Endpoint · inferenceGETagenttools-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.
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…
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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%.Overview
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UNRATED
0 of 30 paid calls toward a letter
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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.
MCP server on this host: io.github.Thyphex/agenttools-hub
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first 2 days ago · last 11 hr ago
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Handshakescomputed 1 day ago
0.400 from 2 free handshakes over 30 days · UNRATED ?
| Component | Weight | Measured | Lower bound ? | Adds | Short ? | Uncertain ? | |
|---|---|---|---|---|---|---|---|
| livenesscosts the most it answered at all |
0.60 | 1.00 | 0.000 | 0.000 | 0 | −0.600 | |
| latency p95 it answered as fast as its class |
0.20 | 1.00 | 1.000 | 0.200 | 0 | 0 | |
| price stability the price stayed where it was listed |
0.20 | 1.00 | 1.000 | 0.200 | 0 | 0 | |
| Composite | 1.00 | 0.400 | gap to 1.000 = 0.600 · 0.000 short · 0.600 uncertain | 0.400 | −0.000 | −0.600 |
Not measured: correctness · nobody paid; honesty · nobody paid; schema conformance · no answer had a published shape to check
LOW_DIVERSITYBURN_INFREE_TIER_ONLY
Verify it yourself
npx teppi-check 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.curl -s https://api.teppi.xyz/v1/trust/cap_01M3XJC3B5RGFM73CTABQNT5RG