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Dev Token Estimator

by AgentTools in Onchain & crypto

x402 APIPassing, checked 1 h ago

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%.

GET https://agenttools-hub.vercel.app/api/v1/dev/token-estimator

Last 30 days

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Uptime
100%
Response time
144 ms typical, 144 ms slowest 5%
Last check
1 h ago
Next check
any minute now

How to call it

# See the payment challenge (nothing is charged)
curl -i -X GET "https://agenttools-hub.vercel.app/api/v1/dev/token-estimator?text=The+quick+brown+fox+jumps+over+the+lazy+dog.+Pack+my+box+with+five+dozen+liquor+jugs."
import { wrapFetchWithPayment } from "@x402/fetch";
import { x402Client } from "@x402/core/client";
import { ExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

const client = new x402Client().register(
  "eip155:8453",
  new ExactEvmScheme(privateKeyToAccount(process.env.AGENT_KEY)),
);
const pay = wrapFetchWithPayment(fetch, client);

// Not sure it's safe to pay? Preflight it first for $0.005:
// GET https://toolvet.app/api/v1/check?url=https%3A%2F%2Fagenttools-hub.vercel.app%2Fapi%2Fv1%2Fdev%2Ftoken-estimator
const res = await pay("https://agenttools-hub.vercel.app/api/v1/dev/token-estimator?text=The+quick+brown+fox+jumps+over+the+lazy+dog.+Pack+my+box+with+five+dozen+liquor+jugs.");
console.log(await res.json());

Example input

{
  "text": "The quick brown fox jumps over the lazy dog. Pack my box with five dozen liquor jugs."
}

Example output

{
  "result": {
    "answer": "23 tokens (GPT family)",
    "answerLabel": "85 chars · ±10% estimate",
    "details": [
      {
        "label": "Characters",
        "value": "85"
      },
      {
        "label": "Words",
        "value": "17"
      },
      {
        "label": "CJK characters",
        "value": "0"
      },
      {
        "label": "Method",
        "value": "heuristic (chars/word mix, CJK-aware)"
      }
    ],
    "steps": [
      "CJK characters count ≈ 1.1 tokens each",
      "Other scripts ≈ 3.7 chars/token, blended with words × 1.32",
      "Model multipliers applied: 1, 1.06, 1.12, 1.18, 1.1"
    ],
    "table": {
      "columns": [
        "Model family",
        "Est. tokens"
      ],
      "rows": [
        [
          "gpt-4o / gpt-4.1 / gpt-5 (o200k)",
          23
        ],
        [
          "claude (Opus / Sonnet / Haiku)",
          25
        ],
        [
          "gemini 2.5 / 3",
          26
        ],
        [
          "llama 4 / mistral",
          28
        ],
        [
          "deepseek v3",
          26
        ]
      ]
    },
    "value": 23
  },
  "tool": "token-estimator"
}

Security scan

  • No findings. We scan names, descriptions and tool definitions for hidden instructions and other prompt-injection patterns.

Recent checks

WhenResultHTTPTimePrice
1 h agoPassed402200 ms$0.002
6 h agoPassed402144 ms$0.002