Context engineering reduces AI token costs 40–70% by eliminating overhead tokens that add no value for LLMs. Enter your usage details to get a personalized monthly savings estimate for Claude, GPT-4o, Gemini, and more.
Input: $3.00/1M tokens · Output: $15.00/1M tokens
Without Context Engineering
$24.30
per month
With Context Engineering
$23.40
per month
You Save
$0.900
per month · $10.80/year
Ready to see these savings on your actual prompts?
Optimize my prompts free→Politeness markers ("please", "thank you"), indirect phrasing ("I would like you to"), weak intensifiers ("very", "really"), and filler phrases are detected automatically.
These tokens carry zero information for LLMs. "Analyze this code for bugs" is semantically identical to "Please, I would really appreciate it if you could carefully analyze this code for bugs." Same output. 70% fewer tokens.
Advanced context engineering uses XML structure, role framing, and cognitive modes to pack more signal into fewer tokens — improving both cost and output quality simultaneously.
Hi there! I would really appreciate it if you could please help me analyze this Python code very carefully and check for any potential bugs. Thank you so much!
Analyze this Python code for bugs.
I would like you to write a detailed blog post about machine learning. Feel free to include examples if you think they would be helpful. Thanks!
Write a detailed blog post about machine learning with examples.
Could you please review this pull request very carefully? I'm particularly concerned about security issues. Don't hesitate to point out anything you notice.
Review this pull request. Focus on security issues.
The calculator uses published pricing from Anthropic, OpenAI, and Google as of early 2026. Compression rates (10–80%) reflect real-world results from removing politeness tokens, indirect phrasing, filler words, and redundant context — the antipatterns ContextStellar detects automatically.
Not when done correctly. Removing fluff like "please help me" or "I would really appreciate it if you could" has zero effect on LLM behavior. The key principle: preserve semantic content, strip syntactic overhead. ContextStellar only suggests safe, meaning-preserving reductions.
Most real-world prompts contain 30–60% removable overhead. Short chat prompts see the highest compression (50–70%) because politeness and indirect phrasing dominate. Long technical prompts compress 20–40% since more tokens carry necessary context.
Output tokens are controlled by the model, not your prompt length. Compressing input prompts does not reduce output volume. However, cleaner context often produces more focused outputs, which may indirectly reduce output length over time.
Paste any prompt into the free ContextStellar editor and see exactly which tokens are waste — with one-click fixes.