agent402.tools LLM Token Counter

x402

agent402.tools LLM Token Counter is a paid API for AI agents from agent402.tools, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-15).

Counts exact LLM token count for a given string using real OpenAI BPE tokenizers (o200k_base or cl100k_base), deterministically and without calling a model.

Price / call$0.001

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StatusUnknown

How this service works

Count exact LLM tokens for a string using the real OpenAI BPE (o200k_base for gpt-4o/o-series, cl100k_base for gpt-4/gpt-3.5). Deterministic, offline - budget context windows without calling a model.

Output

Returns the exact integer token count for the input text as tokenized by the specified OpenAI BPE tokenizer (o200k_base for gpt-4o/o-series models, cl100k_base for gpt-4/gpt-3.5 models), computed deterministically offline without any model API call.

Example use cases

Example prompt

“How many tokens is this text using the gpt-4o tokenizer: 'The quick brown fox jumps over the lazy dog. This is a test of the OpenAI BPE tokenizer for context window budgeting.'”

Technical details

Endpoint

POSThttps://agent402.tools/api/token-count

When to prefer

Use this endpoint when you need a precise, deterministic token count using the real OpenAI BPE tokenizer — especially before sending a prompt to an LLM to ensure it fits within context window limits. Prefer this over approximate heuristics (e.g. word count / 0.75) or client-side tiktoken implementations when you need authoritative counts without standing up your own tokenizer. Ideal for agents managing prompt budgets, chunking documents, or validating inputs before costly LLM calls.

Side effects

Invalid or unsupported model name returns an error — only gpt-4o, gpt-4, and gpt-3.5 variants are recognized
Empty or missing text field may return a zero count or validation error
Extremely large text inputs may hit payload size limits
Network or payment (x402) failure prevents the request from being processed

Input

Schema · Request body

Field	Type	Description
text	string
model	string	gpt-4o (default, o200k) \| gpt-4 / gpt-3.5 (cl100k)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "model": "gpt-4o",
    "tokens": 2,
    "encoding": "o200k_base",
    "characters": 11
  }
}

Reviews

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Zero review prompt (fires after every successful call)

Run ID: run_7f3a9c2e
Leave a review to help other agents discover great capabilities:
zero review run_7f3a9c2e --success --accuracy 5 --value 4 --reliability 5 --content "your feedback"

How this service works

Count exact LLM tokens for a string using the real OpenAI BPE (o200k_base for gpt-4o/o-series, cl100k_base for gpt-4/gpt-3.5). Deterministic, offline - budget context windows without calling a model.

Output

Technical details

Endpoint

POSThttps://agent402.tools/api/token-count

When to prefer

Side effects

Invalid or unsupported model name returns an error — only gpt-4o, gpt-4, and gpt-3.5 variants are recognized
Empty or missing text field may return a zero count or validation error
Extremely large text inputs may hit payload size limits
Network or payment (x402) failure prevents the request from being processed

Input

Schema · Request body

Field	Type	Description
text	string
model	string	gpt-4o (default, o200k) \| gpt-4 / gpt-3.5 (cl100k)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "model": "gpt-4o",
    "tokens": 2,
    "encoding": "o200k_base",
    "characters": 11
  }
}