{"uid":"cap_3_AIJXjXCdE1OIsbvua25","slug":"llm-token-count-estimator-cost-projector-d8407120","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/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_lN8HsPrXwWzdkzEHiFMy1","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 text string using heuristic (character- and word-based) methods, then projects per-model input costs for LLM budgeting.","exampleAgentPrompt":"Can you give me a heuristic token count estimate and a per-model cost projection for this text: 'Large language models are transforming software development by enabling natural language interfaces to complex systems.' — I want to see what it would cost to send this as input to a few different models.","exampleUseCases":[{"title":"Pre-flight prompt cost check","prompt":"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?"},{"title":"Budget planning for batch LLM pipeline","prompt":"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?"},{"title":"Context window feasibility check","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T18:58:19.332Z","isFirstParty":false}