OpenAI cost reduction guide
Reduce OpenAI costs before the API call
The fastest way to reduce OpenAI spend is to stop sending tokens the model does not need. SuperCompress compiles long context around the current question before the API call.
Where your OpenAI costs come from
Input tokens add up quickly in agents and RAG apps. SuperCompress is query-aware extractive selection: it keeps original lines that answer this question and drops the rest. Measure original_tokens / kept_tokens (or tokens_saved) on your traffic, do not treat any single percentage as a product-wide API-cost cut. Published suites live on /benchmarks.
How to measure the cut
Every compress response returns original_tokens, kept_tokens, and tokens_saved. Use those fields on your own OpenAI traffic instead of a marketing savings table. Hosted API + MCP, wired into Claude Code, Cursor, Codex and other agents via npm install -g supercompress-proxy && supercompress setup.
Frequently asked questions
Will compression work with OpenAI streaming?
Yes. Compress first, then stream the model response.
Do I need to change my OpenAI model?
No. Compression is provider-agnostic.