AI Project Scope & Token Cost Estimator
ANA›Life Services Authority›National Calculator Authority›AI Project Scope & Token Cost Estimator
.calc-container { max-width: 640px; margin: 2rem 0; padding: 1.5rem; background: #fff; border: 1px solid #ddd; border-radius: 8px; box-shadow: 0 1px 3px rgba(0,0,0,0.06); font-family: system-ui, -apple-system, sans-serif; } .calc-container h3 { font-family: Georgia, serif; font-size: 1.15rem; color: #1a1a1a; margin-bottom: 1rem; padding-bottom: 0.5rem; border-bottom: 2px solid var(--ac, #3d5a80); } .calc-row { display: flex; align-items: center; gap: 0.75rem; margin-bottom: 0.75rem; flex-wrap: wrap; } .calc-row label { min-width: 160px; font-size: 0.9rem; color: #333; font-weight: 500; } .calc-row input[type="number"], .calc-row select { flex: 1; min-width: 120px; max-width: 200px; padding: 0.5rem 0.6rem; border: 1px solid #ccc; border-radius: 4px; font-size: 0.9rem; font-family: system-ui, sans-serif; color: #1a1a1a; background: #fafaf8; } .calc-row input:focus, .calc-row select:focus { outline: none; border-color: var(--ac, #3d5a80); box-shadow: 0 0 0 2px rgba(26,74,138,0.12); } .calc-row .unit { font-size: 0.82rem; color: #888; min-width: 30px; } .calc-btn { display: inline-block; margin-top: 0.5rem; padding: 0.55rem 1.5rem; background: var(--ac, #3d5a80); color: #fff; border: none; border-radius: 4px; font-size: 0.9rem; font-weight: 600; cursor: pointer; font-family: system-ui, sans-serif; } .calc-btn:hover { opacity: 0.9; } .calc-result { margin-top: 1.25rem; padding: 1rem 1.25rem; background: #f0f6fc; border-left: 3px solid var(--ac, #3d5a80); border-radius: 0 6px 6px 0; display: none; } .calc-result.visible { display: block; } .calc-result-label { font-size: 0.78rem; text-transform: uppercase; letter-spacing: 0.06em; color: #666; margin-bottom: 0.25rem; } .calc-result-value { font-size: 1.6rem; font-weight: 700; color: var(--ac, #3d5a80); } .calc-result-detail { font-size: 0.85rem; color: #555; margin-top: 0.5rem; line-height: 1.5; } .calc-note { margin-top: 1rem; font-size: 0.8rem; color: #888; font-style: italic; } .calc-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 0.75rem; margin-top: 0.75rem; } .calc-grid-item { padding: 0.6rem 0.8rem; background: #f8f9fa; border-radius: 4px; border: 1px solid #eee; } .calc-grid-item .label { font-size: 0.75rem; color: #888; text-transform: uppercase; letter-spacing: 0.04em; } .calc-grid-item .value { font-size: 1.1rem; font-weight: 600; color: #1a1a1a; } @media (max-width: 720px) { .calc-row { flex-direction: column; align-items: flex-start; gap: 0.3rem; } .calc-row label { min-width: auto; } .calc-row input[type="number"], .calc-row select { max-width: 100%; width: 100%; } .calc-grid { grid-template-columns: 1fr; } } .calc-chart { margin: 1rem 0; text-align: center; } .calc-chart svg { max-width: 100%; height: auto; } .calc-chart-legend { display: flex; flex-wrap: wrap; justify-content: center; gap: 0.6rem 1.2rem; margin-top: 0.6rem; font-size: 0.8rem; color: #555; } .calc-chart-legend span { display: inline-flex; align-items: center; gap: 0.3rem; } .calc-chart-legend i { display: inline-block; width: 10px; height: 10px; border-radius: 2px; font-style: normal; } .calc-related { max-width: 640px; margin: 2rem 0 1rem; padding: 1.25rem 1.5rem; background: #f8f9fa; border: 1px solid #e8e8e8; border-radius: 8px; } .calc-related h3 { font-family: Georgia, serif; font-size: 1rem; color: #1a1a1a; margin: 0 0 0.75rem; padding-bottom: 0.4rem; border-bottom: 2px solid var(--ac, #3d5a80); } .calc-related-list { list-style: none; padding: 0; margin: 0 0 0.75rem; display: grid; grid-template-columns: 1fr 1fr; gap: 0.4rem 1.5rem; } .calc-related-list li a { font-size: 0.88rem; color: var(--ac, #3d5a80); text-decoration: none; } .calc-related-list li a:hover { text-decoration: underline; } .calc-browse-all { margin: 0.5rem 0 0; font-size: 0.9rem; font-weight: 600; } .calc-browse-all a { color: var(--ac, #3d5a80); text-decoration: none; } .calc-browse-all a:hover { text-decoration: underline; } @media (max-width: 720px) { .calc-related-list { grid-template-columns: 1fr; } }
AI Project Scope & Token Cost Estimator
Estimate token consumption and API costs for AI projects based on model selection, prompt complexity, and usage volume.
