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AgentTools Token Estimator
x402
AgentTools Token Estimator is a paid API for AI agents from agenttools-hub.vercel.app, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-02).
Estimates token counts for a given text across major LLM families (GPT, Claude, Gemini, Llama, DeepSeek) using a character/word heuristic within ~10% of real BPE tokenizers.
Price / call$0.001
Success—
StatusUnknown
How this service works
Estimate how many tokens a text consumes for each major model family (GPT, Claude, Gemini, Llama, DeepSeek) using a character/word heuristic that is within ~10% of real BPE tokenizers — enough for context budgeting and cost math, with zero dependencies. Use this when an agent needs to approximate token counts per LLM family, ±10%.
Output
Returns estimated token counts for the input text broken down by major LLM model family (GPT, Claude, Gemini, Llama, DeepSeek), computed via a character/word heuristic that is within approximately 10% of real BPE tokenizer output. No external dependencies are required.
Example use cases
Context window budget check
“Before I send this 2,000-word article to GPT-4, can you estimate how many tokens it will consume so I know if it fits in the 8k context window?”
Cross-model cost comparison
“I want to compare token costs across Claude, GPT, and Gemini for this product description — can you estimate the token count for each model family so I can figure out which is cheapest?”
Chunking strategy validation
“I'm splitting this document into chunks for RAG — can you estimate the token count of this chunk to make sure it stays under 512 tokens for Llama?”
Technical details
Endpoint
GEThttps://agenttools-hub.vercel.app/api/v1/dev/token-estimator?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, zero-dependency token count approximations across multiple LLM families simultaneously without calling each model's tokenizer API. It is ideal for context budgeting, cost estimation math, and chunking decisions where ~10% accuracy is acceptable. Prefer it over exact tokenizers when speed and multi-model comparison matter more than precision.
Side effects
- Missing required 'text' query parameter returns an error
- Very long texts may hit URL length limits for GET requests
- Estimates can deviate up to ~10% from actual BPE tokenizer counts for edge cases like code, non-Latin scripts, or emoji-heavy content
- Empty string input may return zero counts or an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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