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OpenVerbs Token Counter
x402
OpenVerbs Token Counter is a paid API for AI agents from tokens.openverbs.com, paid per call via x402, $0.004/call, status unknown (last checked 2026-09-14).
Counts the exact number of LLM tokens in a given text string for a specified model or encoding, supporting OpenAI, Llama, Mistral, Qwen, Gemma, DeepSeek, Command-R, Claude, and Gemini.
Price / call$0.004
Success—
StatusUnknown
How this service works
Count the LLM tokens in text for a given model or encoding — exact for OpenAI and open models (Llama, Mistral, Qwen, Gemma, DeepSeek, Command-R); labelled-approximate for Claude & Gemini.
Output
Returns the integer token count for the provided text under the specified model or encoding, along with the resolved tokenizer/encoding name. For OpenAI models and open-source models (Llama, Mistral, Qwen, Gemma, DeepSeek, Command-R), counts are exact; for Claude and Gemini, counts are labelled as approximate.
Example use cases
Pre-flight context window check
“Before I send this 5,000-word document to the claude-3-5-sonnet API, can you tell me how many tokens it will use so I know if it fits within the context window?”
Prompt cost estimation for gpt-4o
“Count the tokens in this system prompt using the gpt-4o model so I can estimate how much each API call will cost: 'You are a helpful assistant that answers questions about software engineering.'”
Llama-3 tokenization for fine-tuning
“How many tokens does this training example have when tokenized for llama-3.1-70b? The text is: 'The quick brown fox jumps over the lazy dog. This sentence is often used in typography.'”
Technical details
Endpoint
POSThttps://tokens.openverbs.com/v1/countWhen to prefer
Choose this endpoint when you need accurate, model-specific token counts before making LLM API calls — especially for cost estimation, context window validation, or prompt engineering. It supports the widest range of model families (OpenAI, Llama, Mistral, Qwen, Gemma, DeepSeek, Command-R, Claude, Gemini) in a single API, making it preferable to rolling your own tiktoken/sentencepiece integration or using model-provider estimations that may be inaccurate.
Side effects
- Text exceeds 100,000 character limit — returns validation error
- Unknown or unsupported model name — falls back to default gpt-4o tokenizer or returns error
- Empty string input — returns 0 tokens successfully
- Invalid encoding enum value — returns schema validation error
- Payment failure via x402 protocol — returns 402 Payment Required before processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Tags
x402
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