AI Model
GPT-4o ($2.50 / $10.00 per 1M tokens in/out) GPT-4o Mini ($0.15 / $0.60 per 1M tokens in/out) GPT-4 Turbo ($10.00 / $30.00 per 1M tokens in/out) GPT-3.5 Turbo ($0.50 / $1.50 per 1M tokens in/out) Claude 3 Opus ($15.00 / $75.00 per 1M tokens in/out) Claude 3.5 Sonnet ($3.00 / $15.00 per 1M tokens in/out) Claude 3 Haiku ($0.25 / $1.25 per 1M tokens in/out) Gemini 1.5 Pro ($1.25 / $5.00 per 1M tokens in/out) Gemini 1.5 Flash ($0.075 / $0.30 per 1M tokens in/out)
Average Prompt (Input) Tokens per Request
Include system prompt + user message. ~750 words ≈ 1,000 tokens.
Average Completion (Output) Tokens per Request
Typical response length. ~750 words ≈ 1,000 tokens.
Requests per Day
Project Duration (Months)
Fine-Tuning Training Tokens (optional, 0 if none)
Total tokens in your fine-tuning dataset. Fine-tuning pricing varies by model.
Embedding Tokens per Day (optional, 0 if none)
For RAG/vector search pipelines. Uses text-embedding-3-small at $0.02/1M tokens.
Infrastructure & Overhead Buffer (%)
Add buffer for retries, testing, prompt iteration, and unexpected spikes.
Calculate Fill in the fields above and click Calculate.
function aiCalc() { // --- Model pricing table: [inputCostPer1M, outputCostPer1M, fineTuningCostPer1M or null] const models = { "gpt4o": { name: "GPT-4o", inCost: 2.50, outCost: 10.00, ftCost: null }, "gpt4o-mini": { name: "GPT-4o Mini", inCost: 0.15, outCost: 0.60, ftCost: 0.30 }, "gpt4-turbo": { name: "GPT-4 Turbo", inCost: 10.00, outCost: 30.00, ftCost: null }, "gpt35-turbo": { name: "GPT-3.5 Turbo", inCost: 0.50, outCost: 1.50, ftCost: 8.00 }, "claude3-opus": { name: "Claude 3 Opus", inCost: 15.00, outCost: 75.00, ftCost: null }, "claude3-sonnet":{ name: "Claude 3.5 Sonnet", inCost: 3.00, outCost: 15.00, ftCost: null }, "claude3-haiku": { name: "Claude 3 Haiku", inCost: 0.25, outCost: 1.25, ftCost: null }, "gemini15-pro": { name: "Gemini 1.5 Pro", inCost: 1.25, outCost: 5.00, ftCost: null }, "gemini15-flash":{ name: "Gemini 1.5 Flash", inCost: 0.075, outCost: 0.30, ftCost: null } };
const EMBEDDING_COST_PER_1M = 0.02; // text-embedding-3-small
// --- Read inputs const modelKey = document.getElementById("ai-model").value; const promptTokens = parseFloat(document.getElementById("ai-avg-prompt-tokens").value); const completionTokens = parseFloat(document.getElementById("ai-avg-completion-tokens").value); const requestsPerDay = parseFloat(document.getElementById("ai-requests-per-day").value); const durationMonths = parseFloat(document.getElementById("ai-project-duration-months").value); const ftTokens = parseFloat(document.getElementById("ai-fine-tuning-tokens").value) || 0; const embeddingPerDay = parseFloat(document.getElementById("ai-embedding-tokens-per-day").value) || 0; const overheadPct = parseFloat(document.getElementById("ai-overhead-pct").value) || 0;
const resultDiv = document.getElementById("ai-result");
// --- Validation const errors = []; if (isNaN(promptTokens) || promptTokens 0) { resultDiv.innerHTML = "" + errors.join("") + ""; return; }
const model = models[modelKey];
// --- Core calculations const daysInProject = durationMonths * 30.44; // avg days per month
// Daily token volumes const dailyInputTokens = promptTokens * requestsPerDay; const dailyOutputTokens = completionTokens * requestsPerDay; const dailyTotalTokens = dailyInputTokens + dailyOutputTokens;
// Project-total inference tokens const totalInputTokens = dailyInputTokens * daysInProject; const totalOutputTokens = dailyOutputTokens * daysInProject; const totalInferenceTokens = totalInputTokens + totalOutputTokens;
// Embedding tokens for project const totalEmbeddingTokens = embeddingPerDay * daysInProject;
