Cost Estimation

Input: Process once and cache
Output: Each token attends to all previous tokens

Example - Generating 1000 tokens:
Token 1:   Attends to input (e.g., 500 tokens)
Token 2:   Attends to input + token 1 (501 tokens)
Token 3:   Attends to input + tokens 1-2 (502 tokens)
...
Token 1000: Attends to all (1,500 tokens)

Total attention operations: 500,500

// Task complexity analysis
const taskToModel = {
  // Simple tasks - use cheapest models
  'classification': ['GPT-4o Mini', 'Gemini Flash', 'Llama 3.1 8B'],
  'extraction': ['GPT-4o Mini', 'Command R', 'Mistral Nemo'],
  'simple_qa': ['GPT-3.5 Turbo', 'Claude Haiku', 'Gemini Flash'],
  
  // Medium tasks - balance cost and quality
  'summarization': ['GPT-4o', 'Claude 3.5 Sonnet', 'Llama 3.1 70B'],
  'translation': ['GPT-4o', 'Command R+', 'Qwen 2.5'],
  'analysis': ['GPT-4o', 'Claude Sonnet', 'Mistral Large'],
  
  // Complex tasks - use best models
  'reasoning': ['GPT-4o', 'Claude 3.5 Sonnet', 'Claude Opus'],
  'code_generation': ['GPT-4o', 'Claude 3.5 Sonnet', 'Llama 3.1 405B'],
  'creative_writing': ['GPT-4o', 'Claude 3.5 Sonnet', 'Command R+']
};

// Same 10,000 character input
const text = "...10,000 characters...";

// Token counts (approximate)
const tokenization = {
  'Qwen 2.5':    2300,  // 0.92 ratio
  'Llama 3.1':   2375,  // 0.95 ratio  
  'GPT-4o':      2500,  // 1.0 ratio (baseline)
  'Gemini 1.5':  2625,  // 1.05 ratio
  'Claude 3.5':  2750   // 1.1 ratio
};

// Cost comparison at $0.35 per 1M (Llama 3.1 70B pricing)
const costs = {
  'Qwen 2.5':   (2300 / 1_000_000) * 0.35,  // $0.000805
  'Llama 3.1':  (2375 / 1_000_000) * 0.35,  // $0.000831
  'GPT-4o':     (2500 / 1_000_000) * 2.50,  // $0.00625
  'Claude 3.5': (2750 / 1_000_000) * 3.00   // $0.00825
};

// OpenAI Batch API example
const standardCost = {
  'GPT-4o': 2.50,           // Input per 1M
  'GPT-4o Batch': 1.25      // 50% discount
};

// Trade-off: 24-hour processing time
// Best for: Non-urgent bulk processing

// Verbose prompt (expensive)
const verbosePrompt = `
I would really appreciate it if you could please take a moment 
to carefully analyze the following text and provide me with a 
comprehensive summary that captures all of the main points and 
key ideas presented in the document.
`;
// Tokens: ~45

// Concise prompt (efficient)
const concisePrompt = `
Summarize the key points:
`;
// Tokens: ~7

// Savings: 84% reduction

// Uncontrolled output
const openEnded = "Explain quantum computing.";
// Model might generate: 2000+ tokens

// Controlled output
const controlled = "Explain quantum computing in 100 words.";
// Model generates: ~130 tokens (includes some overhead)

// Cost comparison (GPT-4o):
// Uncontrolled: 2000 tokens × $10/1M = $0.020
// Controlled:   130 tokens × $10/1M  = $0.0013
// Savings: 93.5%

// Inefficient: Resending full context each time
const inefficientChat = [
  { tokens: 5000 },  // Initial context + message 1
  { tokens: 5100 },  // Full context + message 2
  { tokens: 5200 },  // Full context + message 3
];
// Total: 15,300 tokens

// Efficient: Use conversation memory strategically
const efficientChat = [
  { tokens: 5000 },  // Initial context + message 1
  { tokens: 1100 },  // Recent context + message 2
  { tokens: 1200 },  // Recent context + message 3
];
// Total: 7,300 tokens (52% savings)

// Example: FAQ system
const cache = new Map();

async function getAnswer(question) {
  // Check cache first
  const cacheKey = normalizeQuestion(question);
  if (cache.has(cacheKey)) {
    return cache.get(cacheKey);  // $0 cost
  }
  
  // Call API only for new questions
  const answer = await callAI(question);  // Full cost
  cache.set(cacheKey, answer);
  return answer;
}

// If 70% of questions are repeats:
// Savings: 70% of API costs

// Remove unnecessary whitespace
function compress(text) {
  return text
    .replace(/\s+/g, ' ')           // Multiple spaces → single
    .replace(/\n\s*\n/g, '\n')      // Multiple newlines → single
    .trim();
}

// Example:
const original = `
  This  has     extra    spaces.
  
  
  And multiple blank lines.
`;
// Tokens: ~18

const compressed = compress(original);
// "This has extra spaces. And multiple blank lines."
// Tokens: ~11
// Savings: 39%

// Cost comparison
const embedding = {
  model: 'text-embedding-3-small',
  inputCost: 0.02,        // Per 1M tokens
  outputCost: 0           // No output
};

const generation = {
  model: 'GPT-4o',
  inputCost: 2.50,        // Per 1M tokens
  outputCost: 10.00       // Per 1M tokens
};

// Embedding 1M tokens: $0.02
// Generating 1M tokens: $12.50 (input + output)
// Embeddings are 625x cheaper!

// Using GPT-4o for simple classification
const result = await classify(text, 'GPT-4o');
// Cost: $0.0025 per 1000 tokens

// Use GPT-4o Mini for simple tasks
const result = await classify(text, 'GPT-4o Mini');
// Cost: $0.00015 per 1000 tokens
// Savings: 94%

// Requesting verbose outputs
prompt = "Write a comprehensive, detailed analysis...";
// Output: 3000+ tokens at $10/1M = $0.030+

// Request concise outputs
prompt = "Write a concise analysis (max 200 words)...";
// Output: ~300 tokens at $10/1M = $0.003
// Savings: 90%

// Sending full conversation history every time
const context = fullHistory.join('\n');  // 10,000 tokens
const prompt = context + newMessage;     // +100 tokens
// Cost per message: $0.025

// Summarize and limit context
const context = recentHistory.slice(-5).join('\n');  // 500 tokens
const prompt = context + newMessage;                  // +100 tokens
// Cost per message: $0.0015
// Savings: 94%

// Regenerating same content repeatedly
for (let i = 0; i < 1000; i++) {
  await generateFAQ(commonQuestion);
}
// Cost: 1000x API calls

// Cache frequent responses
const cached = cache.get(commonQuestion);
if (cached) return cached;

const result = await generateFAQ(commonQuestion);
cache.set(commonQuestion, result);
// Cost: 1x API call (999 cache hits = $0)