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LLM Token Count Estimator & Cost Projector
x402
LLM Token Count Estimator & Cost Projector is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Estimates token count for a given text using heuristic (character- and word-based) methods and projects approximate input costs across LLM models for budget planning.
Price / call$0.002
Success—
StatusUnknown
How this service works
Heuristic token-count estimate for a text (character- and word-based) plus an illustrative per-model input-cost projection for budgeting. Not an exact BPE tokenizer.
Output
Returns a heuristic token count estimate (character- and word-based approximation, not exact BPE), along with illustrative per-model input cost projections in USD/USDC for budgeting purposes. Note: results are approximations and may differ from actual tokenizer output.
Example use cases
Pre-flight prompt cost check
“Before I send this 2,000-word document to GPT-4, can you estimate the token count and tell me roughly what it'll cost me in input tokens?”
Context window budget planning
“I'm building an agent that processes customer support tickets. Estimate how many tokens this sample ticket uses so I can figure out how many I can batch per API call without hitting the context limit.”
Multi-model cost comparison
“I have this system prompt and user message combined — can you run a token estimate and show me what the input cost would look like across different LLMs so I can pick the cheapest one for my use case?”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/v1/llm-token-estimateWhen to prefer
Use this endpoint when you need a fast, cheap heuristic approximation of token counts and cost projections without running a full BPE tokenizer locally. Ideal for budgeting and pre-flight checks in agent pipelines, especially when exact tokenization is not critical and speed matters. Not suitable when exact token counts are required for context window enforcement — use an official tokenizer library for that.
Side effects
- Empty or missing text input returns validation error
- Extremely large texts may time out or be truncated
- Estimates may diverge significantly from actual BPE tokenizer counts for non-English or code-heavy text
- Unknown model name in cost projection may return null or default pricing
- Malformed request body returns 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"chars": 66,
"words": 11,
"costs_usd": {
"claude-sonnet": 0.000042
},
"tokens_estimate": 14
}
}Tags
x402
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