5 LLM Token Optimization Techniques That Actually Work

Every token you send to an LLM costs money. Most developers don't realize that 20-40% of their tokens are wasted - extra whitespace, repeated instructions, verbose phrasing, and stale conversation history.

Here are five proven techniques to cut token usage without affecting response quality.

1. Whitespace Normalization

LLMs tokenize whitespace. Double spaces, extra newlines, trailing spaces, and inconsistent indentation all become billable tokens that add zero value.

Normalizing whitespace typically saves 5-15% of tokens. It's the easiest optimization with zero quality risk.

Before: "Please   help   me    understand\n\n\n\nquantum   computing."
After:  "Please help me understand\nquantum computing."
Saved:  8 tokens (18% reduction)

2. Redundancy Elimination

Developers often repeat instructions across system and user messages. "Be concise" appears in the system prompt, then "please be brief" in the user prompt. The LLM only needs it once.

Deduplicating redundant phrases typically saves 5-15% more tokens on top of whitespace optimization.

3. System Prompt Compression

System prompts tend to be verbose. "You are a helpful, friendly, knowledgeable AI assistant that provides accurate, detailed, and comprehensive responses" can be compressed to "You are a helpful assistant" with the same behavioral outcome.

Automated compression identifies verbose modifiers and compresses them while preserving the behavioral instructions. Savings: 10-30% of system prompt tokens.

4. Context Window Pruning

The most impactful technique for chat applications. A 50-message conversation sends the entire history with every API call, even though only the last few messages are relevant.

Aggressive pruning keeps only the last 6 messages and injects a one-line summary of dropped context. A 4,200-token conversation becomes 980 tokens - 77% reduction.

5. Model-Aware Routing

Not a token optimization per se, but the biggest cost lever. Sending simple requests to expensive models is pure waste. "What is 2+2?" costs $0.000075 on GPT-4o but only $0.00000375 on GPT-4o-mini - 20x cheaper, same answer.

Automatically classifying request complexity and routing to the cheapest capable model typically saves 30-40% of total LLM spend.

Compound Effect

These techniques compound: whitespace (10%) × redundancy (10%) × compression (15%) × pruning (40%) × routing (35%) = 60-70% total reduction.

Promptly applies all five automatically. Point your SDK to Promptly's endpoint and every request is optimized before hitting the LLM provider.