{"uid":"cap_4LU5OCn63xExJXSgi36ij","slug":"llm-token-count-estimator-cost-projector-93bf62aa","name":"LLM Token Count Estimator & Cost Projector","description":"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.","url":"https://api.24klabs.ai/api/v1/llm-token-estimate","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string"}}},"responseSchema":{"type":"json","example":{"chars":66,"words":11,"costs_usd":{"claude-sonnet":0.000042},"tokens_estimate":14}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_PvqofCpvJRFv8D7AQjaoO","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"Give me a heuristic token estimate and per-model cost projection for this text: 'Large language models have transformed the way developers build applications, enabling natural language interfaces at scale.' — I want to see what it would cost to use as input on GPT-4 and Claude.","exampleUseCases":[{"title":"Pre-flight prompt cost check","prompt":"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?"},{"title":"Context window budget planning","prompt":"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."},{"title":"Multi-model cost comparison","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T12:57:17.037Z","isFirstParty":false}