// --- Cost calculations (base, before overhead) const inputCost = (totalInputTokens / 1_000_000) * model.inCost; const outputCost = (totalOutputTokens / 1_000_000) * model.outCost; const inferenceCost = inputCost + outputCost;
// Fine-tuning cost let ftCost = 0; let ftNote = ""; if (ftTokens > 0) { if (model.ftCost !== null) { ftCost = (ftTokens / 1_000_000) * model.ftCost; ftNote = "Fine-tuning cost calculated at $" + model.ftCost.toFixed(2) + "/1M tokens."; } else { ftNote = "⚠️ Fine-tuning is not publicly available for " + model.name + ". Fine-tuning cost excluded."; } }
// Embedding cost const embeddingCost = (totalEmbeddingTokens / 1_000_000) * EMBEDDING_COST_PER_1M;
// Subtotal before overhead const subtotal = inferenceCost + ftCost + embeddingCost;
// Overhead const overheadAmount = subtotal * (overheadPct / 100); const totalCost = subtotal + overheadAmount;
// Monthly cost const monthlyCost = totalCost / durationMonths;
- // --- Context window utilisation check
- const contextWarning = (promptTokens + completionTokens) > 100000
- ? "⚠️ Combined prompt + completion tokens exceed 100K — verify your model's context window limit."
- "";
// --- Format helpers function fmt(n, decimals=2) { return n.toLocaleString("en-US", {minimumFractionDigits: decimals, maximumFractionDigits: decimals}); } function fmtInt(n) { return Math.round(n).toLocaleString("en-US"); } function fmtTokens(n) { if (n >= 1_000_000_000) return fmt(n/1_000_000_000, 2) + "B"; if (n >= 1_000_000) return fmt(n/1_000_000, 2) + "M"; if (n >= 1_000) return fmt(n/1_000, 1) + "K"; return fmtInt(n); }
// --- Output resultDiv.innerHTML = ` ### 📊 Estimation Results — ${model.name}
Metric Value
Tokens per Request (In + Out)
${fmtInt(promptTokens)} + ${fmtInt(completionTokens)} = ${fmtInt(promptTokens+completionTokens)}
Daily Token Volume
${fmtTokens(dailyTotalTokens)} tokens/day
Project Duration
${fmt(durationMonths,1)} months (~${fmtInt(daysInProject)} days)
Total Inference Tokens
${fmtTokens(totalInferenceTokens)}
↳ Input Tokens
${fmtTokens(totalInputTokens)}
↳ Output Tokens
${fmtTokens(totalOutputTokens)}
${totalEmbeddingTokens > 0 ? Total Embedding Tokens
${fmtTokens(totalEmbeddingTokens)} : ""}
${ftTokens > 0 ? Fine-Tuning Tokens
${fmtTokens(ftTokens)} : ""}
Cost Component Amount (USD)
Input Token Cost
$${fmt(inputCost)}
Output Token Cost
$${fmt(outputCost)}
${embeddingCost > 0 ? Embedding Cost
$${fmt(embeddingCost)} : ""}
${ftCost > 0 ? Fine-Tuning Cost
$${fmt(ftCost)} : ""}
Subtotal (before overhead)
$${fmt(subtotal)}
Overhead Buffer (${fmt(overheadPct,1)}%)
$${fmt(overheadAmount)}
💰 Total Project Cost $${fmt(totalCost)}
📅 Estimated Monthly Cost $${fmt(monthlyCost)}/mo
${ftNote ? `${ftNote}
: ""}
${contextWarning ?${contextWarning}
: ""};
}
aiCalc();
#### Formulas Used
Daily Input Tokens = Avg Prompt Tokens × Requests per Day
Daily Output Tokens = Avg Completion Tokens × Requests per Day
Total Inference Tokens = (Daily Input + Daily Output) × (Duration Months × 30.44 days)
Input Cost = (Total Input Tokens ÷ 1,000,000) × Model Input Price per 1M
Output Cost = (Total Output Tokens ÷ 1,000,000) × Model Output Price per 1M
Embedding Cost = (Embedding Tokens/Day × Days) ÷ 1,000,000 × $0.02
Fine-Tuning Cost = (Fine-Tuning Tokens ÷ 1,000,000) × Model Fine-Tuning Price per 1M
Total Cost = (Input Cost + Output Cost + Embedding Cost + Fine-Tuning Cost) × (1 + Overhead% ÷ 100)
Monthly Cost = Total Cost ÷ Duration Months
#### Assumptions & References
- Embedding cost uses OpenAI text-embedding-3-small at $0.02/1M tokens as a baseline.
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