Install the library, then run this example to see how the same Japanese string is segmented by three encodings:
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python -m pip install tiktokenimport tiktoken
text = "お誕生日おめでとう"
for name in ("p50k_base", "cl100k_base", "o200k_base"):
encoding = tiktoken.get_encoding(name)
print(f"{name}: {len(encoding.encode(text))} tokens")The OpenAI Cookbook example reports 14 tokens for p50k_base, 9 for cl100k_base, and 8 for o200k_base. These are example results for this string, not a general rule or a guarantee for other text. This script counts only the string; it does not establish an API request’s full input count.
Choose the tokenizer for the target
For local text inspection, select an encoding known to match the model rather than hard-coding one and reusing it indiscriminately. The Cookbook demonstrates tiktoken.encoding_for_model(model). For models in other stacks, use that model’s own tokenizer. A model-aware local tokenizer improves the relevance of a text count, but it still may not account for provider-side request formatting.
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Failure 2: The counter counts text, not the request
A script that encodes only each message’s visible content can omit information the API processes. OpenAI’s documentation says its input-token count includes formatting tokens used to represent request structure, such as message roles and boundaries. Tools, schemas, images, and files can also affect the full input count. The exact contribution depends on the request format and provider; there is no universal message-overhead constant to add to a text-only count.
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For an OpenAI Responses request, count the same input shape
OpenAI provides an input-token counting endpoint for supported Responses input forms. Use the same structure you intend to send when you need a request-level pre-send count, rather than counting only a text fragment. The official Python guide currently shows this example; model availability and APIs can change:
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from openai import OpenAI
client = OpenAI()
count = client.responses.input_tokens.count(
model="gpt-6-astra",
input="Tell me a joke.",
)
print(count.input_tokens)For a structured request, pass its supported structured input instead of substituting just the visible text. The endpoint’s count covers supported input and formatting, not generated output that has not yet been produced. See OpenAI’s token-counting guide for supported forms and current usage.
For a Hugging Face chat model, apply its chat template
Chat models often expect a model-specific representation of roles and message boundaries. Hugging Face’s chat templating documentation explains how to apply the tokenizer’s template. If you render a template to text and then tokenize it separately, set add_special_tokens=False when the rendered template already contains the required special tokens. Otherwise, the tokenizer may add another layer and duplicate them.
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Which counting method should you use?
| Method | What it counts | Best use and limitation |
|---|---|---|
| Raw text with a chosen encoding | A string encoded with the selected tokenizer | Quick text inspection or an estimate when the encoding matches the target; misses any request structure not in the string. |
| Model-aware local tokenizer | Text using the tokenizer associated with the target model | Preferable for local text counts; not necessarily a count of provider-side formatting or all request components. |
| Chat-template tokenizer | Conversation text formatted with the target chat model’s template | Useful for local chat-model inputs; avoid adding special tokens a second time after rendering the template. |
| Request-level counting endpoint | Supported structured input before sending, including applicable formatting | Use when available for the provider and request format; support is provider-specific. |
| Returned API usage | Usage reported after the request is processed | Use to inspect actual reported usage, rather than treating a local estimate as exact. |
What a pre-send count cannot tell you
Counting input before sending does not predict generated output. After the call, inspect the response’s reported usage for the actual usage information the API provides. Output totals may include tokens that do not appear in the visible text, so counting the displayed answer alone is not necessarily equivalent to the reported output usage.
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