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Token Counter & LLM Cost Estimator
x402
Token Counter & LLM Cost Estimator is a paid API for AI agents from bess-x402.onrender.com, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-29).
Counts tokens in a text and estimates the cost of an LLM call (input + expected output tokens) across popular models, including context window fit checks.
Price / call$0.002
Success—
StatusUnknown
How this service works
Count a text's tokens and estimate the cost of one call on popular LLMs (or the models you list), input plus an expected number of output tokens, and whether it fits each context window.
Output
Returns the token count for the input text, then for each requested model (or a default set of popular models): the estimated cost in USD for the input tokens, the estimated cost for the expected output tokens, the total estimated cost per call, and whether the combined token count fits within that model's context window.
Example use cases
Pre-flight prompt cost check
“Before I send this system prompt to the API, can you count its tokens and estimate the cost on openai/gpt-4o and anthropic/claude with around 300 output tokens? Here's the text: 'You are an expert legal analyst. Review the following contract and identify any clauses that...'”
Context window feasibility check
“Will this 8000-word research paper fit in the context windows of GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro? Count the tokens and tell me which models can handle it, assuming 200 output tokens.”
Model cost comparison for budget planning
“I'm deciding which LLM to use for my app — can you estimate the per-call cost across all major models for this prompt with 1000 expected output tokens: 'Summarize the following customer support ticket and suggest a resolution:'?”
Technical details
Endpoint
GEThttps://bess-x402.onrender.com/v1/ai/token-cost?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to programmatically estimate LLM API costs before making calls, validate that a prompt fits within a model's context window, or compare pricing across multiple LLMs for a given workload. It is ideal for cost-aware agent pipelines, budget planning for LLM-powered applications, and dynamic model selection based on price or context constraints.
Side effects
- Missing required 'text' query parameter returns an error
- Invalid model ID or prefix returns an error or skips that model
- Very long texts may exceed processing limits
- output_tokens not provided defaults to a preset value which may not reflect actual usage
- Model pricing data may be stale if LLM providers update their rates
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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