# How to Reduce AI Token Costs by 78% Without Losing Quality

Last updated: April 2026 | Reading time: 7 min

AI coding tools are expensive. If you're using GPT-4, Claude, or any frontier model through an API, you're paying per token — and most of those tokens are wasted.

Here's the math that should worry you:

Metric | Typical Usage
--- | ---
Average codebase | ~186,000 tokens
Tokens sent per request | 10,000 - 50,000
Duplicate content in context | 15-30%
Boilerplate / low-value content | 40-60%
Cost per 1K API requests (GPT-4) | ~$300-$600

You're paying full price for tokens that add zero information. Duplicate imports, boilerplate comments, copy-pasted utility functions, auto-generated code — all of it burns your budget.

## The Three Sources of Token Waste

### 1. Duplicate Code (15-30% of tokens)

Most codebases have significant duplication — utility functions copied across modules, similar error handlers, repeated patterns. Your AI tool sends all copies every time.

### 2. Boilerplate (40-60% of tokens)

License headers, auto-generated code, verbose comments, import blocks, configuration templates — high token count, near-zero information value for AI.

### 3. Wrong Granularity (variable)

RAG systems and AI tools send entire files when your AI only needs a function signature. Or they send 3 files when the AI needs visibility into 300. There's no middle ground.

## The Solution: Information-Theoretic Context Compression

Entroly attacks all three sources simultaneously:

Waste Source | How Entroly Eliminates It | Token Savings
--- | --- | ---
Duplicate code | SimHash dedup — 64-bit fingerprints detect copies in O(1) | 15-30%
Boilerplate | Shannon entropy scoring — low-information content scored down | 40-60%
Wrong granularity | 3-level variable resolution — full / signatures / references | 20-40%
Combined | Knapsack-optimal selection | ~78%

## Real Cost Breakdown

For a team of 5 developers, each making ~200 AI requests/day:

| Without Entroly | With Entroly
--- | --- | ---
Tokens/request | 45,000 | 9,900
Daily tokens (team) | 45M | 9.9M
Monthly cost (GPT-4) | $2,700 | $594
Monthly cost (Claude) | $2,025 | $446
Annual savings |  | $18,000 - $25,000

## Setup: Works With Any LLM

### Option A: MCP Server (Cursor, Claude Code, VS Code)

    pip install entroly[full]
    entroly init    # auto-detects your IDE

### Option B: HTTP Proxy (Any LLM API)

    pip install entroly[full]
    entroly proxy

Then point your API calls to `http://localhost:9377/v1` instead of the provider's URL. Entroly intercepts, optimizes context, forwards to the real API.

### Option C: Docker

    docker run --rm -p 9377:9377 -v .:/workspace:ro ghcr.io/juyterman1000/entroly:latest

## Track Your Savings

Entroly tracks every token saved and every dollar reduced:

    entroly dashboard    # live metrics, trend charts
    entroly digest       # weekly summary email

The dashboard shows:

- Lifetime tokens saved — cumulative, survives restarts
- Daily/weekly/monthly cost trends — watch your savings grow
- Per-request breakdown — see exactly where tokens were cut
- Confidence score — how sure Entroly is about its context selection

## Why Not Just Use a Cheaper Model?

You could switch from GPT-4 to GPT-4-mini or from Claude Opus to Haiku. But cheaper models give worse code. Entroly lets you keep the best model while paying less.

Better yet: with 78% fewer tokens, you can often upgrade to a better model for the same price you were paying before.

## Compared to Other Approaches

Approach | Token Reduction | Quality Impact | Setup
--- | --- | --- | ---
Shorter prompts | 10-20% | Worse (less context) | Manual
RAG / retrieval | 30-50% | Mixed (misses deps) | Complex
Cheaper models | 0% (just cheaper) | Worse | Easy
Prompt caching | 20-40% | Same | Medium
Entroly | 78% | Better (more coverage) | 30 seconds

### Stop overpaying for AI tokens.

78% fewer tokens. Better code quality. One install.

    pip install entroly[full] && entroly go

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