Token Efficiency in AI Coding: Why Claude Code Uses 5.5x Fewer Tokens Than Cursor

# AI Model Pricing (per 1M tokens, July 2026)
# Model              | Input    | Output   | Context Window
# -------------------|----------|----------|---------------
# GPT-4o             | $2.50    | $10.00   | 128K
# GPT-4o-mini        | $0.15    | $0.60    | 128K
# Claude 3.5 Sonnet  | $3.00    | $15.00   | 200K
# Claude 3.5 Haiku   | $0.25    | $1.25    | 200K
# Gemini 1.5 Pro     | $1.25    | $5.00    | 2M
# Gemini 1.5 Flash   | $0.075   | $0.30    | 1M

# What does this mean in practice?
# A typical coding session (50 interactions):
# - Claude Code: ~780K input + ~160K output = $4.74 (Sonnet)
# - Cursor:      ~3.6M input + ~205K output = $13.85 (GPT-4o)
# - Aider:       ~2.3M input + ~190K output = $8.65 (GPT-4o)

# Monthly cost for 1 developer (22 working days):
# Claude Code: $104/mo (API) vs. $20/mo (subscription)
# Cursor:      $305/mo (API) vs. $20/mo (subscription)
# The subscription hides the real token cost!

# Claude Code: Map-Reduce Context Strategy

# Step 1: MAP — Create a lightweight project overview
# (runs once, cached for the session)
project_map = {
    "files": ["src/auth.ts", "src/api.ts", ...],  # names only
    "exports": {"src/auth.ts": ["login", "logout", "verify"]},
    "types": {"User": "src/types.ts:12", "Session": "src/types.ts:28"},
    "dependencies": {"src/auth.ts": ["src/api.ts", "src/db.ts"]}
}
# Cost: ~2,000 tokens (one-time)

# Step 2: For each task, REDUCE to relevant context
task = "Refactor the login function to use OAuth2"

# Claude Code identifies relevant files from the map:
relevant_files = ["src/auth.ts", "src/types.ts"]
# NOT: every file in the project

# It sends only the relevant functions, not entire files:
context = extract_functions(relevant_files, ["login", "User", "Session"])
# Cost: ~8,000 tokens (vs. 40,000+ for full files)

# Total per task: ~10,000 tokens
# Compare to Cursor: ~55,000 tokens (full files + related code)

# Cursor: Broad Context Strategy

# Cursor's approach: include everything that MIGHT be relevant
task = "Refactor the login function to use OAuth2"

# Cursor includes:
context = {
    "current_file": full_file("src/auth.ts"),           # 12,000 tokens
    "recently_viewed": [full_file("src/api.ts")],        # 8,000 tokens
    "open_tabs": [full_file("src/types.ts")],            # 5,000 tokens
    "related_by_import": [full_file("src/db.ts")],       # 15,000 tokens
    "similar_code": search_results("login", "auth"),     # 10,000 tokens
    "conversation_history": last_10_messages,            # 5,000 tokens
}
# Total: ~55,000 tokens per interaction

# Why? Cursor prioritizes "never miss relevant context" over efficiency.
# This works well for IDE integration but wastes tokens on irrelevant code.

# Strategy 1: Ignore files aggressively
# .cursorignore / .claudeignore
node_modules/
dist/
build/
*.generated.*
*.lock
coverage/
__tests__/          # Unless you're specifically working on tests
docs/               # Unless you're working on documentation
*.min.js
*.map

# Strategy 2: Use structured outputs to reduce verbosity
# BAD (verbose):
prompt = "Explain what this function does and suggest improvements"
# → Model writes 500 words of explanation = ~700 output tokens

# GOOD (structured):
prompt = """Analyze this function. Return JSON:
{
  "purpose": "one sentence",
  "issues": ["issue1", "issue2"],
  "suggestions": ["suggestion1"]
}"""
# → Model returns 100 tokens of structured data

# Strategy 3: Use incremental context (diffs, not full files)
# BAD: Send entire file for every edit
send_file("src/auth.ts")  # 12,000 tokens

# GOOD: Send only what changed
send_diff("src/auth.ts", since="last_edit")  # 500 tokens

# Strategy 4: Model routing
def handle_request(task):
    if task.complexity == "simple":  # formatting, renaming
        return gpt_4o_mini(task)     # $0.0001
    elif task.complexity == "medium":  # single-file changes
        return claude_haiku(task)      # $0.001
    else:  # multi-file, architectural
        return claude_sonnet(task)     # $0.01

# Monthly Cost: Team of 5 Developers (22 working days)
# 100 interactions/developer/day = 11,000 interactions/month

# Claude Code (Sonnet, map-reduce strategy)
tokens_per_interaction = 10,000  # avg input + output
monthly_tokens = 11,000 * 10,000 = 110M tokens
monthly_cost = 110M * $4.50/1M (blended rate) = $495
subscription = $100/mo (5 * $20)
TOTAL: $595/mo

# Cursor (GPT-4o, broad context)
tokens_per_interaction = 55,000
monthly_tokens = 11,000 * 55,000 = 605M tokens
monthly_cost = 605M * $4.50/1M = $2,723  ← OUCH
subscription = $100/mo (5 * $20)
TOTAL: $2,823/mo

# Optimized Cursor (with .cursorignore + structured outputs)
tokens_per_interaction = 25,000  # 55% reduction
monthly_cost = $1,238
TOTAL: $1,338/mo  ← Still 2.2x more than Claude Code

# Gemini CLI (free tier, 1000 req/day/developer)
interactions = 100 * 5 * 22 = 11,000
free_quota = 1000 * 5 * 22 = 110,000 requests
TOTAL: $0/mo  ← But limited to simpler tasks

# Key insight: Token efficiency matters MORE than subscription price
# The $20/mo subscription is irrelevant when API costs are $500-2700/mo