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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 text string using heuristic (character- and word-based) methods, then projects per-model input costs for LLM budgeting.
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 (based on character and word counts, not exact BPE tokenization), along with an illustrative breakdown of expected input costs across multiple popular LLM models, useful for budgeting and planning.
Example use cases
Pre-flight prompt cost check
“Before I send this 3,000-word article to GPT-4 for summarization, can you estimate how many tokens it contains and tell me roughly how much the input will cost?”
Budget planning for batch LLM pipeline
“I'm building a pipeline that will process about 500 customer support tickets through an LLM. Here's a sample ticket — can you estimate the token count and project the per-model input cost so I can plan my budget?”
Context window feasibility check
“I want to know if this legal document I'm about to paste fits within a typical 128k-token context window — can you estimate the token count for me using the heuristic estimator?”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/llm-token-estimateWhen to prefer
Use this endpoint when you need a fast, cheap heuristic token count and multi-model cost estimate without spinning up a local tokenizer library. Ideal for budgeting pipelines, pre-flight checks, or agentic workflows that need to gauge LLM API costs before making actual calls. Not suitable when exact token counts are required (e.g. for precise context-window boundary checking) — for that, use a model-specific BPE tokenizer.
Side effects
- Empty or missing text input returns a validation error
- Extremely large payloads may time out or be rejected
- Results are approximate — not exact BPE tokenizer output, so actual token counts may differ from real tokenizer results
- Unsupported or unrecognized model names may not appear in cost projections
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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