{"uid":"cap_uNN2KN_xFsJ3isIilC_tp","slug":"agenttools-token-estimator-7db842eb","name":"AgentTools Token Estimator","description":"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%.","url":"https://agenttools-hub.vercel.app/api/v1/dev/token-estimator?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":["text"],"properties":{"text":{"type":"string","description":"Text"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","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":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_EmS1hkZvSpjnqciq9GrYl","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"How many tokens would this text use across GPT, Claude, Gemini, Llama, and DeepSeek: 'The quick brown fox jumps over the lazy dog. This is a test of token estimation for context budgeting.'?","exampleUseCases":[{"title":"Context window budget check","prompt":"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?"},{"title":"Cross-model cost comparison","prompt":"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?"},{"title":"Chunking strategy validation","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-02T12:42:41.156Z","isFirstParty":false,"canonicalSlug":"agenttools-token-estimator-7db842eb"}